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11 - Actions and agents

Published online by Cambridge University Press:  05 July 2014

Eduardo Alonso
Affiliation:
City University London
Keith Frankish
Affiliation:
The Open University, Milton Keynes
William M. Ramsey
Affiliation:
University of Nevada, Las Vegas
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Summary

Introduction

Classical artificial intelligence (AI) approaches to action tended to focus on single, isolated software systems that acted in a relatively inflexible way, automatically following pre-set rules. However, new technologies and software applications have created a need for artificial entities that are more autonomous, flexible, and adaptive, and that operate as social entities in multi-agent systems. This chapter introduces and surveys this emerging agent-centered AI and highlights the importance of developing theories of action, learning, and negotiation in multi-agent scenarios such as the internet.

Action in AI

Historically, the “Physical Symbol System Hypothesis” in AI (Newell and Simon 1976) has been embedded in so-called deliberative systems. Such systems are characterized by containing symbolic models of the world, and decisions about which actions to perform are made via manipulation of these symbols. To get an AI system to “act” it is enough to give it a logical representation of a theory of action (how systems make decisions and act accordingly) and get it to do a bit of theorem proving.

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Publisher: Cambridge University Press
Print publication year: 2014

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References

Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd edn.). Upper Saddle River, NJ: Prentice Hall. The third edition of the first AI handbook that shamelessly introduced AI from an agent’s perspective. See in particular the second chapter on Intelligent Agents.Google Scholar
Wooldridge, M. (2009). An Introduction to Multiagent Systems. Chichester, UK: John Wiley & Sons. The second edition of an ideal introductory text on agents and multi-agent systems, despite being somewhat limited in its coverage of learning.Google Scholar
Alonso, E. (2002). AI and agents: State of the art, AI Magazine 23: 25–30.Google Scholar
Alonso, E. (2004). Rights and argumentation in open multi-agent systems, Artificial Intelligence Review 21: 3–24.CrossRefGoogle Scholar
Alonso, E. (ed.) (2007). Multi-Agent Learning, Special issue of Autonomous Agents and Multi-Agent Systems 15(1).Google Scholar
Alonso, E., d’Inverno, M., Kudenko, D., Luck, M., and Noble, J. (2001). Learning in multi-agent systems, Knowledge Engineering Review 16: 277–84.CrossRefGoogle Scholar
Bond, A. H. and Gasser, L. (eds.) (1988). Readings in Distributed Artificial Intelligence. San Mateo, CA: Morgan Kaufmann.Google Scholar
Bordini, R., Dastani, M., Dix, J., and El Fallah-Seghrouchni, A. (eds.) (2005). Multi-Agent Programming: Languages, Platforms and Applications. Berlin: Springer.CrossRefGoogle Scholar
Bratman, M., Israel, D. J., and Pollack, M. E. (1988). Plans and resource-bounded practical reasoning, Computational Intelligence, 4: 349–55.CrossRefGoogle Scholar
Brooks, R. (1986). A robust layered control system for a mobile robot, IEEE Journal of Robotics and Automation, 2: 14–23.CrossRefGoogle Scholar
Busoniu, L., Babuska, R., and De Schutter, B. (2008). A comprehensive survey of multi-agent reinforcement learning, IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews 38: 156–72.CrossRefGoogle Scholar
Chapman, D. (1987). Planning for conjunctive goals, Artificial Intelligence, 32: 333–77.CrossRefGoogle Scholar
Durfee, E. H. (1988). Coordination for Distributed Problem Solvers. Boston, MA: Kluwer Academic.CrossRefGoogle Scholar
Etzioni, O. and Weld, D. (2007). Intelligent agents on the internet: Fact, fiction, and forecast, IEEE Expert: Intelligent Systems and Their Applications, 10: 44–9.CrossRefGoogle Scholar
Ferguson, I. A. (1992). TouringMachines: An Architecture for Dynamic, Rational, Mobile Agents. PhD thesis, University of Cambridge.
