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Resolving conflicts in knowledge for ambient intelligence

Published online by Cambridge University Press:  30 October 2015

Martin Homola ,
Theodore Patkos ,
Giorgos Flouris ,
Ján Šefránek ,
Alexander Šimko ,
Jozef Frtús ,
Dimitra Zografistou  and
Martin Baláž
Martin Homola
Affiliation:
Comenius University in Bratislava, Mlynská dolina, 842 48 Bratislava, Slovakia e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Theodore Patkos
Affiliation:
Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion Crete, Greece e-mail: [email protected], [email protected], [email protected]
Giorgos Flouris
Affiliation:
Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion Crete, Greece e-mail: [email protected], [email protected], [email protected]
Ján Šefránek
Affiliation:
Comenius University in Bratislava, Mlynská dolina, 842 48 Bratislava, Slovakia e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Alexander Šimko
Affiliation:
Comenius University in Bratislava, Mlynská dolina, 842 48 Bratislava, Slovakia e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Jozef Frtús
Affiliation:
Comenius University in Bratislava, Mlynská dolina, 842 48 Bratislava, Slovakia e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Dimitra Zografistou
Affiliation:
Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion Crete, Greece e-mail: [email protected], [email protected], [email protected]
Martin Baláž
Affiliation:
Comenius University in Bratislava, Mlynská dolina, 842 48 Bratislava, Slovakia e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
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Abstract

Ambient intelligence (AmI) proposes pervasive information systems composed of autonomous agents embedded within the environment who, in orchestration, complement human activity in an intelligent manner. As such, it is an interesting and challenging application area for many computer science fields and approaches. A critical issue in such application scenarios is that the agents must be able to acquire, exchange, and evaluate knowledge about the environment, its users, and their activities. Knowledge populated between the agents in such systems may be contextually dependent, ambiguous, and incomplete. Conflicts may thus naturally arise, that need to be dealt with by the agents in an autonomous way. In this survey, we relate AmI to the area of knowledge representation and reasoning (KR), where conflict resolution has been studied for a long time. We take a look at a number of KR approaches that may be applied: context modelling, multi-context systems, belief revision, ontology evolution and debugging, argumentation, preferences, and paraconsistent reasoning. Our main goal is to describe the state of the art in these fields, and to draw attention of researchers to important theoretical issues and practical challenges that still need to be resolved, in order to reuse the results from KR in AmI systems or similar complex and demanding applications.

Type
Articles
Information
The Knowledge Engineering Review , Volume 30 , Issue 5 , November 2015 , pp. 455 - 513
Copyright
© Cambridge University Press, 2015 

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References

Aarts, E., Harwig, R. & Schuurmans, M. 2001. Ambient intelligence. In The Invisible Future: The Seamless Integration of Technology into Everyday Life, Denning P. J. (ed.). McGraw-Hill Companies, 235–250.Google Scholar
A&C 2014. Tutorials on structured argumentation original articles. Argument & Computation, Special Issue 5(1), 2014.Google Scholar
Acciarri, A., Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Palmieri, M. & Rosati, R. 2005. QuOnto: querying ontologies. In Proceedings, The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, 9–13 July, 1670–1671. AAAI Press.Google Scholar
Aceto, G. 2010. Implementation of a Non-Ground Meta-Interpreter for Defeasible Logic. PhD thesis, Università di Bologna.Google Scholar
Adaricheva, K. V., Sloan, R. H., Szörényi, B. & Turán, G. 2011. Horn belief contraction: remainders, envelopes and complexity. In Logical Formalizations of Commonsense Reasoning, Papers from the 2011 AAAI Spring Symposium, 21–23 March. Technical report SS-11-06, AAAI.Google Scholar
Alcântara, J., Damásio, C. V. & Pereira, L. M. 2004. A declarative characterization of disjunctive paraconsistent answer sets. In Proceedings of the 16th European Conference on Artificial Intelligence, ECAI’2004, including Prestigious Applicants of Intelligent Systems, PAIS 2004, 22–27 August, 951–952. IOS Press.Google Scholar
Alchourrón, C. E. & Makinson, D. 1985. On the logic of theory change: safe contraction. Studia Logica 440(4), 405–422.CrossRefGoogle Scholar
Alchourrón, C. E., Gärdenfors, P. & Makinson, D. 1985. On the logic of theory change: partial meet contraction and revision functions. Journal of Symbolic Logic 50(2), 510–530.CrossRefGoogle Scholar
Alferes, J. J., Damásio, C. V. & Pereira, L. M. 1995. A logic programming system for nonmonotonic reasoning. Journal of Automated Reasoning 14(1), 93–147.CrossRefGoogle Scholar
Alferes, J. J., Leite, J. A., Pereira, L. M., Przymusinska, H. & Przymusinski, T. C. 2000. Dynamic updates of non-monotonic knowledge bases. The Journal of Logic Programming 45(1–3), 43–70.CrossRefGoogle Scholar
Alferes, J. J., Damásio, C. V. & Pereira, L. M. 2003. Semantic web logic programming tools. In Principles and Practice of Semantic Web Reasoning, International Workshop, PPSWR 2003, 8 December, Proceedings, LNCS 2901, 16–32. Springer. ISBN 3-540-20582-9.Google Scholar
Amgoud, L. & Prade, H. 2009. Using arguments for making and explaining decisions. Artificial Intelligence 173(3–4), 413–436.CrossRefGoogle Scholar
Amgoud, L. & Vesic, S. 2011. A new approach for preference-based argumentation frameworks. Annals of Mathematics and Artificial Intelligence 63(2), 149–183.CrossRefGoogle Scholar
Amgoud, L. & Vesic, S. 2012. On the use of argumentation for multiple criteria decision making. In Advances in Computational Intelligence—14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, 9–13 July, Proceedings, Part IV, CCIS 300, 480–489. Springer.CrossRefGoogle Scholar
Arenas, M., Bertossi, L. E. & Chomicki, J. 1999. Consistent query answers in inconsistent databases. In Proceedings of the Eighteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 31 May–2 June, 68–79. ACM Press.CrossRefGoogle Scholar
Arieli, O. & Denecker, M. 2003. Reducing preferential paraconsistent reasoning to classical entailment. Journal of Logic and Computation 13(4), 557–580.CrossRefGoogle Scholar
Arieli, O., Denecker, M., Van Nuffelen, B. & Bruynooghe, M. 2004. Database repair by signed formulae. In Foundations of Information and Knowledge Systems, Third International Symposium, FoIKS 2004, 17–20 February, Proceedings, LNCS 2942, 14–30. Springer.CrossRefGoogle Scholar
Artikis, A., Sergot, M. & Paliouras, G. 2010. A logic programming approach to activity recognition. In Proceedings of the 2nd ACM International Workshop on Events in Multimedia, EiMM’10, 3–8.Google Scholar
Asuncion, V. & Zhang, Y. 2009. Translating preferred answer set programs to propositional logic. In Logic Programming and Nonmonotonic Reasoning, 10th International Conference, LPNMR 2009, 14–18 September, Proceedings, LNCS 5753, 396–401. Springer.CrossRefGoogle Scholar
Augusto, J. C., Liu, J., McCullagh, P., Wang, H. & Jian-Bo, Y. 2008. Management of uncertainty and spatio-temporal aspects for monitoring and diagnosis in a smart home. International Journal of Computational Intelligence Systems 1(4), 361–378.Google Scholar
Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D. & Patel-Schneider, P. F. (eds) 2003. The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, ISBN 0-521-78176-0.Google Scholar
Baláž, M., Frtús, J. & Homola, M. 2014a. Conflict resolution in structured argumentation. In LPAR 2013, 19th International Conference on Logic for Programming, Artificial Intelligence and Reasoning, 12–17 December, Short Papers Proceedings, EPiC Series 26, 23–34. EasyChair.Google Scholar
Baláž, M., Frtús, J., Homola, M., Sefránek, J. & Flouris, G. 2014b. Embedding defeasible logic programs into generalized logic programs. In Proceedings of the 28th Workshop on (Constraint) Logic Programming (WLP 2014) Proceedings of the 23rd International Workshop on Functional and (Constraint) Logic Programming, 15–17 September, CEUR Workshop Proceedings 1335, 11–25.Google Scholar
Baláž, M., Frtús, J., Flouris, G., Homola, M. & Sefránek, J. 2015. Conflict resolution in assumption-based frameworks. In Multi-Agent Systems—12th European Conference, EUMAS 2014, 18–19 December, Revised Selected Papers, LNCS 8953, 360–369. Springer.CrossRefGoogle Scholar
Balduccini, M. & Gelfond, M. 2003. Diagnostic reasoning with A-Prolog. Theory and Practice of Logic Programming 3(4), 425–461.CrossRefGoogle Scholar
Bao, J., Voutsadakis, G., Slutzki, G. & Honavar, V. 2009. Package-based description logics. In Modular Ontologies: Concepts, Theories and Techniques for Knowledge Modularization, Stuckenschmidt, H., Parent, C. & Spaccapietra S. (eds), LNCS 5445, 349–371. Springer.CrossRefGoogle Scholar
Baroni, P., Giacomin, M. & Simari, G. R. 2013. Belief revision and argumentation: a reasoning process view. In Trends in Belief Revision and Argumentation Dynamics, Fermé E. L., Gabbay D. M. & Simari G. R. (eds), Logic and Cognitive Systems 48, 1–25. College Publications.Google Scholar
Batsakis, S., Stravoskoufos, K. & Petrakis, E. G. M. 2011. Temporal reasoning for supporting temporal queries in OWL 2.0. In Knowledge-Based and Intelligent Information and Engineering Systems—15th International Conference, KES 2011, 12–14 September, Proceedings, Part I, LNCS 6881, 558–567. Springer.CrossRefGoogle Scholar
Baumann, R. 2012. What does it take to enforce an argument? Minimal change in abstract argumentation. In ECAI 2012—20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track, 27–31 August, FAIA 242, 127–132. IOS Press.Google Scholar
Baumann, R. & Brewka, G. 2010. Expanding argumentation frameworks: enforcing and monotonicity results. In Computational Models of Argument: Proceedings of COMMA 2010, 8–10 September, FAIA 216, 75–86. IOS Press.Google Scholar
Baumann, R. & Brewka, G. 2013. Spectra in abstract argumentation: an analysis of minimal change. In Logic Programming and Nonmonotonic Reasoning, 12th International Conference, LPNMR 2013, 15–19 September, Proceedings, LNCS 8148, 174–186. Springer.CrossRefGoogle Scholar
Bechhofer, S., Horrocks, I., Goble, C. A. & Stevens, R. 2001. OilEd: a reason-able ontology editor for the semantic web. In KI 2001: Advances in Artificial Intelligence, Joint German/Austrian Conference on AI, 19–21 September, Proceedings, LNCS 2174, 396–408. Springer.CrossRefGoogle Scholar
