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An event calculus production rule system for reasoning in dynamic and uncertain domains

Published online by Cambridge University Press:  07 March 2016

THEODORE PATKOS ,
DIMITRIS PLEXOUSAKIS ,
ABDELGHANI CHIBANI  and
YACINE AMIRAT
THEODORE PATKOS
Affiliation:
Information Systems Laboratory, Institute of Computer Science, FO.R.T.H., Heraklion, Crete, Greece (e-mail: [email protected], [email protected])
DIMITRIS PLEXOUSAKIS
Affiliation:
Information Systems Laboratory, Institute of Computer Science, FO.R.T.H., Heraklion, Crete, Greece (e-mail: [email protected], [email protected])
ABDELGHANI CHIBANI
Affiliation:
Lissi Laboratory, University of Paris-Est Créteil (UPEC), Vitry-sur-Seine, France (e-mail: [email protected], [email protected])
YACINE AMIRAT
Affiliation:
Lissi Laboratory, University of Paris-Est Créteil (UPEC), Vitry-sur-Seine, France (e-mail: [email protected], [email protected])
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Abstract

Action languages have emerged as an important field of knowledge representation for reasoning about change and causality in dynamic domains. This paper presents Cerbere, a production system designed to perform online causal, temporal and epistemic reasoning based on the Event Calculus. The framework implements the declarative semantics of the underlying logic theories in a forward-chaining rule-based reasoning system, coupling the high expressiveness of its formalisms with the efficiency of rule-based systems. To illustrate its applicability, we present both the modeling of benchmark problems in the field, as well as its utilization in the challenging domain of smart spaces. A hybrid framework that combines logic-based with probabilistic reasoning has been developed, that aims to accommodate activity recognition and monitoring tasks in smart spaces.

Keywords

Event calculusRule-based reasoningSmart environments
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 16 , Special Issue 3: Special Issue on the International Web Rule Symposia 2012-2014 , May 2016 , pp. 325 - 352
Copyright
Copyright © Cambridge University Press 2016 