Fikes, R. and Nilsson, N. (1971). STRIPS: A new approach to the application of theorem proving to problem solving, Artificial Intelligence, 2: 189–208.CrossRefGoogle Scholar
Jennings, N., Faratin, P., Lomuscio, A., Parsons, S., Sierra, C., and Wooldridge, M. (2001). Automated negotiation: prospects, methods and challenges, International Journal of Group Decision and Negotiation 10: 199–215.CrossRefGoogle Scholar
Jennings, N., Sycara, K., and Wooldridge, M. (1998). A roadmap of agent research and development, Autonomous Agents and Multi-Agent Systems 1: 7–38.CrossRefGoogle Scholar
Jennings, N. and Wooldridge, M. (eds.) (1998). Agent Technology: Foundations, Applications, and Markets. Berlin: Springer.CrossRefGoogle Scholar
Kaelbling, L. P., Littman, M., and Moore, A. (1996). Reinforcement learning: A survey, Journal of Artificial Intelligence Research 4: 237–85.Google Scholar
Kraus, S. (2001). Strategic Negotiation in Multiagent Environments, Cambridge, MA: MIT Press.Google Scholar
Leake, D. (ed.). (2005). Twenty-fifth Anniversary Issue, special issue of AI Magazine 26(4).
Luck, M., McBurney, P., Shehory, O., and Willmott, S. (eds.) (2005). Agent Technology: Computing as Interaction (A Roadmap for Agent Based Computing). Southampton:AgentLink III.Google Scholar
Müller, J. (1997). A cooperation model for autonomous agents, in Müller, J., Wooldridge, M., and Jennings, N. (eds.), Intelligent Agents III: Agent Theories, Architectures, and Languages (pp. 245–60) (Lecture Notes in Computer Science, 1193). Berlin: Springer.CrossRefGoogle Scholar
Newell, A. and Simon, H. A. (1976). Computer science as empirical enquiry: Symbols and search, Communications of the ACM 19: 113–26.CrossRefGoogle Scholar
Rahwan, I., Ramchurn, S., Jennings, N., McBurney, P., Parsons, S., and Sonenberg, L. (2003). Argumentation-based negotiation, The Knowledge Engineering Review 18: 343–75.CrossRefGoogle Scholar
Rao, A. S., Georgeff, M. P., and Sonenberg, E. A. (1992). Social plans: A preliminary report, in Werner, E. and Demazeau, Y. (eds.), Decentralized AI 3: Proceedings of the 3rd European Workshop on Modelling Autonomous Agents in a Multi-Agent World (pp. 57–76), Amsterdam: Elsevier.Google Scholar
Rosenschein, J. S. and Zlotkin, G. (1994). Rules of Encounter: Designing Conventions for Automated Negotiation among Computers, Cambridge, MA: MIT Press.Google Scholar
Stone, P. and Veloso, M. (2000). Multiagent systems: A survey from a machine learning perspective, Autonomous Robots 8: 345–83.CrossRefGoogle Scholar
Tuyls, K. and Weiss, G. (2012). Multiagent learning: Basics, challenges, prospects, AI Magazine 33.CrossRefGoogle Scholar
Vohra, R. and Wellman, M. (eds.) (2007). Foundations of multi-agent learning, Artificial Intelligence 171: 363–4.CrossRefGoogle Scholar
Weiss, G. (ed.) (1999). Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Cambridge, MA: MIT Press.Google Scholar
Weiss, G. and Dillenbourg, P. (1999). What is “multi” in multiagent learning?, in Dillenbourg, P. (ed.), Collaborative Learning: Cognitive and computational approaches (pp. 64–80). Oxford: Pergamon Press.Google Scholar

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  • Actions and agents
  • Edited by Keith Frankish, The Open University, Milton Keynes, William M. Ramsey, University of Nevada, Las Vegas
  • Book: The Cambridge Handbook of Artificial Intelligence
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139046855.015
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  • Actions and agents
  • Edited by Keith Frankish, The Open University, Milton Keynes, William M. Ramsey, University of Nevada, Las Vegas
  • Book: The Cambridge Handbook of Artificial Intelligence
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139046855.015
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Actions and agents
  • Edited by Keith Frankish, The Open University, Milton Keynes, William M. Ramsey, University of Nevada, Las Vegas
  • Book: The Cambridge Handbook of Artificial Intelligence
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139046855.015
Available formats
×