Belnap, N. D. 1977. A useful four-valued logic. In Modern Uses of Multiple-Valued Logic, Dunn, J. M. & Epstein, G. (eds), Episteme 2, 5–37. Springer.CrossRefGoogle Scholar
Ben-Eliyahu, R. & Dechter, R. 1991. Default logic, propositional logic, and constraints. In Proceedings of the 9th National Conference on Artificial Intelligence, 14–19 July, 1, 379–385. AAAI Press.Google Scholar
Benerecetti, M., Bouquet, P. & Ghidini, C. 2000. Contextual reasoning distilled. Journal of Experimental & Theoretical Artificial Intelligence 12(3), 279–305.CrossRefGoogle Scholar
Benerecetti, M., Bouquet, P. & Ghidini, C. 2001. On the dimensions of context dependence: partiality, approximation, and perspective. In Modeling and Using Context, Third International and Interdisciplinary Conference, CONTEXT, 2001, 27–30 July, Proceedings, LNCS 2116, 59–72. Springer.CrossRefGoogle Scholar
Benferhat, S., Kaci, S., Le Berre, D. & Williams, M.-A. 2004. Weakening conflicting information for iterated revision and knowledge integration. Artificial Intelligence 153(1–2), 339–371.CrossRefGoogle Scholar
Bertossi, L. E., Hunter, A. & Schaub, T. 2005. Introduction to inconsistency tolerance. In Inconsistency Tolerance Bertossi L. E., Hunter, A. & Schaub, T. (eds), LNCS 3300, 1–14. Springer.CrossRefGoogle Scholar
Besnard, P. & Hunter, A. 1995. Quasi-classical logic: non-trivializable classical reasoning from inconsistent information. In Symbolic and Quantitative Approaches to Reasoning and Uncertainty, European Conference, ECSQARU’95, 3–5 July, Proceedings, LNCS 946, 44–51. Springer.CrossRefGoogle Scholar
Besnard, P. & Hunter, A. 2001. A logic-based theory of deductive arguments. Artificial Intelligence 128(12), 203–235.CrossRefGoogle Scholar
Besnard, P. & Hunter, A. 2008. Elements of Argumentation. MIT Press.CrossRefGoogle Scholar
Besnard, P. & Schaub, T. 1998. Signed systems for paraconsistent reasoning. Journal of Automated Reasoning 20(1), 191–213.CrossRefGoogle Scholar
Besnard, P., Schaub, T., Tompits, H. & Woltran, S. 2005. Representing paraconsistent reasoning via quantified propositional logic. In Inconsistency Tolerance, Bertossi, L. E., Hunter, A. & Schaub, T. (eds), LNCS 3300, 84–118. Springer.CrossRefGoogle Scholar
Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A. & Riboni, D. 2010. A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing 6(2), 161–180.CrossRefGoogle Scholar
Bikakis, A. & Antoniou, G. 2010a. Defeasible contextual reasoning with arguments in ambient intelligence. IEEE Transactions on Knowledge and Data Engineering 22(11), 1492–1506.CrossRefGoogle Scholar
Bikakis, A. & Antoniou, G. 2010b. Rule-based contextual reasoning in ambient intelligence. In Semantic Web Rules—International Symposium, RuleML 2010, 21–23 October, Proceedings, LNCS 6403, 74–88. Springer.CrossRefGoogle Scholar
Bikakis, A. & Antoniou, G. 2011. Partial preferences and ambiguity resolution in contextual defeasible logic. In Logic Programming and Nonmonotonic Reasoning—11th International Conference, LPNMR 2011, 16–19 May, Proceedings, LNCS 6645, 193–198. Springer.CrossRefGoogle Scholar
Bikakis, A., Patkos, T., Antoniou, G. & Plexousakis, D. 2008. A survey of semantics-based approaches for context reasoning in ambient intelligence. In Constructing Ambient Intelligence—AmI 2007 Workshops, 7–10 November, Revised Papers, CCIS 11, 14–23. Springer.CrossRefGoogle Scholar
Bikakis, A., Antoniou, G. & Hassapis, P. 2011. Strategies for contextual reasoning with conflicts in ambient intelligence. Knowledge and Information Systems 27(1), 45–84.CrossRefGoogle Scholar
Bishop, B., Kiryakov, A., Ognyanoff, D., Peikov, I., Tashev, Z. & Velkov, R. 2011. OWLIM: a family of scalable semantic repositories. Semantic Web 2(1), 33–42.CrossRefGoogle Scholar
Blair, H. A. & Subrahmanian, V. S. 1987. Paraconsistent logic programming. In Foundations of Software Technology and Theoretical Computer Science, Seventh Conference, 17–19 December, Proceedings, LNCS 287, 340–360. Springer.CrossRefGoogle Scholar
Blair, H. A. & Subrahmanian, V. S. 1989. Paraconsistent logic programming. Theoretical Computer Science 68(2), 135–154.CrossRefGoogle Scholar
Boella, G., Kaci, S. & van der Torre, L. 2009a. Dynamics in argumentation with single extensions: abstraction principles and the grounded extension. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 10th European Conference, ECSQARU 2009, 1–3 July, Proceedings, LNCS 5590, 107–118. Springer.CrossRefGoogle Scholar
Boella, G., Kaci, S. & van der Torre, L. W. N. 2009b. Dynamics in argumentation with single extensions: attack refinement and the grounded extension (extended version). In Argumentation in Multi-Agent Systems, 6th International Workshop, ArgMAS 2009, 12 May, Revised Selected and Invited Papers, LNCS 6057, 150–159. Springer.CrossRefGoogle Scholar
Bögl, M., Eiter, T., Fink, M. & Schüller, P. 2010. The MCS-IE system for explaining inconsistency in multi-context systems. In Logics in Artificial Intelligence—12th European Conference, JELIA 2010, 13–15 September, Proceedings, LNCS 6341, 356–359. Springer.CrossRefGoogle Scholar
Bondarenko, A., Dung, P. M., Kowalski, R. A. & Toni, F. 1997. An abstract, argumentation-theoretic approach to default reasoning. Artificial Intelligence 93, 63–101.CrossRefGoogle Scholar
Booth, R., Meyer, T. & Varzinczak, I. J. 2009. Next steps in propositional Horn contraction. In IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, 11–17 July, 702–707.Google Scholar
Booth, R., Meyer, T., Varzinczak, I. & Wassermann, R. 2011. On the link between partial meet, kernel, and infra contraction and its application to Horn logic. Journal of Artificial Intelligence Research 42, 31–53.Google Scholar
Borgida, A. & Serafini, L. 2003. Distributed description logics: assimilating information from peer sources. Journal on Data Semantics 1, 153–184.CrossRefGoogle Scholar
Bosse, T. & Sharpanskykh, A. 2010. A framework for modeling and analysis of ambient agent systems: application to an emergency case. In Ambient Intelligence and Future Trends—International Symposium on Ambient Intelligence (ISAmI 2010), 16–18 June, AISC 72, 121–129. Springer.CrossRefGoogle Scholar
Bouquet, P., Giunchiglia, F., van Harmelen, F., Serafini, L. & Stuckenschmidt, H. 2004. Contextualizing ontologies. Journal of Web Semantics 1(4), 325–343.CrossRefGoogle Scholar
Boutilier, C., Brafman, R. I., Hoos, H. H. & Poole, D. 1999. Reasoning with conditional ceteris paribus preference statements. In UAI ’99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 30 July–1 August, 71–80. Morgan Kaufmann.Google Scholar
Boutilier, C., Brafman, R. I., Domshlak, C., Hoos, H. H. & Poole, D. 2004. CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. Journal of Artificial Intelligence Research 21, 135–191.CrossRefGoogle Scholar
Bozzato, L. & Serafini, L. 2013. Materialization calculus for contexts in the semantic web. In Informal Proceedings of the 26th International Workshop on Description Logics, 23–26 July, CEUR Workshop Proceedings 1014, 552–572.Google Scholar
Bozzato, L., Ghidini, C. & Serafini, L. 2013. Comparing contextual and flat representations of knowledge: a concrete case about football data. In Proceedings of the 7th International Conference on Knowledge Capture, K-CAP 2013, 23–26 June, Benjamins, V. R., d’Aquin, M. & Gordon, A. (eds), ACM, 9–16.Google Scholar
Bozzato, L., Eiter, T. & Serafini, L. 2014. Contextualized knowledge repositories with justifiable exceptions. In Informal Proceedings of the 27th International Workshop on Description Logics, 17–20 July, Bienvenu, M., Ortiz, M., Rosati, R. & Simkus, M. (eds), CEUR Workshop Proceedings 1193, 112–123. CEUR-WS.org.Google Scholar
Braga Silva, T. R. M., Ruiz, L. B. & Loureiro, A. A. F. 2011. Conflicts treatment for ubiquitous collective and context-aware applications. Journal of Applied Computing Research 1(1), 33–47.CrossRefGoogle Scholar
Bratman, M. E. 1987. Intention, Plans, and Practical Reason. Harvard University Press.Google Scholar
Brewka, G. 1996. Well-founded semantics for extended logic programs with dynamic preferences. Journal of Artificial Intelligence Research 4, 19–36.CrossRefGoogle Scholar
Brewka, G. 2002. Logic programming with ordered disjunction. In Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, 28 July–1 August, 100–105. AAAI Press.Google Scholar
Brewka, G. & Eiter, T. 1999. Preferred answer sets for extended logic programs. Artificial Intelligence 109(1–2), 297–356.CrossRefGoogle Scholar
Brewka, G. & Eiter, T. 2007. Equilibria in heterogeneous nonmonotonic multi-context systems. In Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 22–26 July, 385–390. AAAI Press.Google Scholar
Brewka, G. & Eiter, T. 2009. Argumentation context systems: a framework for abstract group argumentation. In Logic Programming and Nonmonotonic Reasoning, 10th International Conference, LPNMR 2009, 14–18 September, Proceedings, LNCS 5753, 44–57. Springer.CrossRefGoogle Scholar
Brewka, G. & Woltran, S. 2010. Abstract dialectical frameworks. In Principles of Knowledge Representation and Reasoning: Proceedings of the Twelfth International Conference, KR 2010, 9–13 May.Google Scholar
Brewka, G., Niemelä, I. & Truszczynski, M. 2003. Answer set optimization. In IJCAI-03, Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, 9–15 August, 867–872. Morgan Kaufmann.Google Scholar
Brewka, G., Roelofsen, F. & Serafini, L. 2007. Contextual default reasoning. In IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, 6–12 January, 268–273.Google Scholar
Brewka, G., Eiter, T., Fink, M. & Weinzierl, A. 2011. Managed multi-context systems. In IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, 16–22 July, 786–791.Google Scholar
Byrne, E. & Hunter, A. 2004. Man bites dog: looking for interesting inconsistencies in structured news reports. Data & Knowledge Engineering 48(3), 265–295.CrossRefGoogle Scholar
Cali, A., Gottlob, G. & Lukasiewicz, T. 2009. Datalog±: a unified approach to ontologies and integrity constraints. In Proceedings of the 12th International Conference on Database Theory (ICDT-09).Google Scholar
Calvanese, D., Kharlamov, E., Nutt, W. & Zheleznyakov, D. 2010. Evolution of DL-Lite knowledge bases. In The Semantic Web—ISWC 2010, 9th International Semantic Web Conference, ISWC 2010, 7–11 November, Revised Selected Papers, Part I, LNCS 6496, 112–128. Springer.CrossRefGoogle Scholar
Caminada, M. & Amgoud, L. 2007. On the evaluation of argumentation formalisms. Artificial Intelligence 171(5–6), 286–310.CrossRefGoogle Scholar
Caminada, M. W. A. & Gabbay, D. M. 2009. A logical account of formal argumentation. Studia Logica 93(2–3), 109–145.CrossRefGoogle Scholar
Caminada, M. W. A., Carnielli, W. A. & Dunne, P. E. 2012. Semi-stable semantics. Journal of Logic and Computation 22(5), 1207–1254.CrossRefGoogle Scholar
Casali, A., Godo, L. & Sierra, C. 2005. Graded BDI models for agent architectures. In Computational Logic in Multi-Agent Systems, 5th International Workshop, CLIMA V, 29–30 September, Revised Selected and Invited Papers, LNCS 3487, 126–143. Springer.CrossRefGoogle Scholar
