Probabilistic hybrid action models for predicting concurrent percept-driven robot behavior

Journal of Artificial Intelligence Research

Volumn 24, Issue , 2005, Pages 799-849

  Beetz, Michael ^a   Grosskreutz, Henrik ^b  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLOSED LOOP CONTROL SYSTEMS; MATHEMATICAL MODELS; PLANNING; ROBOTS; SAMPLING;

CAUSAL MODELS; PROBABILISTIC HYBRID ACTION MODELS (PHAMS); ROBOT PLANS; SAMPLING PROJECTIONS;

PROBABILITY;

EID: 31144431760     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.1565     Document Type: Article

References (83)

1. Alami, R., Chatila, R., Fleury, S., Ingrand, M. H. F., Khatib, M., Morisset, B., Moutarlier, P., & Simeon, T. (2000). Around the lab in 40 days.... In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2000), pp. 88-94.
2. Allen, J., & Ferguson, G. (1994). Actions and events in interval temporal logic. Journal of Logic and Computation, 4(5), 531-579.
3. Allen, J., Kautz, H., Pelavin, R., & Tenenberg, J. (Eds.). (1990). Reasoning about Plans. Morgan Kaufmann.
4. Alur, R., Henzinger, T., & Ho, P. (1996). Automatic symbolic verification of embedded systems. IEEE Transactions on Software Engineering, 22(3), 181-201.
5. Alur, R., Henzinger, T., & Wong-Toi, H. (1997). Symbolic analysis of hybrid systems. In Proceedings of the Thirtyssixth IEEE Conference on Decision and Control (CDC), pp. 702-707. IEEE Press.
6. Andre, D., & Russell, S. (2001). Programmable reinforcement learning agents. In Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, pp. 1019-1025. MIT Press.
7. Arkin, R. (1998). Behavior based Robotics. MIT Press.
8. Bacchus, F., Halpern, J., & Levesque, H. (1999). Reasoning about noisy sensors and effectors in the situation calculus. Artificial Intelligence 111(1-2).
9. Beetz, M. (1999). Structured Reactive Controllers - a computational model of everyday activity. In Etzioni, O., Müller, J., & Bradshaw, J. (Eds.), Proceedings of the Third International Conference on Autonomous Agents, pp. 228-235.
10. Beetz, M. (2000). Concurrent Reactive Plans: Anticipating and forestalling execution failures, Vol. LNAI 1772 of Lecture Notes in Artificial Intelligence. Springer Publishers.
11. Beetz, M. (2001). Structured Reactive Controllers. Journal of Autonomous Agents and Multi-Agent Systems. Special Issue: Best Papers of the International Conference on Autonomous Agents '99, 4, 25-55.
12. Beetz, M. (2002a). Plan-based Control of Robotic Agents, Vol. LNAI 2554 of Lecture Notes in Artificial Intelligence. Springer Publishers.
13. Beetz, M. (2002b). Plan representation for robotic agents. In Proceedings of the Sixth International Conference on AI Planning and Scheduling, pp. 223-232.
14. Beetz, M., Arbuckle, T., Bennewitz, M., Burgard, W., Cremers, A., Fox, D., Grosskreutz, H., Hähnel, D., & Schulz, D. (2001). Integrated plan-based control of autonomous service robots in human environments. IEEE Intelligent Systems, 16(5), 56-65.
15. Beetz, M., Arbuckle, T., Cremers, A., & Mann, M. (1998). Transparent, flexible, and resource-adaptive image processing for autonomous service robots. In Prade, H. (Ed.), Proceedings of the Thirteenth European Conference on Artificial Intelligence (ECAI-98), pp. 632-636.
16. Beetz, M., & Belker, T. (2000). Environment and task adaptation for robotic agents. In Horn, W. (Ed.), Proceedings of the Fourteenth European Conference on Artificial Intelligence (ECAI-2000), pp. 648-652.
17. Beetz, M., Bennewitz, M., & Grosskreutz, H. (1999). Probabilistic, prediction-based schedule debugging for autonomous robot office couriers. In Proceedings of the Twentythird German Conference on Artificial Intelligence (KI 99), Bonn, Germany, pp. 243-254. Springer Publishers.
18. Beetz, M., Burgard, W., Fox, D., & Cremers, A. (1998). Integrating active localization into high-level control systems. Robotics and Autonomous Systems, 23, 205-220.