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References

Aggarwal, J. and Ryoo, M. 2011. Human activity analysis: A review. ACM Compututing Surveys 43, 3 (Apr.), 16:1–16:43.Google Scholar
Anicic, D., Fodor, P., Rudolph, S., Stühmer, R., Stojanovic, N. and Studer, R. 2010. A rule-based language for complex event processing and reasoning. In Web Reasoning and Rule Systems, vol. 6333, Hitzler, P. and Lukasiewicz, T., Eds. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 42–57.Google Scholar
Artikis, A., Sergot, M. and Paliouras, G. 2010. A logic programming approach to activity recognition. In Proc. of the 2Nd ACM International Workshop on Events in Multimedia. EiMM '10, ACM, New York, NY, USA, 3–8.Google Scholar
Artikis, A., Sergot, M. and Paliouras, G. 2012. Run-time composite event recognition. In Proc. of the 6th ACM International Conference on Distributed Event-Based Systems. DEBS '12. ACM, New York, NY, USA, 69–80.Google Scholar
Bikakis, A. and Antoniou, G. 2010. Defeasible contextual reasoning with arguments in ambient intelligence. Knowledge and Data Engineering, IEEE Transactions 22, 11 (Nov.), 1492–1506.Google Scholar
Brachman, R. and Levesque, H. 2004. Knowledge Representation and Reasoning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.Google Scholar
Bragaglia, S., Chesani, F., Mello, P. and Sottara, D. 2012. A rule-based calculus and processing of complex events. In Proc. of the 6th International Conference on Rules on the Web: Research and Applications. RuleML'12, Springer-Verlag, Berlin, Heidelberg, 151–166.Google Scholar
Chen, L. and Khalil, I. 2011. Activity recognition: Approaches, practices and trends. In Activity Recognition in Pervasive Intelligent Environments, Vol. 4, Chen, L., Nugent, C. D., Biswas, J. and Hoey, J., Eds. Atlantis Ambient and Pervasive Intelligence, Atlantis Press, 1–31.Google Scholar
Chesani, F., Mello, P., Montali, M. and Torroni, P. 2010. A logic-based, reactive calculus of events. Fundamenta Informaticae 105, 135–161.Google Scholar
Ferraris, P., Lee, J. and Lifschitz, V. 2011. Stable models and circumscription. Artificial Intelligence 175, 1, 236–263.CrossRefGoogle Scholar
Forgy, C. L. 1990. Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem. IEEE Computer Society Press, Los Alamitos, CA, USA, Chapter Expert Systems, 324–341.Google Scholar
Helaoui, R., Riboni, D., Niepert, M., Bettini, C. and Stuckenschmidt, H. 2012. Towards activity recognition using probabilistic description logics. Activity Context Representation: Techniques and Languages, AAAI Technical Report WS-12-05, 26–31.Google Scholar
Kowalski, R. and Sadri, F. 1996. Towards a unified agent architecture that combines rationality with reactivity. In Logic in Databases, Vol. 1154, Pedreschi, D. and Zaniolo, C., Eds. Lecture Notes in Computer Science, Springer, Berlin Heidelberg, 135–149.Google Scholar
Kowalski, R. and Sadri, F. 2010. An agent language with destructive assignment and model-theoretic semantics. In Proc. of the 11th International Conference on Computational Logic in Multi-agent Systems, CLIMA'10. Springer-Verlag, Berlin, Heidelberg, 200–218.Google Scholar
Kowalski, R. and Sadri, F. 2012. A logic-based framework for reactive systems. In Rules on the Web: Research and Applications, Vol. 7438, Bikakis, A. and Giurca, A., Eds. Lecture Notes in Computer Science, Springer, Berlin Heidelberg, 1–15.Google Scholar
Kowalski, R. and Sadri, F. 2015. Reactive computing as model generation. New Generation Computing 33, 1, 33–67.Google Scholar
Kowalski, R. and Sergot, M. 1986. A logic-based calculus of events. New Generation Computing 4, 1 (Jan.), 67–95.Google Scholar
Lee, J. and Palla, R. 2012. Reformulating the situation calculus and the event calculus in the general theory of stable models and in answer set programming. Journal of Artificial Intelligence Research 43, 1 (Jan.), 571–620.Google Scholar
Levesque, H. and Lakemeyer, G. 2007. Cognitive robotics. In Handbook of Knowledge Representation, Elsevier Science, San Diego, USA, 869–886.Google Scholar
Lifschitz, V. 1994. Circumscription. In Handbook of Logic in Artificial Intelligence and Logic Programming, Oxford University Press, Vol. 3, 297–352.Google Scholar
Lu, C.-H. and 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
Ma, J., Miller, R., Morgenstern, L. and Patkos, T. 2013. An epistemic event calculus for ASP-based reasoning about knowledge of the past, present and future. In LPAR 2013, 19th International Conference on Logic for Programming, Artificial Intelligence and Reasoning, December 12–17, 2013, Stellenbosch, South Africa, Short papers proceedings. 75–87.Google Scholar
Miller, R., Morgenstern, L. and Patkos, T. 2013. Reasoning about knowledge and action in an epistemic event calculus. In Proc. 11th International Symposium on Logical Formalizations of Commonsense Reasoning.Google Scholar
Miller, R. and Shanahan, M. 2002. Some alternative formulations of the event calculus. In Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II, Kakas, A. and Sadri, F., Eds. Springer-Verlag, London, UK, 452–490.Google Scholar
Mueller, E. 2006. Commonsense Reasoning, 1st ed. Morgan Kaufmann.CrossRefGoogle Scholar
Mueller, E. T. 2004. Event calculus reasoning through satisfiability. Journal of Logic and Computation 14, 5, 703–730.CrossRefGoogle Scholar
Muñoz, A., Botía, J. A. and Augusto, J. C. 2010. Intelligent decision-making for a smart home environment with multiple occupants. In Computational Intelligence in Complex Decision Systems, Vol. 2, Atlantis Computational Intelligence Systems, 325–371.Google Scholar
Patkos, T., Chibani, A., Plexousakis, D. and Amirat, Y. 2012. A production rule-based framework for causal and epistemic reasoning. In Proc. of the 6th International Conference on Rules on the Web: Research and Applications, RuleML'12. Springer-Verlag, Berlin, Heidelberg, 120–135.Google Scholar
Patkos, T. and Plexousakis, D. 2009. Reasoning with knowledge, action and time in dynamic and uncertain domains. In Proc. of the 21st International Jont Conference on Artifical Intelligence. IJCAI'09, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 885–890.Google Scholar
Pecora, F., Cirillo, M., Dell'Osa, F., Ullberg, J. and Saffiotti, A. 2012. A constraint-based approach for proactive, context-aware human support. Journal of Ambient Intelligence and Smart Environments (JAISE) 4, 4, 347–367.Google Scholar
Petrick, R. P. A. and Levesque, H. J. 2002. Knowledge Equivalence in Combined Action Theories. In 8th International Conference on Principles of Knowledge Representation and Reasoning (KR2002). 303–314.Google Scholar
Riboni, D. and Bettini, C. 2011. COSAR: Hybrid Reasoning for Context-aware Activity Recognition. Personal and Ubiquitous Computing 15, 3 (Mar.), 271–289.Google Scholar
Roy, P., Giroux, S., Bouchard, B., Bouzouane, A., Phua, C., Tolstikov, A. and 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, vol. 4, Chen, L., Nugent, C. D., Biswas, J., and Hoey, J., Eds. Atlantis Ambient and Pervasive Intelligence, Atlantis Press, 33–58.Google Scholar
Sadri, F. 2011. Ambient intelligence: A survey. ACM Compututing Surveys 43, 4 (Oct.), 36:1–36:66.Google Scholar
Shanahan, M. 1999. The ramification problem in the event calculus. In Proc. of the 16th International Joint Conference on Artifical Intelligence, IJCAI'99, Vol. 1. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 140–146.Google Scholar
Singla, G., Cook, D. J. and 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.Google Scholar
Skarlatidis, A., Artikis, A., Filippou, J. and Paliouras, G. 2015. A Probabilistic logic programming event calculus. Theory and Practice of Logic Programming 15, 213–245.Google Scholar
Skarlatidis, A., Paliouras, G., Vouros, G. A. and Artikis, A. 2011. Probabilistic event calculus based on Markov logic networks. In Proc. of 5th International Symposium on Rule-Based Modeling and Computing on the Semantic Web (RuleML), Oxford University Press, 155–170.Google Scholar
Thielscher, M. 2000. Challenges for Action Theories: Solving the Ramification and Qualification Problem. LNAI, Vol. 1775. Springer.Google Scholar
van Kasteren, T., Noulas, A., Englebienne, G. and Kröse, B. 2008. Accurate activity recognition in a home setting. In Proc. of the 10th International Conference on Ubiquitous Computing. UbiComp '08, ACM, New York, NY, USA, 1–9.Google Scholar
Yang, Q. 2009. Activity recognition: Linking low-level sensors to high-level intelligence. In Proc. of the 21st International Joint Conference on Artifical Intelligence, IJCAI'09, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 20–25.Google Scholar
Ye, J., Dobson, S. and McKeever, S. 2012. Situation identification techniques in pervasive computing: A review. Pervasive and Mobile Computing 8, 1 (Feb.), 36–66.Google Scholar
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