Cayrol, C. & Lagasquie-Schiex, M.-C. 2009. Bipolar abstract argumentation systems. In Argumentation in Artificial Intelligence, Rahwan, I. & Simari G. R. (eds). Springer, 65–84.CrossRefGoogle Scholar
Cayrol, C., de Saint-Cyr, F. D. & Lagasquie-Schiex, M.-C. 2010. Change in abstract argumentation frameworks: adding an argument. Journal of Artificial Intelligence Research 38, 49–84.CrossRefGoogle Scholar
Celino, I., Contessa, S., Corubolo, M., Dell’Aglio, D., Valle, E. D., Fumeo, S. & Krüger, T. 2012. Linking smart cities datasets with human computation—the case of UrbanMatch. In The Semantic Web—ISWC 2012, 11th International Semantic Web Conference, 11–15 November, Proceedings, Part II, LNCS 7650, 34–49. Springer.CrossRefGoogle Scholar
Chan, M., Estève, D., Escriba, C. & Campo, E. 2008. A review of smart homes-present state and future challenges. Computer Methods and Programs Biomedicine 91(1), 55–81.CrossRefGoogle ScholarPubMed
Chen, L. & Khalil, I. 2011. Activity recognition: approaches, practices and trends. In Activity Recognition in Pervasive Intelligent Environments, Chen, L., Nugent, C., Biswas, J. & Hoey, J. (eds), Atlantis Ambient and Pervasive Intelligence 4, 1–31. Atlantis Press.CrossRefGoogle Scholar
Chomicki, J. & Marcinkowski, J. 2005. On the computational complexity of minimal-change integrity maintenance in relational databases. In Inconsistency Tolerance, Bertossi, L. E., Hunter, A. & Torsten S. (eds), LNCS 3300, 119–150. Springer.CrossRefGoogle Scholar
Chortis, M. & Flouris, G. 2015. A diagnosis and repair framework for ${ DL{\hbox -}Lite}_{\cal A} $ KBs. In Proceedings of the First DIACHRON Workshop on Managing the Evolution and Preservation of the Data Web Co-Located with 12th European Semantic Web Conference (ESWC 2015), 31 May, CEUR Workshop Proceedings 1377, 1–15.Google Scholar
Chou, S.-C. T. & Winslett, M. 1994. A model-based belief revision system. Journal of Automated Reasoning 12(2), 157–208.CrossRefGoogle Scholar
Cimatti, A. & Serafini, L. 1995. Multi-agent reasoning with belief contexts: the approach and a case study. In Intelligent Agents, ECAI-94 Workshop on Agent Theories, Architectures, and Languages, 8–9 August, Proceedings, LNCS 890. Springer.Google Scholar
Cirillo, M., Lanzellotto, F., Pecora, F. & Saffiotti, A. 2009. Monitoring domestic activities with temporal constraints and components. In Intelligent Environments 2009—Proceedings of the 5th International Conference on Intelligent Environments, AISE 2, 117–124. IOS Press.Google Scholar
Cohen, P. R. & Levesque, H. J. 1990. Intention is choice with commitment. Artificial Intelligence 42(2), 213–261.CrossRefGoogle Scholar
Cohn, A. G. & Hazarika, S. M. 2001 Qualitative spatial representation and reasoning: an overview. Fundamenta Informaticae 46(1), 1–29.Google Scholar
Cook, D. J., Augusto, J. C. & Jakkula, V. R. 2009. Ambient intelligence: technologies, applications, and opportunities. Pervasive and Mobile Computing 5(4), 277–298.CrossRefGoogle Scholar
Coste-Marquis, S. & Marquis, P. 2005. On the complexity of paraconsistent inference relations. In Inconsistency Tolerance, Bertossi, L. E., Hunter, A. & Schaub, T. (eds), LNCS 3300, 151–190. Springer.CrossRefGoogle Scholar
Coste-Marquis, S., Konieczny, S., Marquis, P. & Ouali, M. A. 2012. Weighted attacks in argumentation frameworks. In Principles of Knowledge Representation and Reasoning: Proceedings of the Thirteenth International Conference, KR 2012, 10–14 June. AAAI Press.Google Scholar
Coste-Marquis, S., Konieczny, S., Mailly, J.-G. & Marquis, P. 2014. On the revision of argumentation systems: minimal change of arguments statuses. In Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, KR 2014, 20–24 July. AAAI Press.Google Scholar
Creignou, N., Papini, O., Pichler, R. & Woltran, S. 2014. Belief revision within fragments of propositional logic. Journal of Computer and Systems Sciences 80(2), 427–449.CrossRefGoogle Scholar
Cuenca Grau, B, Parsia, B. & Sirin, E. 2004. Working with multiple ontologies on the semantic web. In The Semantic Web—ISWC 2004: Third International Semantic Web Conference, 7–11 November, Proceedings, LNCS 3298, 620–634. Springer.CrossRefGoogle Scholar
Cuenca Grau, B, Kharlamov, E. & Zheleznyakov, D. 2012. Ontology contraction: beyond the propositional paradise. In Proceedings of the 6th Alberto Mendelzon International Workshop on Foundations of Data Management, 27–30 June, CEUR Workshop Proceedings 866, 62–74.Google Scholar
da Costa, N. C. A. & Subrahmanian, V. S. 1989. Paraconsistent logics as a formalism for reasoning about inconsistent knowledge bases. Artificial Intelligence in Medicine 1(4), 167–174.CrossRefGoogle Scholar
Dalal, M. 1988. Investigations into a theory of knowledge base revision: preliminary report. In Proceedings of the 7th National Conference on Artificial Intelligence. 21–26 August, 475–479. AAAI Press.Google Scholar
Daly, E. M., Lécué, F. & Bicer, V. 2013. Westland row why so slow? Fusing social media and linked data sources for understanding real-time traffic conditions. In 18th International Conference on Intelligent User Interfaces, IUI’13, 19–22 March, 203–212. ACM.CrossRefGoogle Scholar
Damásio, C. V. & Pereira, L. M. 1995. A model theory for paraconsistent logic programming. In Progress in Artificial Intelligence, 7th Portuguese Conference on Artificial Intelligence, EPIA’95, 3–6 October, Proceedings, LNCS 990, 377–386. Springer.CrossRefGoogle Scholar
DARC 2012. Workshop on the dynamics of argumentation, rules and conditionals. University of Luxembourg. http://icr.uni.lu/darc/DARC/.Google Scholar
De Giacomo, G., Lenzerini, M., Poggi, A. & Rosati, R. 2007. On the approximation of instance level update and erasure in description logics. In Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 22–26 July, 403–408. AAAI Press.Google Scholar
De Giacomo, G., Lenzerini, M., Poggi, A. & Rosati, R. 2009. On instance-level update and erasure in description logic ontologies. Journal of Logic and Computation 19(5), 745–770.CrossRefGoogle Scholar
Delgrande, J. P. 2008. Horn clause belief change: contraction functions. In Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference, KR 2008, 16–19 September, 156–165. AAAI Press.Google Scholar
Delgrande, J. P. & Wassermann, R. 2010. Horn clause contraction functions: belief set and belief base approaches. In Principles of Knowledge Representation and Reasoning: Proceedings of the Twelfth International Conference, KR 2010, 9–13 May. AAAI Press.Google Scholar
Delgrande, J. P., Schaub, T. & Tompits, H. 2003. A framework for compiling preferences in logic programs. Theory and Practice of Logic Programming 3(2), 129–187.CrossRefGoogle Scholar
Delgrande, J. P., Schaub, T. & Tompits, H. 2004a. Domain-specific preferences for causal reasoning and planning. In Proceedings of the Fourteenth International Conference on Automated Planning and Scheduling (ICAPS 2004), 3–7 June, 63–72. AAAI.Google Scholar
Delgrande, J. P., Schaub, T., Tompits, H. & Wang, K. 2004b. A classification and survey of preference handling approaches in nonmonotonic reasoning. Computational Intelligence 20(2), 308–334.CrossRefGoogle Scholar
Dimopoulos, Y., Nebel, B. & Koehler, J. 1997. Encoding planning problems in nonmonotonic logic programs. In Recent Advances in AI Planning, 4th European Conference on Planning, ECP’97, 24–26 September, Proceedings, LNCS 1348, 169–181. Springer.CrossRefGoogle Scholar
Dix, J., Hansson, S. O., Kern-Isberner, G. & Simari, G. R. 2013. Belief change and argumentation in multi-agent scenarios (dagstuhl seminar 13231). Dagstuhl Reports 3(6), 1–21.Google Scholar
Djedidi, R. & Aufaure, M.-A. 2009. Change management patterns (CMP) for ontology evolution process. In Proceedings of the 3rd International Workshop on Ontology Dynamics, (IWOD 2009), collocated with the 8th International Semantic Web Conference (ISWC-2009), 26 October, CEUR Workshop Proceedings 519.Google Scholar
Djedidi, R. & Aufaure, M.-A. 2010. ONTO-EVO AL an ontology evolution approach guided by pattern modeling and quality evaluation. In Foundations of Information and Knowledge Systems, 6th International Symposium, FoIKS 2010, 15–19 February, Proceedings, LNCS 5956, 286–305. Springer.CrossRefGoogle Scholar
Doherty, P., Lukaszewicz, W. & Szalas, A. 1997. Computing circumscription revisited: a reduction algorithm. Journal of Automated Reasoning 18(3), 297–336.CrossRefGoogle Scholar
Doyle, J. 1979. A truth maintenance system. Artificial Intelligence 12(3), 231–272.CrossRefGoogle Scholar
Dung, P. M. 1995. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77(2), 321–357.CrossRefGoogle Scholar
Dung, P. M., Kowalski, R. A. & Toni, F. 2009. Assumption-based argumentation. In Argumentation in Artificial Intelligence, Rahwan, I. & Simari, G. R. (eds). Springer, 199–218.CrossRefGoogle Scholar
Dunne, P. E. & Wooldridge, M. 2009 Complexity of abstract argumentation. In Argumentation in Artificial Intelligence, Rahwan, I. & Simari, G. R. (eds). Springer, 85–104.CrossRefGoogle Scholar
Dunne, P. E., Hunter, A., McBurney, P., Parsons, S. & Wooldridge, M. 2011. Weighted argument systems: basic definitions, algorithms, and complexity results. Artificial Intelligence 175(2), 457–486.CrossRefGoogle Scholar
Efstathiou, V. & Hunter, A. 2010. Jargue: an implemented argumentation system for classical propositional logic. In COMMA 2010 Third International Conference on Computational Models of Argument, 8–10 September. Demo paper. http://www.ing.unibs.it/ comma2010/demos/Efstathiou_etal.pdf.Google Scholar
Eiter, T., Faber, W., Leone, N. & Pfeifer, G. 2003a. Computing preferred answer sets by meta-interpretation in answer set programming. Theory and Practice of Logic Programming 3(4–5), 463–498.CrossRefGoogle Scholar
Eiter, T., Faber, W., Leone, N., Pfeifer, G. & Polleres, A. 2003b. A logic programming approach to knowledge-state planning, II: the ${\rm DLV}^{\cal K} $ system. Artificial Intelligence 144(1–2), 157–211.CrossRefGoogle Scholar
Eiter, T., Ianni, G., Schindlauer, R. & Tompits, H. 2006. dlvhex: a system for integrating multiple semantics in an answer-set programming framework. In 20th Workshop on Logic Programming, 22–24 February, INFSYS Research Report 1843-06-02, 206–210. Technische Universität Wien.Google Scholar
Eiter, T., Fink, M. & Moura, J. 2010a. Paracoherent answer set programming. In Principles of Knowledge Representation and Reasoning: Proceedings of the Twelfth International Conference, KR 2010, 9–13 May. AAAI Press.Google Scholar
Eiter, T., Fink, M., Schüller, P. & Weinzierl, A. 2010b. Finding explanations of inconsistency in multi-context systems. In Principles of Knowledge Representation and Reasoning: Proceedings of the Twelfth International Conference, KR 2010, 9–13 May. AAAI Press.Google Scholar