19. Beetz, M., & Grosskreutz, H. (1998). Causal models of mobile service robot behavior. In Simmons, R., Veloso, M., & Smith, S. (Eds.), Proceedings of the Fourth International Conference on AI Planning Systems, pp. 163-170, Morgan Kaufmann.
20. Beetz, M., & Grosskreutz, H. (2000). Probabilistic hybrid action models for predicting concurrent percept-driven robot behavior. In Proceedings of the Sixth International Conference on AI Planning Systems, Toulouse, France. AAAI Press.
21. Beetz, M., & McDermott, D. (1992). Declarative goals in reactive plans. In Hendler, J. (Ed.), Proceedings of the First International Conference on AI Planning Systems, pp. 3-12, Morgan Kaufmann.
22. Beetz, M., & McDermott, D. (1996). Local planning of ongoing activities. In Drabble, B. (Ed.), Proceedings of the Third International Conference on AI Planning Systems, pp. 19-26, Morgan Kaufmann.
23. Beetz, M., & Peters, H. (1998). Structured reactive communication plans -integrating conversational actions into high-level robot control systems. In Proceedings of the Twentysecond German Conference on Artificial Intelligence (KI 98), Bremen, Germany. Springer Publishers.
24. Belker, T., Beetz, M., & Cremers, A. (2002). Learning action models for the improved execution of navigation plans. Robotics and Autonomous Systems, 38(3-4), 137-148.
25. Bertoli, P., Cimatti, A., & Roveri, M. (2001). Planning in nondeterministic domains under partial observability via symbolic model checking. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01). AAAI Press.
26. Blythe, J. (1995). AI planning in dynamic, uncertain domains. In Extending Theories of Action: Formal Theory & Practical Applications: Papers from the 1995 AAAI Spring Symposium, pp. 28-32. AAAI Press, Menlo Park, CA.
27. Blythe, J. (1996). Decompositions of Markov chains for reasoning about external change in planners. In Drabble, B. (Ed.), Proceedings of the 3rd International Conference on Artificial Intelligence Planning Systems (AIPS-96), pp. 27-34. AAAI Press.
28. Blythe, J. (1999). Decision-theoretic planning. AI Magazine, 20(2), 37-54.
29. Bonasso, P., Firby, J., Gat, E., Kortenkamp, D., Miller, D., & Slack, M. (1997). Experiences with an architecture for intelligent, reactive agents. Journal of Experimental and Theoretical Artificial Intelligence, 9(1).
30. Boutilier, C., Dean, T., & Hanks, S. (1998). Decision theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research, 11, 1-94.
31. Boutilier, C., Reiter, R., Soutchanski, M., & Thrun, S. (2000). Decision-theoretic, high-level robot programming in the situation calculus. In Proceedings of the Seventeenth AAAI National Conference on Artificial Intelligence, pp. 355-362, Austin, TX.
32. Bradtke, S., & Duff, M. (1995). Reinforcement learning methods for continuous-time Markov decision problems. In Tesauro, G., Touretzky, D., & Leen, T. (Eds.), Advances in Neural Information Processing Systems, Vol. 7, pp. 393-400. MIT Press.
33. Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1), 14-23.
34. Burgard, W., Cremers, A., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., Steiner, W., & Thrun, S. (2000). Experiences with an interactive museum tour-guide robot. Artificial Intelligence, 114(1-2), 3-55.
35. Cimatti, A., & Roveri, M. (2000). Conformant planning via symbolic model checking. Journal of Artificial Intelligence Research (JAIR), 13, 305-338.
36. Dean, T., Firby, J., & Miller, D. (1988). Hierarchical planning involving deadlines, travel time and resources. Computational Intelligence, 4(4), 381-398.
37. Doherty, P., Granlund, G., Krzysztof, G., Sandewall, E., Nordberg, K., Skarman, E., & Wiklund, J. (2000). The WITAS unmanned aerial vehicle project. In Proceedings of the Fourteenth European Conference on Artificial Intelligence (ECAI-00), pp. 747-755, Berlin, Germany.
38. Drabble, B. (1993). Excalibur: a program for planning and reasoning with processes. Artificial Intelligence, 62, 1-40.
