Decision-Theoretic, High-level Agent Programming in the Situation Calculus

Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI 2000

Volumn , Issue , 2000, Pages 355-362

  Boutilier, Craig ^a   Reiter, Ray ^a   Soutchanski, Mikhail ^a   Thrun, Sebastian ^b  

^a UNIVERSITY OF TORONTO   (Canada)

^b Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HIGH LEVEL LANGUAGES; MARKOV PROCESSES; PROGRAM INTERPRETERS;

AGENT PROGRAMMING; CONTROL PROGRAM; DECISION-THEORETIC; DECISION-THEORETIC PLANNING; LOGICAL AXIOMATIZATION; LOGICAL LANGUAGE; MARKOV DECISION PROCESSES; OPTIMAL COMPLETION; SEAMLESS INTEGRATION; SITUATION CALCULUS;

ROBOT PROGRAMMING;

EID: 84880895882     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. F. Bacchus, J. Halpern, and H. Levesque. Reasoning about noisy sensors in the situation calculus. IJCAI-95, pp.1933-1940, Montreal, 1995.
2. F. Bacchus and F. Kabanza. Using temporal logic to control search in a forward chaining planner. In M. Ghallab, A. Milani, eds., New Directions in Planning, pp.141-153, 1996. IOS Press.
3. A. Barto, S. Bradtke, and S. Singh. Learning to act using real-time dynamic programming. Art. Intel., 72:81-138, 1995.
4. C. Boutilier, T. Dean, and S. Hanks. Decision theoretic planning: Structural assumptions and computational leverage. J. Art. Intel. Res., 11:1-94, 1999.
5. W. Burgard, A. Cremers, D. Fox, D. Hähnel, G. Lakemeyer, D. Schulz, W. Steiner, and S. Thrun. Experiences with an interactive museum tour-guide robot. Art. Intel., 114, 1999.
6. R. Dearden and C. Boutilier. Abstraction and approximate decision theoretic planning. Art. Intel., 89:219-283, 1997.
7. H. Geffner and B. Bonet. High-level planning and control with incomplete information using POMDPs. AAAI Fall Symp. on Cognitive Robotics, Orlando, 1998.
8. M. Kearns, Y. Mansour, and A. Ng. A sparse sampling algorithm for near-optimal planning in large Markov decision processes. IJCAI-99, Stockholm, 1999.
9. S. Koenig and R. Simmons. Real-time search in nondeterministic domains. IJCAI-95, pp.1660-1667, Montreal, 1995.
10. H. Levesque, R. Reiter, Y. Lespérance, F. Lin, and R. Scherl. GOLOG: a logic programming language for dynamic domains. J. Logic Prog., 31(1-3):59-83, 1997.
11. R. Parr and S. Russell. Reinforcement learning with hierarchies of machines. NIPS-10, pp.1043-1049. MIT Press. 1998.
12. D. Poole. The independent choice logic for modelling multiple agents under uncertainty. Art. Intel., 94:7-56, 1997.
13. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
14. R. Reiter. Natural actions, concurrency and continuous time in the situation calculus. KR'96, pp.2-13, Cambridge, 1996.
15. R. Reiter. Sequential, temporal GOLOG. KR'98, pp.547-556, Trento, 1998.
16. R. Reiter. The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression. In V. Lifschitz, ed, Artificial Intelligence and Mathematical Theory of Computation (Papers in Honor of John McCarthy), pp.359-380. Academic Press, 1991.
17. M. Soutchanski. Execution monitoring of high-level temporal programs. IJCAI-99 Workshop on Robot Action Planning, Stockholm, 1999.
18. R. Sutton. TD models: Modeling the world at a mixture of time scales. ICML-95, pp.531-539, Lake Tahoe, 1995.
19. S. Thrun, M. Bennewitz, W. Burgard, A. Cremers, F. Dellaert, D. Fox, D. Hähnel, C. Rosenberg, N. Roy, J. Schulte, and D. Schulz. MINERVA: A second generation mobile tour-guide robot. ICRA-99, 1999.