Emaldi, M., Lazaro, J., Laiseca, X. & Lopez-de Ipina, D. 2012. LinkedQR: improving tourism experience through linked data and QR codes. In Ubiquitous Computing and Ambient Intelligence, LNCS 7656, 371–378. Springer.CrossRefGoogle Scholar
Erdmann, M. & Waterfeld, W. 2012. Overview of the NeOn toolkit. In Ontology Engineering in a Networked World, Suárez-Figueroa, M. D. C., Gómez-Pérez, A., Motta, E. & Gangemi, A. (eds). Springer, 281–301.CrossRefGoogle Scholar
Ernst, N. A., Borgida, A., Mylopoulos, J. & Jureta, I. 2012. Agile requirements evolution via paraconsistent reasoning. In Advanced Information Systems Engineering—24th International Conference, CAiSE 2012, 25–29 June, Proceedings, LNCS 7328, 382–397. Springer.Google Scholar
Falappa, M. A., Kern-Isberner, G. & Simari, G. R. 2009. Belief revision and argumentation theory. In Argumentation in Artificial Intelligence, Rahwan, I. & Simari, G. R. (eds). Springer, 341–360.CrossRefGoogle Scholar
Falappa, M. A., Garca, A. J., Kern-Isberner, G. & Simari, G. R. 2011. On the evolving relation between belief revision and argumentation. The Knowledge Engineering Review 26(1), 35–43.CrossRefGoogle Scholar
Feldmann, R., Monien, B. & Schamberger, S. 2000. A distributed algorithm to evaluate quantified Boolean formulae. In Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, 30 July–3 August, 285–290. AAAI Press.Google Scholar
Fermé, E. L. & Hansson, S. O. 2011. AGM 25 years: twenty-five years of research in belief change. Journal of Philosophical Logic 40(2), 295–331.Google Scholar
Fermé, E. L., Gabbay, D. M. & Simari, G. R. (eds) 2013. Trends in belief revision and argumentation, Proceedings of the 2012 Workshop on Belief Revision and Argumentation, Logic and Cognitive Systems 48. College Publications.Google Scholar
Fitting, M. 1991a. Kleene’s logic, generalized. Journal of Logic and Computation 1(6), 797–810.CrossRefGoogle Scholar
Fitting, M. 1991b. Bilattices and the semantics of logic programming. Journal of Logic Programming 11(1 and 2), 91–116.CrossRefGoogle Scholar
Flouris, G. 2006a. On belief change in ontology evolution. AI Communications Journal (AI-Com) 19 (4), 395–397 (PhD thesis summary).Google Scholar
Flouris, G. 2006b. On Belief Change and Ontology Evolution. PhD thesis, University of Crete.Google Scholar
Flouris, G. & Plexousakis, D. 2006. Bridging ontology evolution and belief change. In Advances in Artificial Intelligence, 4th Helenic Conference on AI, SETN 2006, 18–20 May, Proceedings, LNCS 3955, 486–489. Springer.CrossRefGoogle Scholar
Flouris, G., Plexousakis, D. & Antoniou, G. 2004. Generalizing the AGM postulates: preliminary results and applications. In 10th International Workshop on Non-Monotonic Reasoning (NMR 2004), 6–8 June, Proceedings, 171–179.Google Scholar
Flouris, G., Plexousakis, D. & Antoniou, G. 2005. On applying the AGM theory to DLs and OWL. In The Semantic Web—ISWC 2005, 4th International Semantic Web Conference, ISWC 2005, 6–10 November, Proceedings, LNCS 3729, 216–231. Springer.CrossRefGoogle Scholar
Flouris, G., Huang, Z., Pan, J. Z., Plexousakis, D. & Wache, H. 2006a. Inconsistencies, negations and changes in ontologies. In Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, 16–20 July, 1295–1300. AAAI Press.Google Scholar
Flouris, G., Plexousakis, D. & Antoniou, G. 2006b. On generalizing the AGM postulates. In STAIRS 2006—Proceedings of the Third Starting AI Researchers’ Symposium, FAIA 142, 132–143. IOS Press.Google Scholar
Flouris, G., Manakanatas, D., Kondylakis, H., Plexousakis, D. & Antoniou, G. 2008. Ontology change: classification and survey. The Knowledge Engineering Review 26(2), 117–152.Google Scholar
Flouris, G., Roussakis, Y., Poveda-Villalon, M., Mendes, P. N. & Fundulaki, I. 2012. Using provenance for quality assessment and repair in linked open data. In Proceedings of the 2nd Joint Workshop on Knowledge Evolution and Ontology Dynamics, in conjunction with the 11th International Semantic Web Conference (ISWC 2012), 12 November, CEUR Workshop Proceedings 890.Google Scholar
Flouris, G., Konstantinidis, G., Antoniou, G. & Christophides, V. 2013. Formal foundations for RDF/S KB evolution. Knowledge and Information Systems 35(1), 153–191.CrossRefGoogle Scholar
Fuchs, F., Hochstatter, I., Krause, M. & Berger, M. 2005. A metamodel approach to context information. In 3rd IEEE Conference on Pervasive Computing and Communications Workshops (PerCom 2005 Workshops), 8–12 March, 8–14. IEEE Computer Society.Google Scholar
Fuhrmann, A. 1991. Theory contraction through base contraction. Journal of Philosophical Logic 20(2), 175–203.CrossRefGoogle Scholar
Gabbay, D. M., Kurucz, A., Wolter, F. & Zakharyaschev, M. 2003. Many-Dimensional Modal Logics: Theory and Applications, Studies in Logic and the Foundations of Mathematics 148. Elsevier.Google Scholar
Gabel, T., Sure, Y. & Voelker, J. 2004. KAON – ontology management infrastructure. SEKT informal deliverable D3.1.1.a. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1.5228 rep=rep1 type=pdf.Google Scholar
García, A. J. & Simari, G. R. 2004. Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming 4(1–2), 95–138.CrossRefGoogle Scholar
Gärdenfors, P. 1990. The dynamics of belief systems: foundations versus coherence theories. Revue Internationale de Philosophie 44, 24–46.Google Scholar
Gärdenfors, P. & Makinson, D. 1988. Revisions of knowledge systems using epistemic entrenchment. In Proceedings of the 2nd Conference on Theoretical Aspects of Reasoning About Knowledge, March, 83–95. Morgan Kaufmann.Google Scholar
Gelfond, M. & Lifschitz, V. 1988. The stable model semantics for logic programming. In Logic Programming, Proceedings of the Fifth International Conference and Symposium, 15–19 August, 1070–1080. MIT Press.Google Scholar
Gelfond, M. & Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9(3/4), 365–386.CrossRefGoogle Scholar
Ghidini, C. & Giunchiglia, F. 2001. Local models semantics, or contextual reasoning=locality+compatibility. Artificial Intelligence 127(2), 221–259.CrossRefGoogle Scholar
Ghidini, C. & Serafini, L. 1998. Distributed first order logics. In Frontiers of Combining Systems, FroCoS’98, Second International Workshop, 121–140. Research Studies Press.Google Scholar
Ghidini, C. & Serafini, L. 2008. Mapping properties of heterogeneous ontologies. In Artificial Intelligence: Methodology, Systems, and Applications, 13th International Conference, AIMSA 2008, 4–6 September, Proceedings, LNCS 5253, 181–193. Springer.CrossRefGoogle Scholar
Ghidini, C., Serafini, L. & Tessaris, S. 2008. Bridging heterogeneous representations of binary relations: first results. In Proceedings of the 21st International Workshop on Description Logics (DL2008), 13–16 May, CEUR Workshop Proceedings 353.Google Scholar
Ginsberg, M. L. 1988. Multivalued logics: a uniform approach to reasoning in artificial intelligence. Computational Intelligence 4(3), 265–316.CrossRefGoogle Scholar
Ginsberg, M. L. & Smith, D. E. 1988. Reasoning about action i: a possible worlds approach. Artificial Intelligence 35(2), 165–195.CrossRefGoogle Scholar
Giunchiglia, E., Narizzano, M. & Tacchella, A. 2001. QuBE: a system for deciding quantified Boolean formulas satisfiability. In Automated Reasoning, First International Joint Conference, IJCAR 2001, 18–23 June, Proceedings, LNCS 2083, 364–369. Springer.CrossRefGoogle Scholar
Giunchiglia, F. 1993. Contextual reasoning. Epistemologia, Special Issue on I Linguaggi e le Macchine 16, 345–364.Google Scholar
Giunchiglia, F. & Ghidini, C. 1998. Local models semantics, or contextual reasoning = locality + compatibility. In Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR’98), 2–5 June, 282–291. Morgan Kaufmann.Google Scholar
Giunchiglia, F. & Serafini, L. 1994. Multilanguage hierarchical logics or: how we can do without modal logics. Artificial Intelligence 65(1), 29–70.CrossRefGoogle Scholar
Gonçalves, R., Knorr, M. & Leite, J. 2014a. Evolving bridge rules in evolving multi-context systems. In Computational Logic in Multi-Agent Systems—15th International Workshop, CLIMA XV, 18–19 August, Proceedings, LNCS 8624, 52–69. Springer.CrossRefGoogle Scholar
Gonçalves, R., Knorr, M. & Leite, J. 2014b. Evolving multi-context systems. In ECAI 2014—21st European Conference on Artificial Intelligence, 18–22 August, Including Prestigious Applications of Intelligent Systems (PAIS 2014), FAIA 263, 375–380. IOS Press.Google Scholar
Gottlob, G. 1992. Complexity results for nonmonotonic logics. Journal of Logic and Computation 2(3), 397–425.CrossRefGoogle Scholar
Governatori, G., Maher, M. J., Antoniou, G. & Billington, D. 2004. Argumentation semantics for defeasible logic. Journal of Logic and Computation 14(5), 675–702.Google Scholar
Grell, S., Konczak, K. & Schaub, T. 2005 nomore<: a system for computing preferred answer sets. In Logic Programming and Nonmonotonic Reasoning, 8th International Conference, LPNMR 2005, 5–8 September, Proceedings, LNCS 3662, 394–398. Springer.CrossRefGoogle Scholar
Grieco, L., Lembo, D., Rosati, R. & Ruzzi, M. 2005. Consistent query answering under key and exclusion dependencies: algorithms and experiments. In Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management, 31 October–5 November, 792–799. ACM.CrossRefGoogle Scholar
Grove, A. 1988. Two modellings for theory change. Journal of Philosophical Logic 17(2), 157–170.CrossRefGoogle Scholar
Gu, T., Pung, H. K. & Zhang, D. Q. 2005. A service-oriented middleware for building context-aware services. Journal of Network and Computer Applications 28(1), 1–18.CrossRefGoogle Scholar
Gustafsson, J. 1996. An Implementation and Optimization of an Algorithm for Reducing Formulas in Second-Order Logic. Technical report, Department of Mathematics, Linkoping University.Google Scholar
Gutierrez, C., Hurtado, C. A. & Vaisman, A. A. 2006. The meaning of erasing in RDF under the Katsuno-Mendelzon approach. In Ninth International Workshop on the Web and Databases, WebDB 2006, 30 June.Google Scholar
Gutierrez, C., Hurtado, C. A. & Vaisman, A. 2007. Introducing time into RDF. IEEE Transactions on Knowledge and Data Engineering 19(2), 207–218.Google Scholar
Haase, P., van Harmelen, F., Huang, Z., Stuckenschmidt, H. & Sure, Y. 2005. A framework for handling inconsistency in changing ontologies. In The Semantic Web—ISWC 2005, 4th International Semantic Web Conference, ISWC 2005, 6–10 November, Proceedings, LNCS 3729, 353–367. Springer.Google Scholar
Halaschek-Wiener, C. & Katz, Y. 2006. Belief base revision for expressive description logics. In Proceedings of the OWLED*06 Workshop on OWL: Experiences and Directions, 10–11 November, CEUR Workshop Proceedings 216.Google Scholar
Hansson, S. O. 1991. Belief contraction without recovery. Studia Logica 50(2), 251–260.CrossRefGoogle Scholar
Hansson, S. O. 1994. Kernel contraction. Journal of Symbolic Logic 59(3), 845–859.CrossRefGoogle Scholar
