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ALPprolog – A new logic programming method for dynamic domains

Published online by Cambridge University Press:  06 July 2011

CONRAD DRESCHER  and
MICHAEL THIELSCHER
CONRAD DRESCHER
Affiliation:
Computing Laboratory, University of Oxford, Oxford, UK (e-mail: Conrad.Drescher@comlab.ox.ac.uk)
MICHAEL THIELSCHER
Affiliation:
School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia (e-mail: mit@cse.unsw.edu.au)
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Abstract

Logic programming is a powerful paradigm for programming autonomous agents in dynamic domains as witnessed by languages such as Golog and Flux. In this work we present ALPprolog, an expressive, yet efficient, logic programming language for the online control of agents that have to reason about incomplete information and sensing actions.

Keywords

reasoning about actionsagent logic programs
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 11 , Special Issue 4-5: 27th International Conference on Logic Programming , July 2011 , pp. 451 - 468
Copyright
Copyright © Cambridge University Press 2011

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References

Bordini, R., Hübner, J. and Wooldridge, M. 2007. Programming Multi-Agent Systems in AgentSpeak Using Jason. Wiley, San Francisco, CA.Google Scholar
Drescher, C., Liu, H., Baader, F., Guhlemann, S., Petersohn, U., Steinke, P. and Thielscher, M. 2009a. Putting abox updates into action. In Proceedings of the Seventh International Symposion on Frontiers of Combining Systems (FroCoS 2009), Trento, Italy. Springer-Verlag, Berlin, Germany.Google Scholar
Drescher, C., Schiffel, S. and Thielscher, M. 2009b. A declarative agent programming language based on action theories. In Proceedings of the Seventh International Symposion on Frontiers of Combining Systems (FroCoS 2009), Trento, Italy. Springer-Verlag, Berlin, Germany.Google Scholar
Genesereth, M. R., Love, N. and Pell, B. 2005. General game playing: Overview of the AAAI competition. AI Magazine 26 (2), 62–72.Google Scholar
Herzig, A. and Varzinczak, I. 2007. Metatheory of actions: Beyond consistency. Artificial Intelligence 171 (16–17), 951–984.CrossRefGoogle Scholar
Lloyd, J. W. 1987. Foundations of Logic Programming. Springer, New York.CrossRefGoogle Scholar
Levesque, H., Reiter, R., Lespérance, Y., Lin, F. and Scherl, R. 1997. GOLOG: A logic programming language for dynamic domains. Journal of Logic Programming 31 (1–3), 59–83.CrossRefGoogle Scholar
Levesque, H. J. 1996. What is planning in the presence of sensing? In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI 1996), Portland, Oregon, USA. AAAI, California, 1139–1146.Google Scholar
Liu, H., Lutz, C., Milicic, M. and Wolter, F. 2006. Updating description logic ABoxes. In Proceedings of the Tenth International Conference on Principles of Knowledge Representation and Reasoning (KR 06), Lake District, UK.Google Scholar
Love, N., Hinrichs, T., Haley, D., Schkufza, E. and Genesereth, M. 2008. General game playing: Game description language specification. Technical Report, LG-2006-01, Stanford University, California.Google Scholar
McCarthy, J. and Hayes, P. J. 1969. Some philosophical problems from the standpoint of artificial intelligence. In Machine Intelligence 4, Meltzer, B. and Michie, D., Eds. Edinburgh University Press, Edinburgh, UK, 463–502.Google Scholar
Reiter, R. 2001a. Knowledge in Action: Logical Foundations for Describing and Implementing Dynamical Systems. MIT Press, Cambridge, MA.CrossRefGoogle Scholar
Reiter, R. 2001b. On knowledge-based programming with sensing in the situation calculus. ACM Transactions on Computational Logic 2 (4), 433–457.CrossRefGoogle Scholar
Russell, S. J. and Norvig, P. 2003. Artificial Intelligence: A Modern Approach, 2nd international ed. Prentice Hall, Upper Saddle River, NJ.Google Scholar
Sardina, S. and Vassos, S. 2005. The wumpus world in IndiGolog: A preliminary report. In Proceedings of the Workshop on Nonmonotonic Reasoning, Action and Change at IJCAI, Edinburgh, UK. IJCAI, Freiburg, Germany, 90–95Google Scholar
Schiffel, S. and Thielscher, M. 2007. Fluxplayer: A successful general game player. In Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI 2007). AAAI Press, Menlo Park, CA, 1191–1196.Google Scholar
Thielscher, M. 1999. From situation calculus to fluent calculus: State update axioms as a solution to the inferential frame problem. Artificial Intelligence 111 (1–2), 277–299.CrossRefGoogle Scholar
Thielscher, M. 2005a. FLUX: A logic programming method for reasoning agents. Theory and Practice of Logic Programming 5 (4–5), 533–565.CrossRefGoogle Scholar
Thielscher, M. 2005b. A FLUX agent for the Wumpus World. In Proceedings of the Workshop on Nonmonotonic Reasoning, Action and Change at IJCAI, Edinburgh, UK, Morgenstern, L. and Pagnucco, M., Eds. IJCAI, Freiburg, Germany, 104–108.Google Scholar
Thielscher, M. 2010a. A general game description language for incomplete information games. In Proceedings of the Twenty-Fourth National Conference on Artificial Intelligence (AAAI 2010). AAAI Press, Atlanta, 994–999.Google Scholar
Thielscher, M. 2010b. Integrating action calculi and AgentSpeak: Closing the gap. In Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (KR), Toronto, Canada, 79–89.Google Scholar
Thielscher, M. 2011. A unifying action calculus. Artificial Intelligence Journal 175, 120–141.CrossRefGoogle Scholar