39. Draper, D., Hanks, S., & Weld, D. (1994). Probabilistic planning with information gathering and contingent execution. In Proceedings of the Second International Conference on AI Planning Systems, p. 31.
40. Firby, J. (1987). An investigation into reactive planning in complex domains. In Proceedings of the Sixth National Conference on Artificial Intelligence, pp. 202-206, Seattle, WA.
41. Forbus, K. (1984). Qualitative process theory. Artificial Intelligence, 24, 85-168.
42. Fox, D., Burgard, W., Dellaert, F., & Thrun, S. (1999). Monte Carlo localization: Efficient position estimation for mobile robots. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, Orlando, FL.
43. Ghallab, M., & Laruelle, H. (1994). Representation and control in IxTeT, a temporal planner. In Hammond, K. (Ed.), Proceedings of the Second International Conference on AI Planning Systems, pp. 61-67, Morgan Kaufmann.
44. Giacomo, G. D., Lesperance, Y., & Levesque, H. (1997). Reasoning about concurrent execution, prioritized interrupts, and exogene ous actions in the situation calculus. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan.
45. Grosskreutz, H., & Lakemeyer, G. (2000a). cc-Golog: Towards more realistic logic-based robot controllers. In Proceedings of the Seventeenth National Conference on Artificial Intelligence.
46. Grosskreutz, H., & Lakemeyer, G. (2000b). Turning high-level plans into robot programs in uncertain domains. In Proceedings of the Fourteenth European Conference on Artificial Intelligence (ECAI-00), pp. 548-552.
47. Haddawy, P., & Hanks, S. (1992). Representations for decision-theoretic planning: Utility functions for deadline goals. In Nebel, B., Rich, C., & Swartout, W. (Eds.), Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning, pp. 71-82, Cambridge, MA. Morgan Kaufmann.
48. Haddawy, P., & Rendell, L. (1990). Planning and decision theory. The Knowledge Engineering Review, 5, 15-33.
49. Hanks, S. (1990). Practical temporal projection. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pp. 158-163.
50. Hanks, S., Madigan, D., & Gavrin, J. (1995). Probabilistic temporal reasoning with endogenous change. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 245-254. Morgan Kaufmann.
51. Hayes, P. (1985). The second naive physics manifesto. In Hobbs, J. R., & Moore, R. C. (Eds.), Formal Theories of the Commonsense World, pp. 1-36. Ablex, Norwood, NJ.
52. Hendrix, G. (1973). Modeling simultaneous actions and continuous processes. Artificial Intelligence, 4, 145-180.
53. Horswill, I. (1996). Integrated systems and naturalistic tasks. In: Strategic Directions in Computing Research, AI Working Group.
54. Kabanza, F., Barbeau, M., & St-Denis, R. (1997). Planning control rules for reactive agents. Artificial Intelligence, 95, 67-113.
55. Kaelbling, L., Cassandra, A., & Kurien, J. (1996). Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.
56. Konolige, K., Myers, K., Ruspini, E., & Saffiotti, A. (1997). The Saphira architecture: A design for autonomy. Journal of Experimental and Theoretical Artificial Intelligence, 9(2).
57. Kushmerick, N., Hanks, S., & Weld, D. (1995). An algorithm for probabilistic planning. Artificial Intelligence, 76, 239-286.
58. Lakemeyer, G. (1999). On sensing and off-line interpreting in golog. In Levesque, H., & Pirri, F. (Eds.), Logical Foundations for Cognitive Agents. Springer Publishers.
59. Levesque, H., Reiter, R., Lesperance, Y., Lin, F., & Scherl, R. (1997). Golog: A logic programming language for dynamic domains. Journal of Logic Programming, 31, 59-84.
60. Levesque, H. J. (1996). What is planning in the presence of sensing. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 1139-1146, Portland, OR.
61. McAllester, D., & Rosenblitt, D. (1991). Systematic nonlinear planning. In Proceedings of the Ninth National Conference on Artificial Intelligence, pp. 634-639, Anaheim, CA.
62. McCarthy, J. (1963). Situations, actions and causal laws. Tech. rep., Stanford University. Reprinted 1968 in Semantic Information Processing (M. Minsky ed.).