Hansson, S. O. 1996. Knowledge-level analysis of belief base operations. Artificial Intelligence 82(1–2), 215–235.CrossRefGoogle Scholar
Hansson, S. O. 1997. What’s new isn’t always best. Theoria: Special Issue on Non-Prioritized Belief Revision 63(1–2), 1–13.Google Scholar
Hansson, S. O., Fermé, E. L., Cantwell, J. & Falappa, M. A. 2001. Credibility limited revision. Journal of Symbolic Logic 66(4), 1581–1596.CrossRefGoogle Scholar
Helaoui, R., Niepert, M. & Stuckenschmidt, H. 2011. Recognizing interleaved and concurrent activities using qualitative and quantitative temporal relationships. Pervasive and Mobile Computing 7(6), 660–670.CrossRefGoogle Scholar
Helaoui, R., Riboni, D., Niepert, M., Bettini, C. & Stuckenschmidt, H. 2012. Towards activity recognition using probabilistic description logics. In Activity Context Representation: Techniques and Languages, Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI technical report WS-12-05, AAAI.Google Scholar
Henricksen, K. & Indulska, J. 2004. Modelling and using imperfect context information. In 2nd IEEE Conference on Pervasive Computing and Communications Workshops (PerCom 2004 Workshops), 14–17 March, 33–37. IEEE Computer Society.Google Scholar
Homola, M. 2010. Semantic Investigations in Distributed Ontologies. PhD thesis, Comenius University in Bratislava.Google Scholar
Homola, M. & Patkos, T. 2015. Different types of conflicting knowledge in AmI environments. In Knowledge Engineering and Knowledge Management—EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, 24–28 November. Revised Selected Papers, LNCS 8982, 52–63. Springer.CrossRefGoogle Scholar
Homola, M. & Serafini, L. 2010. Augmenting subsumption propagation in distributed description logics. Applied Artificial Intelligence 24(1–2), 39–76.CrossRefGoogle Scholar
Hong, X., Nugent, C., Mulvenna, M., McClean, S., Scotney, B. & Devlin, S. 2009. Evidential fusion of sensor data for activity recognition in smart homes. Pervasive and Mobile Computing 5(3), 236–252.CrossRefGoogle Scholar
Horn, A. 1951. On sentences which are true of direct unions of algebras. Journal of Symbolic Logic 16(1), 14–21.CrossRefGoogle Scholar
Horrocks, I., Sattler, U. & Tobies, S. 2000. Practical reasoning for very expressive description logics. Logic Journal of the IGPL 8(3), 239–263.CrossRefGoogle Scholar
Horrocks, I., Patel-Schneider, P. F., Bechhofer, S. & Tsarkov, D. 2005. OWL rules: a proposal and prototype implementation. Journal of Web Semantics 3(1), 23–40.CrossRefGoogle Scholar
Horrocks, I., Kutz, O. & Sattler, U. 2006. The even more irresistible SROIQ. In Proceedings, Tenth International Conference on Principles of Knowledge Representation and Reasoning, Lake District of the United Kingdom, 2–5 June, 57–67. AAAI Press.Google Scholar
Hunter, A. 2000a. Merging potentially inconsistent items of structured text. Data & Knowledge Engineering 34(3), 305–332.CrossRefGoogle Scholar
Hunter, A. 2000b. Reasoning with contradictory information using quasi-classical logic. Journal of Logic and Computation 10(5), 677–703.CrossRefGoogle Scholar
Hunter, A. & Konieczny, S. 2005. Approaches to measuring inconsistent information. In Inconsistency Tolerance, Bertossi L., Hunter A. & Schaub T. (eds), LNCS 3300, 191–236. Springer.CrossRefGoogle Scholar
Hunter, A. & Nuseibeh, B. 1998. Managing inconsistent specifications: reasoning, analysis, and action. ACM Transactions on Software Engineering and Methodology 7(4), 335–367.CrossRefGoogle Scholar
Information Society Technologies Advisory Group 2003. Ambient intelligence: from vision to reality. Report, ISTAG. ftp://ftp.cordis.europa.eu/pub/ist/docs/istag-ist2003_consolidated_report.pdf.Google Scholar
Inoue, K., Koshimura, M. & Hasegawa, R. 1992. Embedding negation as failure into a model generation theorem prover. In Automated Deduction—CADE-11, 11th International Conference on Automated Deduction, 15–18 June, Proceedings, LNCS 607, 400–415. Springer.CrossRefGoogle Scholar
Jakkula, V. R., Crandall, A. S. & Cook, D. J. 2009. Enhancing anomaly detection using temporal pattern discovery. In Advanced Intelligent Environments, Kameas A. D., Callagan V., Hagras H., Weber M. & Minker W. (eds). Springer, 175–194.CrossRefGoogle Scholar
Jennings, N. R., Sycara, K. P. & Wooldridge, M. 1998 A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems 1(1), 7–38.CrossRefGoogle Scholar
Ji, Q., Haase, P., Qi, G., Hitzler, P. & Stadtmüller, S. 2009. RaDON—repair and diagnosis in ontology networks. In The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009, 31 May–4 June, Proceedings, LNCS 5554, 863–867. Springer.CrossRefGoogle Scholar
Jin, Y., Wang, K. & Wen, L. 2012. Possibilistic reasoning in multi-context systems: preliminary report. In PRICAI 2012: Trends in Artificial Intelligence—12th Pacific Rim International Conference on Artificial Intelligence, 3–7 September, Proceedings, LNCS 7458, 180–193. Springer.CrossRefGoogle Scholar
Joseph, M. & Serafini, L. 2011. Simple reasoning for contextualized RDF knowledge. In Modular Ontologies—Proceedings of the Fifth International Workshop, WoMO 2011, August, FAIA 230, 79–93. IOS Press.Google Scholar
Junker, U. & Konolige, K. 1990. Computing the extensions of autoepistemic and default logics with a truth maintenance system. In Proceedings of the 8th National Conference on Artificial Intelligence, 29 July–3 August, 278–283. AAAI Press.Google Scholar
Kalyanpur, A., Parsia, B., Sirin, E. & Cuenca Grau, B. 2006 Repairing unsatisfiable concepts in OWL ontologies. In The Semantic Web: Research and Applications, 3rd European Semantic Web Conference, ESWC 2006, 11–14 June, Proceedings, LNCS 4011, 170–184. Springer.CrossRefGoogle Scholar
Kaminski, T., Knorr, M. & Leite, J. 2015. Efficient paraconsistent reasoning with ontologies and rules. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, 25–31 July, 3098–3105. AAAI Press.Google Scholar
Katsuno, H. & Mendelzon, A. O. 1991. On the difference between updating a knowledge base and revising it. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (KR’91). 22–25 April, 387–394. Morgan Kaufmann.Google Scholar
Kifer, M. & Lozinskii, E. L. 1992. A logic for reasoning with inconsistency. Journal of Automated Reasoning 9(2), 179–215.CrossRefGoogle Scholar
Klarman, S. & Gutiérrez-Basulto, V. Description logics of context. Journal of Logic and Computation, to appear. doi: 10.1093/logcom/ext011.CrossRefGoogle Scholar
Knorr, M., Slota, M., Leite, J. & Homola, M. 2014. What if no hybrid reasoner is available? Hybrid MKNF in multi-context systems. Journal of Logic and Computation 24(6), 1279–1311.CrossRefGoogle Scholar
Knox, S., Coyle, L. & Dobson, S. 2010. Using ontologies in case-based activity recognition. In Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, 19–21 May. AAAI Press.Google Scholar
Konstantinidis, G., Flouris, G., Antoniou, G. & Christophides, V. 2008a. A formal approach for RDF/S ontology evolution. In ECAI 2008—18th European Conference on Artificial Intelligence, 21–25 July, Proceedings, FAIA 178, 70–74. IOS Press.Google Scholar
Konstantinidis, G., Flouris, G., Antoniou, G. & Christophides, V. 2008b. On RDF/S ontology evolution. In Semantic Web, Ontologies and Databases, VLDB Workshop, SWDB-ODBIS 2007, 24 September, Revised Selected Papers, LNCS 5005, 21–42. Springer.CrossRefGoogle Scholar
Köster, M., Novák, P., Mainzer, D. & Fuhrmann, B. 2009. Two case studies for Jazzyk BSM. In Agents for Games and Simulations, Trends in Techniques, Concepts and Design [AGS 2009, The First International Workshop on Agents for Games and Simulations, 11 May], LNCS 5920, 33–47. Springer.CrossRefGoogle Scholar
Krümpelmann, P., Thimm, M., Falappa, M. A., Garca, A. J., Kern-Isberner, G. & Simari, G. R. 2012. Selective revision by deductive argumentation. In Theory and Applications of Formal Argumentation—First International Workshop, TAFA 2011, 16–17 July, Revised Selected Papers, LNCS 7132, 147–162. Springer.CrossRefGoogle Scholar
Kuipers, B. 1984. Commonsense reasoning about causality: deriving behavior from structure. Artificial Intelligence 24(1), 169–203.Google Scholar
Kutz, O., Wolter, F. & Zakharyaschev, M. 2002. Connecting abstract description systems. In Proceedings of the Eights International Conference on Principles and Knowledge Representation and Reasoning (KR-02), 22–25 April, 215–226. Morgan Kaufmann.Google Scholar
Kutz, O., Lutz, C., Wolter, F. & Zakharyaschev, M. 2003. ${\cal E} $ -connections of description logics. In Proceedings of the 2003 International Workshop on Description Logics (DL2003), 5–7 September, CEUR Workshop Proceedings, 81.Google Scholar
Lam, J. S. C., Sleeman, D. H., Pan, J. Z. & Vasconcelos, W. W. 2008 A fine-grained approach to resolving unsatisfiable ontologies. Journal on Data Semantics 10, 62–95.Google Scholar
Lam, S. C., Sleeman, D. & Vasconselos, W. 2005a. ReTax++: a tool for browsing and revising ontologies. In ISWC 2005, 4th International Semantic Web Conference, 6–10 November, Posters Track. http://homepages.abdn.ac.uk/d.sleeman/pages/published-papers/publications-2005/p155-ISWC-poster-joey.pdf.Google Scholar
Lam, S. C. J., Sleeman, D. & Vasconcelos, W. 2005b. Retax+: a cooperative taxonomy revision tool. In Applications and Innovations in Intelligent Systems XII Proceedings of AI-2004, the Twenty-Fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, 64–77. Springer.CrossRefGoogle Scholar
Langlois, M., Sloan, R. H., Szörényi, B. & Turán, G. 2008. Horn complements: towards Horn-to-Horn belief revision. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, 13–17 July, 466–471. AAAI Press.Google Scholar
Lausen, G., Meier, M. & Schmidt, M. 2008. SPARQLing constraints for RDF. In EDBT 2008, 11th International Conference on Extending Database Technology, 25–29 March, Proceedings, ACM ICPS 261, 499–509. ACM.CrossRefGoogle Scholar
Lécué, F., Schumann, A. & Sbodio, M. L. 2012. Applying semantic web technologies for diagnosing road traffic congestions. In The Semantic Web—ISWC 2012, 11th International Semantic Web Conference, 11–15 November, Proceedings, Part II, LNCS 7650, 114–130. Springer.CrossRefGoogle Scholar
Lee, K. & Meyer, T. 2004. A classification of ontology modification. In AI 2004: Advances in Artificial Intelligence, 17th Australian Joint Conference on Artificial Intelligence, 4–6 December, Proceedings, LNCS 3339, 248–258. Springer.CrossRefGoogle Scholar
Lehmann, J. & Bühmann, L. 2010. ORE—a tool for repairing and enriching knowledge bases. In The Semantic Web—ISWC 2010, 9th International Semantic Web Conference, ISWC 2010, 7–11 November, Revised Selected Papers, Part II, LNCS 6497, 177–193. Springer.CrossRefGoogle Scholar
Leite, J. & Martins, J. 2011. Social abstract argumentation. In IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, 16–22 July, Walsh, T. (ed.), 2287–2292. IJCAI/AAAI.Google Scholar
Leite, J. A. 2002. Evolving Knowledge Bases, FAIA 81. IOS Press.Google Scholar
