Anytime search in dynamic graphs

Artificial Intelligence

Volumn 172, Issue 14, 2008, Pages 1613-1643

  Likhachev, Maxim ^a   Ferguson, Dave ^b,c   Gordon, Geoff ^c   Stentz, Anthony ^c   Thrun, Sebastian ^d  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b INTEL RESEARCH   (United States)

^c Carnegie Mellon University   (United States)

^d Stanford University   (United States)

Author keywords

A*; Anytime planning; Anytime search; Heuristic search; Incremental search; Planning; Replanning; Search

Indexed keywords

PLANNING; STABILIZERS (AGENTS);

DYNAMIC GRAPHS; OPTIMAL PLANNING; REAL WORLD; SPEED UPS;

DYNAMIC MODELS;

EID: 47649108079     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2007.11.009     Document Type: Article

References (68)

1. Hart P.E., Nilsson N.J., and Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems, Science, and Cybernetics SSC 4 2 (1968) 100-107
2. Dijkstra E.W. A note on two problems in connexion with graphs. Numerische Mathematik 1 (1959) 269-271
3. *. Journal of the Association for Computing Machinery 32 3 (1985) 505-536
4. Zilberstein S., and Russell S. Approximate reasoning using anytime algorithms. Imprecise and Approximate Computation (1995), Kluwer Academic Publishers
5. T.L. Dean, M. Boddy, An analysis of time-dependent planning, in: Proceedings of the National Conference on Artificial Intelligence (AAAI), 1988
6. H. Moravec, Certainty grids for mobile robots, in: Proceedings of the NASA/JPL Space Telerobotics Workshop, 1987
7. S. Koenig, Y. Smirnov, Sensor-based planning with the freespace assumption, in: Proceedings of the International Conference on Robotics and Automation (ICRA), 1997
8. J. Ambite, C. Knoblock, Planning by rewriting: Efficiently generating high-quality plans, in: Proceedings of the National Conference on Artificial Intelligence (AAAI), 1997
9. S. Zilberstein, S. Russell, Anytime sensing, planning and action: A practical model for robot control, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1993, pp. 1402-1407
10. N. Hawes, An anytime planning agent for computer game worlds, in: Proceedings of the Workshop on Agents in Computer Games at the 3rd International Conference on Computers and Games (CG'02), 2002
11. Pai D.K., and Reissell L.-M. Multiresolution rough terrain motion planning. IEEE Transactions on Robotics and Automation 14 1 (1998) 19-33
12. H. Prendinger, M. Ishizuka, APS, a prolog-based anytime planning system, in: Proceedings 11th International Conference on Applications of Prolog (INAP), 1998
13. Korf R.E. Real-time heuristic search. Artificial Intelligence 42 2-3 (1990) 189-211
14. Dasgupta P., Chakrabarti P., and DeSarkar S. Agent searching in a tree and the optimality of iterative deepening. Artificial Intelligence 71 (1994) 195-208
15. S. Koenig, Chapter 2 of goal-directed acting with incomplete information: Acting with agent-centered search, Ph.D. thesis, Carnegie Mellon University, 1997
16. *, in: Proceedings of the National Conference on Artificial Intelligence (AAAI), 2002, Student abstract
17. Hansen E.A., and Zhou R. Anytime heuristic search. Journal of Artificial Intelligence Research (JAIR) 28 (2007) 267-297
18. J.G. Gaschnig, Performance measurement and analysis of certain search algorithms, Ph.D. thesis, Carnegie Mellon University, 1979
19. Bonet B., and Geffner H. Planning as heuristic search. Artificial Intelligence 129 1-2 (2001) 5-33
20. Korf R.E. Linear-space best-first search. Artificial Intelligence 62 (1993) 41-78
21. * speed optimizations. In: DeLoura M. (Ed). Game Programming Gems (2000), Charles River Media, Rockland, MA 272-287
22. S. Edelkamp, Planning with pattern databases, in: Proceedings of the European Conference on Planning (ECP), 2001
23. Bagchi A., and Srimani P.K. Weighted heuristic search in networks. Journal of Algorithms 6 (1985) 550-576
24. * when heuristics overestimate. Artificial Intelligence 34 (1988) 97-113
25. Davis H.W., Bramanti-Gregor A., and Wang J. The advantages of using depth and breadth components in heuristic search. In: Ras Z.W., and Saitta L. (Eds). Methodologies for Intelligent Systems, 3 (1988), North-Holland, New York 19-28
26. R. Zhou, E.A. Hansen, Beam-stack search: Integrating backtracking with beam search, in: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2005, pp. 90-98
27. D. Furcy, Chapter 5 of speeding up the convergence of online heuristic search and scaling up offline heuristic search, Ph.D. thesis, Georgia Institute of Technology, 2004
28. W. Zhang, Complete anytime beam search, in: Proceedings of the National Conference on Artificial Intelligence (AAAI), 1998, pp. 425-430
29. Kumar V. Branch-and-bound search. In: Shapiro S.C. (Ed). Encyclopedia of Artificial Intelligence (1992), Wiley-Interscience, New York 1468-1472
30. R. Simmons, A theory of debugging plans and interpretations, in: Proceedings of the National Conference on Artificial Intelligence (AAAI), 1988, pp. 94-99
31. A. Gerevini, I. Serina, Fast plan adaptation through planning graphs: Local and systematic search techniques, in: Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling (AIPS), 2000, pp. 112-121