63. McDermott, D. (1991). A Reactive Plan Language. Research Report YALEU/DCS/RR-864, Yale University.
64. McDermott, D. (1992a). Robot planning. AI Magazine, 13(2), 55-79.
65. McDermott, D. (1992b). Transformational planning of reactive behavior. Research Report YALEU/DCS/RR-941, Yale University.
66. McDermott, D. (1994). An algorithm for probabilistic, totally-ordered temporal projection. Research Report YALEU/DCS/RR-941, Yale University.
67. Muscettola, N., Morris, P., Pell, B., & Smith, B. (1998a). Issues in temporal reasoning for autonomous control systems. In Sycara, K., & Wooldridge, M. (Eds.), Proceedings of the Second International Conference on Autonomous Agents (AGENTS-98), pp. 362-368. ACM Press.
68. Muscettola, N., Nayak, P., Pell, B., & Williams, B. (1998b). Remote Agent: to boldly go where no AI system has gone before. Artificial Intelligence, 103(1-2), 5-47.
69. Myers, K. (1996). A procedural knowledge approach to task-level control. In Drabble, B. (Ed.), Proceedings of the Third International Conference on AI Planning Systems, pp. 158-165, Edinburgh, GB. AAAI Press.
70. Parr, R. (1998). Flexible decomposition algorithms for weakly coupled Markov decision problems. In Cooper, G. F., & Moral, S. (Eds.), Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 422-430, San Francisco. Morgan Kaufmann.
71. Parr, R., & Russell, S. (1998). Reinforcement learning with hierarchies of machines. In Jordan, M. I., Kearns, M. J., & Solla, S. A. (Eds.), Advances in Neural Information Processing Systems, Vol. 10. MIT Press.
72. Pell, B., Gat, E., Keesing, R., Muscettola, N., & Smith, B. (1997). Robust periodic planning and execution for autonomous spacecraft. In Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 1234-1239, San Francisco. Morgan Kaufmann.
73. Pinto, J. (1994). Temporal Reasoning in the Situation Calculus. Ph.D. thesis, Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
74. Reiter, R. (1996). Natural actions, concurrency and continuous time in the situation calculus. In Proceedings of the Fifth International Conference on Principles of Knowledge Representation and Reasoning (KR-96), pp. 2-13.
75. Rohanimanesh, K., & Mahadevan, S. (2001). Decision-theoretic planning with concurrent temporally extended actions. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 472-479.
76. Schmitt, T., Hanek, R., Beetz, M., Buck, S., & Radig, B. (2002). Cooperative probabilistic state estimation for vision-based autonomous mobile robots. IEEE Transactions on Robotics and Automation, 18(5), 670-684.
77. Simmons, R., Goodwin, R., Haigh, K., Koenig, S., & O'Sullivan, J. (1997). A modular architecture for office delivery robots. In Proceedings of the First International Conference on Autonomous Agents, pp. 245-252.
78. Smith, D., Frank, J., & Jonsson, A. (2000). Bridging the gap between planning and scheduling. The Knowledge Engineering Review, 15(1), 47-83.
79. Sutton, R., Precup, D., & Singh, S. (1999). Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence 112(1-2), 181-211.
80. Thrun, S. (2000). Monte Carlo POMDPs. In Advances in Neural Information Processing Systems 12, pp. 1064-1070. MIT Press.
81. Thrun, S., Beetz, M., Bennewitz, M., Cremers, A., Dellaert, F., Fox, D., Hähnel, D., Rosenberg, C., Roy, N., Schulte, J., & Schulz, D. (2000). Probabilistic algorithms and the interactive museum tour-guide robot Minerva. International Journal of Robotics Research, 19(11), 972-999.
82. Thrun, S., Bücken, A., Burgard, W., Fox, D., Fröhlinghaus, T., Hennig, D., Hofmann, T., Krell, M., & Schmidt, T. (1998). Map learning and high-speed navigation in RHINO. In Kortenkamp, D., Bonasso, R., & Murphy, R. (Eds.), AI-based Mobile Robots: Case studies of successful robot systems, pp. 21 - 52. MIT Press.
83. Williamson, M., & Hanks, S. (1994). Utility-directed planning. In Proceedings of the Twelfth National Conference on Artificial Intelligence, p. 1498, Seattle, WA.