Plan Recognition by Program Execution in Continuous Temporal Domains
Abstract
Much of the existing work on plan recognition assumes that actions of other agents can be observed directly. In continuous temporal domains such as traffic scenarios this assumption is typically not warranted. Instead, one is only able to observe facts about the world such as vehicle positions at different points in time, from which the agents’ plans need to be inferred. In this paper we show how this problem can be addressed in the situation calculus and a new variant of the action programming language Golog, which includes features such as continuous time and change, stochastic actions, nondeterminism, and concurrency. In our approach we match observations against a set of candidate plans in the form of Golog programs. We turn the observations into actions which are then executed concurrently with the given programs. Using decision-theoretic optimization techniques those programs are preferred which bring about the observations at the appropriate times. Besides defining this new variant of Golog we also discuss an implementation and experimental results using driving maneuvers as an example.
Keywords
Reward Function Program Execution Atomic Action Vehicle Position Primitive ActionPreview
Unable to display preview. Download preview PDF.
References
- 1.Bacchus, F., Halpern, J.Y., Levesque, H.J.: Reasoning about noisy sensors and effectors in the situation calculus. Artificial Intelligence 111(1-2), 171–208 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
- 2.Boutilier, C., Reiter, R., Soutchanski, M., Thrun, S.: Decision-theoretic, high-level agent programming in the situation calculus. In: Proc. of the 17th Nat’l Conf. on Artificial Intelligence (AAAI 2000), Menlo Park, CA, pp. 355–362 (July 2000)Google Scholar
- 3.Bui, H.H., Venkatesh, S., West, G.: Policy recognition in the abstract hidden markov model. Journal of Artificial Intelligence Research 17, 451–499 (2002)MathSciNetzbMATHGoogle Scholar
- 4.Charniak, E., Goldman, R.: A probabilistic model of plan recognition. In: Proc. of the Ninth Nat’l Conf. on Artificial Intelligence (AAAI 1991), pp. 160–165 (1991)Google Scholar
- 5.Geib, C., Goldman, R.: A probabilistic plan recognition algorithm based on plan tree grammars. Artificial Intelligence 173, 1101–1132 (2009)MathSciNetCrossRefGoogle Scholar
- 6.De Giacomo, G., Lespérance, Y., Levesque, H.J.: ConGolog, a concurrent programming language based on the situation calculus. Artif. Intell. 121, 109–169 (2000)zbMATHCrossRefGoogle Scholar
- 7.Goultiaeva, A., Lespérance, Y.: Incremental plan recognition in an agent programming framework. In: Geib, C., Pynadath, D. (eds.) Proc. of the AAAI Workshop on Plan, Activity, and Intent Recognition (PAIR 2007), pp. 52–59. AAAI Press (July 2007)Google Scholar
- 8.Grosskreutz, H., Lakemeyer, G.: cc-Golog – an action language with continuous change. Logic Journal of the IGPL 11(2), 179–221 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
- 9.Kautz, H.A., Allen, J.F.: Generalized plan recognition. In: Proc. of the Fifth Nat’l Conf. on Artificial Intelligence (AAAI 1986), pp. 32–37 (1986)Google Scholar
- 10.Levesque, H., Reiter, R., Lespérance, Y., Lin, F., Scherl, R.: GOLOG: A logic programming language for dynamic domains. J. Log. Program. 31, 59–84 (1997)zbMATHCrossRefGoogle Scholar
- 11.Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Learning and inferring transportation routines. Artificial Intelligence 171(5-6), 311–331 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
- 12.McCarthy, J.: Situations, Actions, and Causal Laws. Technical Report AI Memo 2 AIM-2, AI Lab, Stanford University (July 1963)Google Scholar
- 13.Pynadath, D.V., Wellman, M.P.: Accounting for context in plan recognition, with application to traffic monitoring. In: Proc. of the Eleventh Annual Conf. on Uncertainty in Artificial Intelligence (UAI 1995), pp. 472–481. Morgan Kaufmann (1995)Google Scholar
- 14.Ramirez, M., Geffner, H.: Plan recognition as planning. In: Proc. of the 21st Int’l Joint Conf. on Artificial Intelligence (IJCAI 2009), pp. 1778–1783 (2009)Google Scholar
- 15.Reiter, R.: Sequential, temporal GOLOG. In: Proc. of the Int’l Conf. on Principles of Knowledge Representation and Reasoning (KR 1998), pp. 547–556 (1998)Google Scholar
- 16.Reiter, R.: Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. The MIT Press (2001)Google Scholar
- 17.Van de Weghe, N., Cohn, A.G., Maeyer, P.D., Witlox, F.: Representing moving objects in computer-based expert systems: the overtake event example. Expert Systems with Applications 29, 977–983 (2005)CrossRefGoogle Scholar