Leite, J. A. & Pereira, L. M. 1998. Generalizing updates: from models to programs. In Logic Programming and Knowledge Representation, Third International Workshop, LPKR’97, 17 October, Selected Papers, LNCS 1471, 224–246. Springer.Google Scholar
Lembo, D., Lenzerini, M., Rosati, R., Ruzzi, M. & Savo, D. F. 2010. Inconsistency-tolerant semantics for description logics. In Web Reasoning and Rule Systems—Fourth International Conference, RR 2010, 22–24 September, Proceedings, LNCS 6333, 103–117. Springer.CrossRefGoogle Scholar
Lembo, D., Lenzerini, M., Rosati, R., Ruzzi, M. & Savo, D. F. 2011. Query rewriting for inconsistent DL-Lite ontologies. In Web Reasoning and Rule Systems—5th International Conference, RR 2011, 29–30 August, Proceedings, LNCS 6902, 155–169. Springer.CrossRefGoogle Scholar
Lenat, D. 1998. The Dimensions of Context-Space. Technical report, CYCorp. http://www.cyc.com/doc/context-space.pdf.Google Scholar
Lester, J., Choudhury, T., Kern, N., Borriello, G. & Hannaford, B. 2005. A hybrid discriminative/generative approach for modeling human activities. In Proceedings of the 19th International Joint Conference on Artificial Intelligence, Kaelbling, L. P. & Saffiotti, A. (eds), 766–772. Professional Book Center.Google Scholar
Letz, R. 2002. Lemma and model caching in decision procedures for quantified Boolean formulas. In Automated Reasoning with Analytic Tableaux and Related Methods, International Conference, TABLEAUX 2002, 30 July–1 August, Proceedings, LNCS 2381, 160–175. Springer.CrossRefGoogle Scholar
Liao, B. 2013. Layered argumentation frameworks with subargument relation and their dynamics. In Trends in Belief Revision and Argumentation Dynamics, Fermé E. L., Gabbay D. M. & Simari G. R. (eds), Logic and Cognitive Systems 48, 247–267. College Publications.Google Scholar
Liao, B., Jin, L. & Koons, R. C. 2011. Dynamics of argumentation systems: a division-based method. Artificial Intelligence 175(11), 1790–1814.CrossRefGoogle Scholar
Lifschitz, V. 1999. Action languages, answer sets, and planning. In The Logic Programming Paradigm: A 25 Years Perspective, Apt K. R., Marek V. W., Truszczynski M. & Warren D. S. (eds). Springer, 357–373.CrossRefGoogle Scholar
Liu, H., Lutz, C., Miličić, M. & Wolter, F. 2006. Updating description logic ABoxes. In Tenth International Conference on Principles of Knowledge Representation and Reasoning Proceedings, 2–5 June, 46–56. AAAI Press.Google Scholar
Lloyd, J. W. 1984 Foundations of Logic Programming. Springer.CrossRefGoogle Scholar
Loke, S. W. 2004. Representing and reasoning with situations for context-aware pervasive computing: a logic programming perspective. The Knowledge Engineering Review 19(3), 213–233.CrossRefGoogle Scholar
Lu, C.-H. & Fu, L.-C. 2009. Robust location-aware activity recognition using wireless sensor network in an attentive home. IEEE Transactions on Automation Science and Engineering 6(4), 598–609.Google Scholar
Lukasiewicz, T., Martinez, M. V. & Simari, G. I. 2013. Preference-based query answering in datalog+/- ontologies. In IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, 3–9 August. AAAI.Google Scholar
Ma, Y. & Hitzler, P. 2009. Paraconsistent reasoning for OWL 2. In Web Reasoning and Rule Systems, Third International Conference, RR 2009, 25–26 October, Proceedings, LNCS 5837, 197–211. Springer.CrossRefGoogle Scholar
Ma, Y., Hitzler, P. & Lin, Z. 2007. Algorithms for paraconsistent reasoning with OWL. In The Semantic Web: Research and Applications, 4th European Semantic Web Conference, ESWC 2007, 3–7 June, Proceedings, LNCS 4519, 399–413. Springer.CrossRefGoogle Scholar
Magiridou, M., Sahtouris, S., Christophides, V. & Koubarakis, M. 2005. RUL: a declarative update language for RDF. In The Semantic Web—ISWC 2005, 4th International Semantic Web Conference, ISWC 2005, 6–10 November, Proceedings, LNCS 3729, 506–521. Springer.CrossRefGoogle Scholar
Mailly, J.-G. 2013. Dynamic of argumentation frameworks. In IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, 3–9 August. AAAI.Google Scholar
Makinson, D. 1987. On the status of the postulate of recovery in the logic of theory change. Journal of Philosophical Logic 16(4), 383–394.CrossRefGoogle Scholar
Martinez, M. V., Ariel, C., Deagustini, D., Falappa, M. A. & Simari, G. R. 2014. Inconsistency-tolerant reasoning in datalog± ontologies via an argumentative semantics. In Advances in Artificial Intelligence—IBERAMIA 2014, 14th Ibero-American Conference on AI, 24–27 November, Proceedings, LNCS 8864, 15–27. Springer.CrossRefGoogle Scholar
Masotti, G., Rosati, R. & Ruzzi, M. 2011. Practical ABox cleaning in Dl-Lite (progress report). In Proceedings of the 24th International Workshop on Description Logics (DL 2011), 13–16 July, CEUR Workshop Proceedings, 745.Google Scholar
Mastrogiovanni, F., Scalmato, A., Sgorbissa, A. & Zaccaria, R. 2011. Smart environments and activity recognition: a logic-based approach. In Activity Recognition in Pervasive Intelligent Environments, Chen L., Nugent C., Biswas J. & Hoey J. (eds), Atlantis Ambient and Pervasive Intelligence 4, 83–109. Atlantis Press.CrossRefGoogle Scholar
McCarthy, J. 1980. Circumscription—a form of non-monotonic reasoning. Artificial Intelligence 13(1–2), 27–39.CrossRefGoogle Scholar
McCarthy, J. 1993. Notes on formalizing context. In Proceedings of the 13th International Joint Conference on Artificial Intelligence. 28 August–3 September, 555–562. Morgan Kaufmann.Google Scholar
McKeever, S., Ye, J., Coyle, L., Bleakley, C. & Dobson, S. 2010. Activity recognition using temporal evidence theory. Journal of Ambient Intelligence and Smart Environments 2(3), 253–269.CrossRefGoogle Scholar
Meilicke, C., Stuckenschmidt, H. & Tamilin, A. 2007. Repairing ontology mappings. In Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 22–26 July, 1408–1413. AAAI Press.Google Scholar
Meilicke, C., Stuckenschmidt, H. & Tamilin, A. 2009. Reasoning support for mapping revision. Journal of Logic and Computation 19(5), 807–829.CrossRefGoogle Scholar
Meyer, T., Lee, K. & Booth, R. 2005. Knowledge integration for description logics. In Proceedings of the Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, 9–13 July, 645–650. AAAI Press.Google Scholar
Meyer, T., Lee, K., Booth, R. & Pan, J. Z. 2006. Finding maximally satisfiable terminologies for the description logic ${\cal A}{\cal L}{\cal C}$ . In Proceedings of the Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference , 16–20 July, 269–274. AAAI Press.Google Scholar
Mileo, A., Merico, D. & Bisiani, R. 2008a. Wireless sensor networks supporting context-aware reasoning in assisted living. In Proceedings of the 1st ACM International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2008, 16–18 July, ACM ICPS 282, 54. ACM.CrossRefGoogle Scholar
Mileo, A., Merico, D. & Bisiani, R. 2008b. A logic programming approach to home monitoring for risk prevention in assisted living. In Logic Programming, 24th International Conference, ICLP 2008, 9–13 December, Proceedings, LNCS 5366, 145–159. Springer.CrossRefGoogle Scholar
Moawad, A., Bikakis, A., Caire, P., Nain, G. & Le Traon, Y. 2013. A rule-based contextual reasoning platform for ambient intelligence environments. In Theory, Practice, and Applications of Rules on the Web—7th International Symposium, RuleML 2013, 11–13 July, Proceedings, LNCS 8035, 158–172. Springer.CrossRefGoogle Scholar
Modgil, S. & Caminada, M. 2009. Proof theories and algorithms for abstract argumentation frameworks. In Argumentation in Artificial Intelligence, Rahwan I. & Simari G. R. (eds). Springer, 105–129.CrossRefGoogle Scholar
Moguillansky, M. O., Rotstein, N. D. & Falappa, M. A. 2008. A theoretical model to handle ontology debugging & change through argumentation. In Proceedings of the 2nd International Workshop on Ontology Dynamics (IWOD 2008), Collocated with the 7th International Semantic Web Conference (ISWC 2008), 26–30 October. http://users.ics.forth.gr/ fgeo/files/IWOD08Proc.pdf.Google Scholar
Moguillansky, M. O., Rotstein, N. D., Falappa, M. A., Garca, A. J. & Simari, G. R. 2013. Dynamics of knowledge in DeLP through argument theory change. Theory and Practice of Logic Programming 13(6), 893–957.CrossRefGoogle Scholar
Moraitis, P. & Spanoudakis, N. 2007. Argumentation-based agent interaction in an ambient-intelligence context. IEEE Intelligent Systems 22(6), 84–93.CrossRefGoogle Scholar
Motik, B., Horrocks, I. & Sattler, U. 2009. Bridging the gap between OWL and relational databases. Journal of Web Semantics 7(2), 74–89.CrossRefGoogle Scholar
Muñoz, A., Botía, J. A. & Augusto, J. C. 2010. Intelligent decision-making for a smart home environment with multiple occupants. In Computational Intelligence in Complex Decision Systems, Ruan D. (ed.), Atlantis Computational Intelligence Systems 2, 325–371. Atlantis Press.CrossRefGoogle Scholar
Muñoz, A., Augusto, J. C., Villa, A. & Botía, J. A. 2011. Design and evaluation of an ambient assisted living system based on an argumentative multi-agent system. Personal Ubiquitous Computing 15(4), 377–387.CrossRefGoogle Scholar
Muñoz Ortega, A. M., Blaya, J. A. B., Clemente, F. J. G., Pérez, G. M. & Skarmeta, A. F. G. 2010. Solving conflicts in agent-based ubiquitous computing systems: a proposal based on argumentation. In Agent-Based Ubiquitous Computing, Mangina E., Carbo J. & Molina J. M. (eds), Atlantis Ambient and Pervasive Intelligence 1, 1–12. Atlantis Press.Google Scholar
Mueller, E. T. 2014 Commonsense Reasoning: An Event Calculus Based Approach, 2nd edition. Morgan Kaufmann.Google Scholar
Niemelä, I. 1995. Towards efficient default reasoning. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 95, 20–25 August, 2 vols, 312–318. Morgan Kaufmann.Google Scholar
Nogueira, M., Balduccini, M., Gelfond, M., Watson, R. & Barry, M. 2001. An A-Prolog decision support system for the space shuttle. In Practical Aspects of Declarative Languages, Third International Symposium, PADL 2001, 11–12 March, Proceedings, LNCS 1990, 169–183. Springer.CrossRefGoogle Scholar
Noy, N. F., Fergerson, R. W. & Musen, M. A. 2000. The knowledge model of Protégé-2000: combining interoperability and flexibility. In Knowledge Acquisition, Modeling and Management, 12th International Conference, EKAW 2000, 2–6 October, Proceedings, LNCS 1937, 17–32. Springer.CrossRefGoogle Scholar
Noy, N. F., Chugh, A., Liu, W. & Musen, M. A. 2006. A framework for ontology evolution in collaborative environments. In The Semantic Web—ISWC 2006, 5th International Semantic Web Conference, ISWC 2006, 5–9 November, Proceedings, LNCS 4273, 544–558. Springer.CrossRefGoogle Scholar
Odintsov, S. P. & Pearce, D. 2005. Routley semantics for answer sets. In Logic Programming and Nonmonotonic Reasoning, 8th International Conference, LPNMR 2005, 5–8 September, Proceedings, LNCS 3662, 343–355. Springer.CrossRefGoogle Scholar