32. Koehler J. Flexible plan reuse in a formal framework. In: Bäckström C., and Sandewall E. (Eds). Current Trends in AI Planning (1994), IOS Press 171-184
33. Veloso M. Planning and Learning by Analogical Reasoning (1994), Springer
34. Alterman R. Adaptive planning. Cognitive Science 12 3 (1988) 393-421
35. Kambhampati S., and Hendler J. A validation-structure-based theory of plan modification and reuse. Artificial Intelligence 55 (1992) 193-258
36. Hanks S., and Weld D. A domain-independent algorithm for plan adaptation. Journal of Artificial Intelligence Research 2 (1995) 319-360
37. S. Edelkamp, Updating shortest paths, in: Proceedings of the European Conference on Artificial Intelligence, 1998
38. *: An adaptive search method illustrated with robot path planning for moving obstacles and goals, and an uncertain environment, Journal of Pattern Recognition and Artificial Intelligence 4 (2)
39. Podsedkowski L., Nowakowski J., Idzikowski M., and Vizvary I. A new solution for path planning in partially known or unknown environment for nonholonomic mobile robots. Robotics and Autonomous Systems 34 (2001) 145-152
40. * algorithm for real-time replanning, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1995
41. *. In: Dietterich T.G., Becker S., and Ghahramani Z. (Eds). Advances in Neural Information Processing Systems (NIPS) 14 (2002), MIT Press, Cambridge, MA
42. Ausiello G., Italiano G., Marchetti-Spaccamela A., and Nanni U. Incremental algorithms for minimal length paths. Journal of Algorithms 12 4 (1991) 615-638
43. Even S., and Gazit H. Updating distances in dynamic graphs. Methods of Operations Research 49 (1985) 371-387
44. D. Frigioni, A. Marchetti-Spaccamela, U. Nanni, Fully dynamic output bounded single source shortest path problem, in: Proceedings of the Symposium on Discrete Algorithms, 1996
45. Goto S., and Sangiovanni-Vincentelli A. A new shortest path updating algorithm. Networks 8 4 (1978) 341-372
46. Lin C., and Chang R. On the dynamic shortest path problem. Journal of Information Processing 13 4 (1990) 470-476
47. Ramalingam G., and Reps T. An incremental algorithm for a generalization of the shortest-path problem. Journal of Algorithms 21 (1996) 267-305
48. H. Rohnert, A dynamization of the all pairs least cost path problem, in: Proceedings of the Symposium on Theoretical Aspects of Computer Science, 1985, pp. 279-286
49. Ahuja R., Pallottino S., and Scutellà M.G. Dynamic shortest paths minimizing travel times and costs. Networks 41 (2003) 197-205
50. Frigioni D., Marchetti-Spaccamela A., and Nanni U. Fully dynamic shortest paths in digraphs with arbitrary arc weights. Journal of Algorithms 49 1 (2003) 86-113
51. S. Chien, R. Knight, A. Stechert, R. Sherwood, G. Rabideau, Using iterative repair to improve the responsiveness of planning and scheduling, in: Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling (AIPS), 2000
52. G.J. Ferrer, Anytime replanning using local subplan replacement, Ph.D. thesis, University of Virginia, 2002
53. Pearl J. Heuristics: Intelligent Search Strategies for Computer Problem Solving (1984), Addison-Wesley
54. Pohl I. First results on the effect of error in heuristic search. Machine Intelligence 5 (1970) 219-236
55. M. Likhachev, Search-based planning for large dynamic environments, Ph.D. thesis, Carnegie Mellon University, 2005
56. *, in: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2005, pp. 99-108
57. * lite, in: Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI), 2002
58. *: Formal analysis, Tech. Rep. CMU-CS-03-148, Carnegie Mellon University, Pittsburgh, PA, 2003
59. *: An anytime, replanning algorithm, in: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2005, pp. 262-271
60. *: The proofs, Tech. Rep. CMU-RI-TR-05-12, Carnegie Mellon University, Pittsburgh, PA, 2005
61. D. Haehnel, Personal communication, 2003
62. Culberson J.C., and Schaeffer J. Pattern databases. Computational Intelligence 14 3 (1998) 318-334
63. S. Koenig, R. Simmons, Easy and hard testbeds for real-time search algorithms, in: Proceedings of the National Conference on Artificial Intelligence (AAAI), 1996
64. Korf R.E. Depth-first iterative-deepening: an optimal admissible tree search. Artificial Intelligence 27 1 (1985) 97-109
65. R.E. Korf, W. Zhang, Divide-and-conquer frontier search applied to optimal sequence alignment, in: Proceedings of the National Conference on Artificial Intelligence (AAAI), 2000, pp. 910-916
66. *: Space-efficient heuristic search in partially ordered graphs, in: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2003, p. 427
67. * algorithm for efficient path replanning, in: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2005
68. D. Ferguson, M. Likhachev, A. Stentz, A guide to heuristic-based path planning, in: Proceedings of the Workshop on Planning under Uncertainty for Autonomous Systems at the International Conference on Automated Planning and Scheduling (ICAPS), 2005