Abstract
We consider the problem of verifying whether one action theory can simulate a second one. Action theories provide modular descriptions of state machines, and simulation means that all possible sequences of actions in one transition system can be matched by the other. We show how Answer Set Programming can be used to automatically prove simulation by induction from an axiomatisation of two action theories and a projection function between them. Our interest in simulation of action theories comes from general game-playing robots as systems that can understand the rules of new games and learn to play them effectively in a physical environment. A crucial property of such games is their playability, that is, each legal play sequence in the abstract game must be executable in the real environment.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Apt, K., Blair, H., Walker, A.: Towards a theory of declarative knowledge. In: Minker, J. (ed.) Foundations of Deductive Databases and Logic Programming, ch. 2, pp. 89–148. Morgan Kaufmann (1987)
Babb, J., Lee, J.: cplus2ASP: Computing action language \({\cal C}\)+ in answer set programming. In: Cabalar, P., Son, T.C. (eds.) LPNMR 2013. LNCS, vol. 8148, pp. 122–134. Springer, Heidelberg (2013)
van Benthem, J.: Logic in Games. MIT Press (2014)
Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming at a glance. Communications of the ACM 54(12), 92–103 (2011)
Brewka, G., Hertzberg, J.: How to do things with worlds: on formalizing actions and plans. Journal of Logic and Computation 3(5), 517–532 (1993)
Cerexhe, T., Gebser, M., Thielscher, M.: Online agent logic programming with oClingo. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS, vol. 8862, pp. 945–957. Springer, Heidelberg (2014)
Clune, J.: Heuristic evaluation functions for general game playing. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1134–1139. AAAI Press, Vancouver (2007)
Fikes, R., Nilsson, N.: STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2, 189–208 (1971)
Finnsson, H., Björnsson, Y.: Simulation-based approach to general game playing. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 259–264. AAAI Press, Chicago (2008)
Finnsson, H., Björnsson, Y.: Learning simulation control in general game-playing agents. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 954–959. AAAI Press, Atlanta (2010)
Fox, M., Long, D.: PDDL2.1: an extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61–124 (2003)
Gebser, M., Kaminski, R., Knecht, M., Schaub, T.: plasp: A prototype for PDDL-based planning in ASP. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS, vol. 6645, pp. 358–363. Springer, Heidelberg (2011)
Gelfond, M.: Answer sets. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Handbook of Knowledge Representation, pp. 285–316. Elsevier (2008)
Gelfond, M., Lifschitz, V.: Representing action and change by logic programs. Journal of Logic Programming 17, 301–321 (1993)
Genesereth, M., Björnsson, Y.: The international general game playing competition. AI Magazine 34(2), 107–111 (2013)
Genesereth, M., Love, N., Pell, B.: General game playing: Overview of the AAAI competition. AI Magazine 26(2), 62–72 (2005)
Genesereth, M., Thielscher, M.: General Game Playing. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool (2014)
Haufe, S., Schiffel, S., Thielscher, M.: Automated verification of state sequence invariants in general game playing. Artificial Intelligence, 187–188, 1–30 (2012)
Hsu, F.H.: Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press (2002)
Kowalski, R.: Database updates in the event calculus. Journal of Logic Programming 12, 121–146 (1992)
Lee, J.: 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, 571–620 (2012)
Li, N., Fan, Y., Liu, Y.: Reasoning about state constraints in the situation calculus. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China (August 2013)
Lloyd, J.: Foundations of Logic Programming, 2nd extended edn. Series Symbolic Computation. Springer (1987)
Lloyd, J., Topor, R.: A basis for deductive database systems II. Journal of Logic Programming 3(1), 55–67 (1986)
Love, N., Hinrichs, T., Haley, D., Schkufza, E., Genesereth, M.: General Game Playing: Game Description Language Specification. Tech. Rep. LG–2006–01, Stanford Logic Group, Computer Science Department, Stanford University, 353 Serra Mall, Stanford, CA 94305 (2006), games.stanford.edu
McCarthy, J.: Situations and Actions and Causal Laws. Stanford Artificial Intelligence Project, Memo 2, Stanford University, CA (1963)
Pritchard, D.: The Encycolpedia of Chess Variants. Godalming (1994)
Rajaratnam, D., Thielscher, M.: Towards general game-playing robots: Models, architecture and game controller. In: Cranefield, S., Nayak, A. (eds.) AI 2013. LNCS, vol. 8272, pp. 271–276. Springer, Heidelberg (2013)
Sandewall, E.: Features and Fluents. The Representation of Knowledge about Dynamical Systems. Oxford University Press (1994)
Sangiorgi, D.: Introduction to Bisumlation and Coinduction. Cambridge University Press (2011)
Schiffel, S., Thielscher, M.: Fluxplayer: A successful general game player. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1191–1196. AAAI Press, Vancouver (2007)
Schiffel, S., Thielscher, M.: A multiagent semantics for the game description language. In: Filipe, J., Fred, A., Sharp, B. (eds.) ICAART 2009. CCIS, vol. 67, pp. 44–55. Springer, Heidelberg (2010)
Thielscher, M.: From situation calculus to fluent calculus: State update axioms as a solution to the inferential frame problem. Artificial Intelligence 111(1-2), 277–299 (1999)
Thielscher, M.: Answer set programming for single-player games in general game playing. In: Hill, P.M., Warren, D.S. (eds.) ICLP 2009. LNCS, vol. 5649, pp. 327–341. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Thielscher, M. (2015). Simulation of Action Theories and an Application to General Game-Playing Robots. In: Eiter, T., Strass, H., Truszczyński, M., Woltran, S. (eds) Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation. Lecture Notes in Computer Science(), vol 9060. Springer, Cham. https://doi.org/10.1007/978-3-319-14726-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-14726-0_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14725-3
Online ISBN: 978-3-319-14726-0
eBook Packages: Computer ScienceComputer Science (R0)