Ohlbach, H. J. 1996. SCAN—elimination of predicate quantifiers. In Automated Deduction—CADE-13, 13th International Conference on Automated Deduction, 30 July–3 August, Proceedings, LNCS 1104, 161–165. Springer.CrossRefGoogle Scholar
Ossowski, S. (ed.) 2013. Agreement Technologies. Springer.CrossRefGoogle Scholar
OWL Working Group (ed.) 2009. OWL 2 Web Ontology Language Document Overview. W3C Recommendation.Google Scholar
Pajares Ferrando, S. P. & Onaindia, E. 2012. Defeasible argumentation for multi-agent planning in ambient intelligence applications. In International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012, 4–8 June, 509–516. IFAAMAS.Google Scholar
Parsons, S., Sierra, C. & Jennings, N. R. 1998. Agents that reason and negotiate by arguing. Journal of Logic and Computation 8(3), 261–292.CrossRefGoogle Scholar
Patel-Schneider, P. F. 1989. A four-valued semantics for terminological logics. Artificial Intelligence 38(3), 319–351.CrossRefGoogle Scholar
Patkos, T., Chrysakis, I., Bikakis, A., Plexousakis, D. & Antoniou, G. 2010. A reasoning framework for ambient intelligence. In Artificial Intelligence: Theories, Models and Applications, 6th Hellenic Conference on AI, SETN 2010, 4–7 May, Proceedings, LNCS 6040, 213–222. Springer.CrossRefGoogle Scholar
Pecora, F., Cirillo, M., Dell’Osa, F., Ullberg, J. & Saffiotti, A. 2012. A constraint-based approach for proactive, context-aware human support. Journal of Ambient Intelligence and Smart Environments 4(4), 347–367.CrossRefGoogle Scholar
Plessers, P. & De Troyer, O. 2006. Resolving inconsistencies in evolving ontologies. In The Semantic Web: Research and Applications, 3rd European Semantic Web Conference, ESWC 2006, 11–14 June, Proceedings, LNCS 4011, 200–214. Springer.CrossRefGoogle Scholar
Pollock, J. L. 1995. Cognitive Carpentry: A Blueprint for How to Build a Person. MIT Press.CrossRefGoogle Scholar
Prakken, H. 2010. An abstract framework for argumentation with structured arguments. Argument & Computation 1(2), 93–124.CrossRefGoogle Scholar
Prakken, H. & Sartor, G. 1997. Argument-based logic programming with defeasible priorities. Journal of Applied Non-Classical Logics 7(1), 25–75.CrossRefGoogle Scholar
Prakken, H. & Vreeswijk, G. 2002. Logics for defeasible argumentation. In Handbook of Philosophical Logic, Gabbay D. M. & Franz G. (eds), 4. Kluwer Academic Publishers, 219–318.Google Scholar
Preuveneers, D., den Bergh, J. V., Wagelaar, D., Georges, A., Rigole, P., Clerckx, T., Berbers, Y., Coninx, K., Jonckers, V. & De Bosschere, K. 2004. Towards an extensible context ontology for ambient intelligence. In Ambient Intelligence: Second European Symposium, EUSAI 2004, 8–11 November, Proceedings, LNCS 3295, 148–159. Springer.CrossRefGoogle Scholar
Qi, G. & Pan, J. Z. 2007. A stratification-based approach for inconsistency handling in description logics. In Proceedings of the International Workshop on Ontology Dynamics (IWOD-07), Held as part of the 4th European Semantic Web Conference (ESWC-07), 7 June, 83–96. http://users.ics.forth.gr/ fgeo/files/IWOD07Proc.pdf.Google Scholar
Qi, G. & Du, J. 2009. Model-based revision operators for terminologies in description logics. In IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, 11–17 July, 891–897.Google Scholar
Qi, G., Liu, W. & Bell, D. A. 2006a. A revision-based approach to handling inconsistency in description logics. Artificial Intelligence Review 26(1–2), 115–128.CrossRefGoogle Scholar
Qi, G., Liu, W. & Bell, D. A. 2006b. Knowledge base revision in description logics. In Logics in Artificial Intelligence, 10th European Conference, JELIA 2006, 13–15 September, Proceedings, LNCS 4160, 386–398. Springer.CrossRefGoogle Scholar
Qi, G., Wang, Z., Wang, K., Fu, X. & Zhuang, Z. 2015. Approximating model-based ABox revision in Dl-Lite: theory and practice. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25–30 January, 254–260. AAAI Press.CrossRefGoogle Scholar
Rahwan, I. & Simari, G. R. (eds) 2009. Argumentation in Artificial Intelligence. Springer.Google Scholar
Rao, A. S. & Georgeff, M. P. 1991. Modeling rational agents within a BDI-architecture. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (KR'91). 22–25 April, 473–484. Morgan Kaufmann.Google Scholar
Rao, A. S. & Georgeff, M. P. 1995. BDI agents: From theory to practice. In Proceedings of the First International Conference on Multiagent Systems, June 12–14, 1995, San Francisco, California, USA, 312–319. The MIT Press.Google Scholar
Reiter, R. 1980. A logic for default reasoning. Artificial Intelligence 13(1–2), 81–132.CrossRefGoogle Scholar
Resendes, S., Carreira, P. & Santos, A. C. 2014. Conflict detection and resolution in home and building automation systems: a literature review. Journal of Ambient Intelligence and Humanized Computing 5(5), 699–715.CrossRefGoogle Scholar
Riazanov, A. & Voronkov, A. 2002. The design and implementation of VAMPIRE. AI Communications 15(2–3), 91–110.Google Scholar
Ribeiro, M. M. & Wassermann, R. 2007. Base revision in description logics—preliminary results. In Proceedings of the International Workshop on Ontology Dynamics (IWOD-07), Held as part of the 4th European Semantic Web Conference (ESWC-07), 7 June, 69–82. http://users.ics.forth.gr/ fgeo/files/IWOD07Proc.pdf.Google Scholar
Ribeiro, M. M., Wassermann, R., Flouris, G. & Antoniou, G. 2013. Minimal change: relevance and recovery revisited. Artificial Intelligence 201, 59–80.CrossRefGoogle Scholar
Riboni, D. & Bettini, C. 2011a. COSAR: hybrid reasoning for context-aware activity recognition. Personal and Ubiquitous Computing 15(3), 271–289.CrossRefGoogle Scholar
Riboni, D. & Bettini, C. 2011b. OWL 2 modeling and reasoning with complex human activities. Pervasive and Mobile Computing 7(3), 379–395.CrossRefGoogle Scholar
Riboni, D., Pareschi, L., Radaelli, L. & Bettini, C. 2011. Is ontology-based activity recognition really effective? In Ninth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom, 21–25 March, Workshop Proceedings, 427–431. IEEE.CrossRefGoogle Scholar
Rieß, C., Heino, N., Tramp, S. & Auer, S. 2010. EvoPat—pattern-based evolution and refactoring of RDF knowledge bases. In The Semantic Web—ISWC 2010, 9th International Semantic Web Conference, ISWC 2010, 7–11 November, Revised Selected Papers, Part I, LNCS 6496, 647–662. Springer.CrossRefGoogle Scholar
Rock, A. 2009. Deimos: Query Answering Defeasible Logic System. Technical report, Griffith University, School of Computing and Information Technology. http://www.ict.griffith.edu.au/arock/defeasible/doc/Deimos-long.pdf, http://www.ict.griffith.edu.au/arock/defeasible/Defeasible.cgi.Google Scholar
Rodrigues, O. & Russo, A. 1998. A translation method for Belnap logic. Research Report Doc 98/7, Imperial College Longon. http://www.doc.ic.ac.uk/research/technicalreports/1998/DTR98-7.pdf.Google Scholar
Rodríguez, A. 2005. Inconsistency issues in spatial databases. In Inconsistency Tolerance, Bertossi L. E., Hunter A. & Schaub T. (eds), LNCS 3300, 237–269. Springer.CrossRefGoogle Scholar
Roelofsen, F. & Serafini, L. 2005. Minimal and absent information in contexts. In IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, 30 July–5 August, 558–563. Professional Book Center.Google Scholar
Roger, M., Simonet, A. & Simonet, M. 2002. Toward updates in description logics. In Proceedings of the 9th International Workshop on Knowledge Representation Meets Databases (KRDB 2002), 21 April, CEUR Workshop Proceedings, 54.Google Scholar
Rotstein, N. D., Moguillansky, M. O., Garca, A. J. & Simari, G. R. 2010. A dynamic argumentation framework. In Computational Models of Argument: Proceedings of COMMA 2010, 8–10 September, FAIA 216, 427–438. IOS Press.Google Scholar
Rott, H. 1992. Preferential belief change using generalized epistemic entrenchment. Journal of Logic, Language and Information 1(1), 45–78.CrossRefGoogle Scholar
Roussakis, Y., Flouris, G. & Christophides, V. 2011. Declarative repairing policies for curated KBs. In Proceedings of the 10th Hellenic Data Management Symposium (HDMS-11).Google Scholar
Roy, P. C., Giroux, S., Bouchard, B., Bouzouane, A., Phua, C., Tolstikov, A. & Biswas, J. 2011 A possibilistic approach for activity recognition in smart homes for cognitive assistance to Alzheimer’s patients. In Activity Recognition in Pervasive Intelligent Environments, Chen, L., Nugent, C. D., Biswas, J. & Hoey, J. (eds), Atlantis Ambient and Pervasive Intelligence 4, 33–58. Atlantis Press.CrossRefGoogle Scholar
Rubel, P., Fayn, J., Simon-Chautemps, L., Atoui, H., Ohlsson, M., Telisson, D., Adami, S., Arod, S., Forlini, M. C., Malossi, C., Placide, J., Ziliani, G. L., Assanelli, D. & Chevalier, P. 2004 New paradigms in telemedicine: ambient intelligence, wearable, pervasive and personalized. In Wearable eHealth Systems for Personalised Health Management, Lymberis, A. & de Rossi, D. (eds), Studies in Health Technology and Informatics 108, 123–132. IOS Press.Google Scholar
Rugnone, A., Vicario, E., Nugent, C. D., Donnelly, M. P., Craig, D., Paggetti, C. & Tamburini, E. 2007 HomeTL: a visual formalism, based on temporal logic, for the design of home based care. In IEEE Conference on Automation Science and Engineering, CASE 2007, 22–25 September, 747–752. IEEE.CrossRefGoogle Scholar
Sabater, J., Sierra, C., Parsons, S. & Jennings, N. R. 2002 Engineering executable agents using multi-context systems. Journal of Logic and Computation 12(3), 413–442.CrossRefGoogle Scholar
Sadri, F. 2010. Intention recognition with event calculus graphs. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and International Conference on Intelligent Agent Technology—Workshops, 31 August–3 September, 386–391. IEEE Computer Society.CrossRefGoogle Scholar
Sadri, F. 2011. Ambient intelligence: a survey. ACM Computing Surveys 43(4), 36:1–36:66.CrossRefGoogle Scholar
Sakama, C. 1992. Extended well-founded semantics for paraconsistent logic programs. In Fifth Generation Computer Systems’92: Proceedings of the International Conference on Fifth Generation Computer Systems. 1–5 June, 592–599. IOS Press.Google Scholar
Sakama, C. & Inoue, K. 1995. Paraconsistent stable semantics for extended disjunctive programs. Journal of Logic and Computation 5(3), 265–285.CrossRefGoogle Scholar
Sakama, C. & Inoue, K. 2000. Prioritized logic programming and its application to commonsense reasoning. Artificial Intelligence 123(1–2), 185–222.Google Scholar
Schaub, T. 2011. Collection on answer set programming (ASP) and more. http://www.cs.uni-potsdam.de/torsten/asp/.Google Scholar
Schaub, T. & Wang, K. 2002. Preferred well-founded semantics for logic programming by alternating fixpoints: preliminary report. In 9th International Workshop on Non-Monotonic Reasoning (NMR 2002), 19–21 April, Proceedings, 238–246.Google Scholar
Schaub, T. & Wang, K. 2003. A semantic framework for preference handling in answer set programming. Theory and Practice of Logic Programming 3(4–5), 569–607.CrossRefGoogle Scholar
Schlobach, S. & Cornet, R. 2003. Non-standard reasoning services for the debugging of description logic terminologies. In IJCAI-03, Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, 9–15 August, 355–362. Morgan Kaufmann.Google Scholar
Sebbak, F., Chibani, A., Amirat, Y., Benhammadi, F. & Mokhtari, A. 2012. An evidential fusion approach for activity recognition under uncertainty in ambient intelligence environments. In The 2012 ACM Conference on Ubiquitous Computing, Ubicomp’12, 5–8 September, 834–840. ACM.CrossRefGoogle Scholar
Šefránek, J. 2008. Preferred answer sets supported by arguments. In Proceedings of the Twelfth International Workshop on Non-Monotonic Reasoning, UNSW-CSE-TR 819, 232–240. The University of New South Wales, School of Computer Science and Engineering.Google Scholar
Šefránek, J. & Šimko, A. 2011 Warranted derivations of preferred answer sets. In 19th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2011) and 25th Workshop on Logic Programming (WLP 2011), 28–30 September, Proceedings, INFSYS Research Report 1842-11-06, 195–207. Technische Universität Wien.Google Scholar
Šefránek, J. & Šimko, A. 2013. A descriptive approach to preferred answer sets. In Applications of Declarative Programming and Knowledge Management—19th International Conference, INAP 2011, and 25th Workshop on Logic Programming, WLP 2011, 28–30 September, Revised Selected Papers, LNCS 7773, 195–214. Springer.CrossRefGoogle Scholar
Serafini, L. & Homola, M. 2012. Contextualized knowledge repositories for the semantic web. Journal of Web Semantics, Special Issue: Reasoning with Context in the Semantic Web 12, 64–87.CrossRefGoogle Scholar
Serafini, L. & Tamilin, A. 2004. Local tableaux for reasoning in distributed description logics. In Proceedings of the 2004 International Workshop on Description Logics (DL2004), 6–8 June, CEUR Workshop Proceedings 104.Google Scholar
Serafini, L. & Tamilin, A. 2005. DRAGO: distributed reasoning architecture for the semantic web. In The Semantic Web: Research and Applications, Second European Semantic Web Conference, ESWC 2005, 29 May–1 June, Proceedings, LNCS 3532, 361–376. Springer.CrossRefGoogle Scholar
Serafini, L., Borgida, A. & Tamilin, A. 2005. Aspects of distributed and modular ontology reasoning. In IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, 30 July–5 August, 570–575.Google Scholar
Serfiotis, G., Koffina, I., Christophides, V. & Tannen, V. 2005. Containment and minimization of RDF/S query patterns. In The Semantic Web—ISWC 2005, 4th International Semantic Web Conference, ISWC 2005, 6–10 November, Proceedings, LNCS 3729, 607–623. Springer.CrossRefGoogle Scholar
Shakarian, P., Simari, G. I. & Falappa, M. A. 2014. Belief revision in structured probabilistic argumentation. In Foundations of Information and Knowledge Systems—8th International Symposium, FoIKS 2014, 3–7 March, Proceedings, LNCS 8367, 324–343. Springer.CrossRefGoogle Scholar
Šimko, A. 2013. Extension of Gelfond-Lifschitz reduction for preferred answer sets: preliminary report. In Kiel Declarative Programming Days 2013: 20th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2013), 22nd International Workshop on Functional and (Constraint) Logic Programming (WFLP 2013), 27th Workshop on Logic Programming (WLP 2013), Technical Report/Bericht 1306, 2–16. Institut für Informatik der Christian-Albrechts-Universität zu Kiel.Google Scholar
Singla, G., Cook, D. J. & Schmitter-Edgecombe, M. 2010. Recognizing independent and joint activities among multiple residents in smart environments. Journal of Ambient Intelligence and Humanized Computing 1(1), 57–63.CrossRefGoogle ScholarPubMed
Sirin, E., Parsia, B., Cuenca Grau, B, Kalyanpur, A. & Katz, Y. 2007. Pellet: a practical OWL-DL reasoner. Journal of Web Semantics 5(2), 51–53.CrossRefGoogle Scholar
Skarlatidis, A., Paliouras, G., Vouros, G. A. & Artikis, A. 2011. Probabilistic event calculus based on Markov logic networks. In Rule-Based Modeling and Computing on the Semantic Web, 5th International Symposium, RuleML 2011-America, 3–5 November, Proceedings, LNCS 7018, 155–170. Springer.CrossRefGoogle Scholar
Snaith, M. & Reed, C. 2012 TOAST: online ASPIC+ implementation. In Computational Models of Argument, Proceedings of COMMA 2012, 10–12 September, FAIA 245, 509–510. IOS Press.Google Scholar
Son, T. C. & Lobo, J. 2001. Reasoning about policies using logic programs. In Answer Set Programming, Towards Efficient and Scalable Knowledge Representation and Reasoning, Proceedings of the 1st International ASP’01 Workshop, 26–28 March. AAAI.Google Scholar
Son, T. C., Pontelli, E. & Sakama, C. 2009. Logic programming for multiagent planning with negotiation. In Logic Programming, 25th International Conference, ICLP 2009, 14–17 July, Proceedings, LNCS 5649, 99–114. Springer.CrossRefGoogle Scholar
Sowa, J. F. 2000. Knowledge representation: logical, philosophical, and computational foundations. Brooks/Cole Publishing.Google Scholar
Springer, T. & Turhan, A.-Y. 2009. Employing description logics in ambient intelligence for modeling and reasoning about complex situations. Journal of Ambient Intelligence and Smart Environments 1(3), 235–259.CrossRefGoogle Scholar
Staab, S. & Studer, R. (eds) 2004. Handbook on Ontologies. Springer.CrossRefGoogle Scholar
Stefanidis, K., Koutrika, G. & Pitoura, E. 2011 A survey on representation, composition and application of preferences in database systems. ACM Transactions on Database Systems 36(3), 19.CrossRefGoogle Scholar
Stojanovic, L., Maedche, A., Motik, B. & Stojanovic, N. 2002. User-driven ontology evolution management. In Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web, 13th International Conference, EKAW 2002, 1–4 October, Proceedings, LNCS 2473, 285–300. Springer.CrossRefGoogle Scholar
Straccia, U. 1997. A sequent calculus for reasoning in four-valued description logics. In Automated Reasoning with Analytic Tableaux and Related Methods, International Conference, TABLEAUX ‘97, 13–16 May, Proceedings, LNCS 1227, 343–357. Springer.CrossRefGoogle Scholar
Strang, T. & Linnhoff-Popien, C. 2004. A context modeling survey. In Workshop on Advanced Context Modelling, Reasoning and Management, UbiComp 2004—The Sixth International Conference on Ubiquitous Computing, 7 September.Google Scholar
Sure, Y., Angele, J. & Staab, S. 2003. OntoEdit: multifaceted inferencing for ontology engineering. Journal on Data Semantics 1(1), 128–152.CrossRefGoogle Scholar
Tan, J. G., Zhang, D., Wang, X. & Cheng, H. S. 2005 Enhancing semantic spaces with event-driven context interpretation. In Proceedings of the Third International Conference on Pervasive Computing, Gellersen, H.-W., Want, R. & Schmidt, A. (eds), 80–97.Google Scholar
Tao, J., Sirin, E., Bao, J. & McGuinness, D. L. 2010. Extending OWL with integrity constraints. In Proceedings of the 23rd International Workshop on Description Logics (DL 2010), 4–7 May, CEUR Workshop Proceedings 573.Google Scholar
Van Gelder, A., Ross, K. A. & Schlipf, J. S. 1988. Unfounded sets and well-founded semantics for general logic programs. In Proceedings of the Seventh ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 21–23 March, 221–230. ACM.CrossRefGoogle Scholar
Van Gelder, A., Ross, K. A. & Schlipf, J. S. 1991. The well-founded semantics for general logic programs. Journal of the ACM 38(3), 620–650.CrossRefGoogle Scholar
Wang, H., Horridge, M., Rector, A. L., Drummond, N. & Seidenberg, J. 2005. Debugging OWL-DL ontologies: a heuristic approach. In The Semantic Web—ISWC 2005, 4th International Semantic Web Conference, ISWC 2005, 6–10 November, Proceedings, LNCS 3729, 745–757. Springer.CrossRefGoogle Scholar
Wang, K., Zhou, L. & Lin, F. 2000. Alternating fixpoint theory for logic programs with priority. In Computational Logic—CL 2000, First International Conference, July 24–28, Proceedings, LNCS 1861, 164–178. Springer.CrossRefGoogle Scholar
Wang, X., Dong, J. S., Chin, C.-Y., Hettiarachchi, S. R. & Zhang, D. 2004. Semantic space: an infrastructure for smart spaces. IEEE Pervasive Computing 3(3), 32–39.CrossRefGoogle Scholar
Wang, Z., Wang, K. & Topor, R. 2010. A new approach to knowledge base revision in Dl-Lite. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, 11–15 July. AAAI Press.CrossRefGoogle Scholar
Weiser, M. 1991. The computer for the 21st century. Scientific American 265(3), 94–104.CrossRefGoogle Scholar
Wijsen, J. 2009. Consistent query answering under primary keys: a characterization of tractable queries. In Database Theory—ICDT 2009, 12th International Conference, 23–25 March, Proceedings, ACM ICPS 361, 42–52. ACM.CrossRefGoogle Scholar
Wooldridge, M. & Jennings, N. R. 1995. Intelligent agents: theory and practice. The Knowledge Engineering Review 10(2), 115–152.CrossRefGoogle Scholar
Wu, J., Osuntogun, A., Choudhury, T., Philipose, M. & Rehg, J. M. 2007 A scalable approach to activity recognition based on object use. In IEEE 11th International Conference on Computer Vision, ICCV 2007, 14–20 October, 1–8. IEEE.CrossRefGoogle Scholar
Yang, Q. 2009. Activity recognition: linking low-level sensors to high-level intelligence. In IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, 11–17 July, 20–25.Google Scholar
Ye, J., Coyle, L., Dobson, S. & Nixon, P. 2007. Ontology-based models in pervasive computing systems. The Knowledge Engineering Review 22(4), 315–347.CrossRefGoogle Scholar
Ye, J., Dobson, S. & McKeever, S. 2012 Situation identification techniques in pervasive computing: a review. Pervasive and Mobile Computing 8(1), 36–66.CrossRefGoogle Scholar
Zablith, F., Antoniou, G., d’Aquin, M., Flouris, G., Kondylakis, H., Motta, E., Plexousakis, D. & Sabou, M. 2015. Ontology evolution: a process-centric survey. The Knowledge Engineering Review 30(1), 45–75.CrossRefGoogle Scholar
Zelkha, E. 1998. The future of information appliances and consumer devices. Palo Alto Ventures (unpublished document).Google Scholar
Zhang, X. & Lin, Z. 2012. Quasi-classical description logic. Multiple-Valued Logic and Soft Computing 18(3–4), 291–327.Google Scholar
Zhang, X., Xiao, G. & Lin, Z. 2009. A tableau algorithm for handling inconsistency in OWL. In The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009, 31 May–4 June, Proceedings, LNCS 5554, 399–413. Springer.CrossRefGoogle Scholar
Zhang, Y. & Foo, N. Y. 1997. Answer sets for prioritized logic programs. In Logic Programming, Proceedings of the 1997 International Symposium, 13–16 October, 69–83. MIT Press.Google Scholar
Zhuang, Z. Q. & Pagnucco, M. 2010. Horn contraction via epistemic entrenchment. In Logics in Artificial Intelligence—12th European Conference, JELIA 2010, 13–15 September, Proceedings, LNCS 6341, 339–351. Springer.CrossRefGoogle Scholar
Zhuang, Z. Q. & Pagnucco, M. 2012. Model based Horn contraction. In Principles of Knowledge Representation and Reasoning: Proceedings of the Thirteenth International Conference, KR 2012, 10–14 June. AAAI Press.Google Scholar