Comparing real-time and incremental heuristic search for real-time situated agents

Autonomous Agents and Multi-Agent Systems

Volumn 18, Issue 3, 2009, Pages 313-341

  Koenig, Sven ^a   Sun, Xiaoxun ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Agents; Incremental search; Real time search; Time constraints

Indexed keywords

EID: 59849106768     PISSN: 13872532     EISSN: 15737454     Source Type: Journal    
DOI: 10.1007/s10458-008-9061-x     Document Type: Article

References (43)

1. A. Barto S. Bradtke S. Singh 1995 Learning to act using real-time dynamic programming Artificial Intelligence 73 1 81 138
2. Bjornsson, M., Enzenberger, M., Holte, R., Schaeffer, J., & Yap, P. (2003). Comparison of different abstractions for pathfinding on maps. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1511-1512).
3. Bonet, B., & Geffner, H. (2000). Planning with incomplete information as heuristic search in belief space. In Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling (pp. 52-61).
4. Bonet, B., Loerincs, G., & Geffner, H. (1997). A robust and fast action selection mechanism. In Proceedings of the National Conference on Artificial Intelligence (pp. 714-719).
5. Bulitko, V. (2003). Lookahead pathologies and meta-level control in real-time heuristic search. In Proceedings of the Euromicro Conference on Real-Time Systems (pp. 13-16).
6. Bulitko, V., Bjornsson, Y., Luvstrek, M., Schaeffer, J., & Sigmundarson, S. (2007). Dynamic control in path-planning with real-time heuristic search. In Proceedings of the International Conference on Automated Planning and Scheduling (pp. 49-56).
7. V. Bulitko G. Lee 2006 Learning in real-time search: A unifying framework Journal of Artificial Intelligence Research 25 119 157
8. E. Dijkstra 1959 A note on two problems in connexion with graphs Numerische Mathematik 1 269 271
9. Edelkamp, S. (1998). Updating shortest paths. In Proceedings of the European Conference on Artificial Intelligence (pp. 655-659).
10. Furcy, D., & Koenig, S. (2000). Speeding up the convergence of real-time search. In Proceedings of the National Conference on Artificial Intelligence (pp. 891-897).
11. Goldenberg, M., Kovarksy, A., Wu, X., & Schaeffer, J. (2003). Multiple agents moving target search. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1538-1538).
12. Hart, P., Nilsson, N., & Raphael, N. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, SSC4(2), 100-107.
13. P. Hart N. Nilsson B. Raphael 1972 Correction to 'a formal basis for the heuristic determination of minimum cost paths' SIGART Newsletter 37 28 29
14. Hebert, M., McLachlan, R., & Chang, P. (1999). Experiments with driving modes for urban robots. In Proceedings of the SPIE Mobile Robots.
15. R. Holte T. Mkadmi R. Zimmer A. MacDonald 1996 Speeding up problem solving by abstraction: A graph oriented approach Artificial Intelligence 85 1-2 321 361
16. Ishida, T. (1992). Moving target search with intelligence. In Proceedings of the National Conference on Artificial Intelligence (pp. 525-532).
17. Ishida, T. (1997). Real-Time search for learning autonomous agents. Kluwer Academic Publishers.
18. S. Koenig 2001 Agent-centered search Artificial Intelligence Magazine 22 4 109 131
19. S. Koenig 2001 Minimax real-time heuristic search Artificial Intelligence 129 165 197
20. Koenig, S. (2004). A comparison of fast search methods for real-time situated agents. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (pp. 864-871).
21. Koenig, S., & Likhachev, M. (2002). D*Lite. In Proceedings of the National Conference on Artificial Intelligence (pp. 476-483).
22. S. Koenig M. Likhachev 2005 Fast replanning for navigation in unknown terrain Transactions on Robotics 21 3 354 363
23. Koenig, S., & Likhachev, M. (2006). A new principle for incremental heuristic search: Theoretical results. In Proceedings of the International Conference on Autonomous Planning and Scheduling (pp. 402-405).
24. S. Koenig M. Likhachev D. Furcy 2004 Lifelong Planning A*Artificial Intelligence Journal 155 1-2 93 146
25. S. Koenig M. Likhachev Y. Liu D. Furcy 2004 Incremental heuristic search in Artificial Intelligence Artificial Intelligence Magazine 25 2 99 112
26. Koenig, S., & Simmons, R. G. (1996). Easy and hard testbeds for real-time search algorithms. In Proceedings of the National Conference on Artificial Intelligence (pp. 279-285).
27. Koenig, S., & Szymanski, B. (1999). Value-update rules for real-time search. In Proceedings of the National Conference on Artificial Intelligence (pp. 718-724).
28. S. Koenig C. Tovey Y. Smirnov 2003 Performance bounds for planning in unknown terrain Artificial Intelligence 147 253 279
29. R. Korf 1990 Real-time heuristic search Artificial Intelligence 42 2-3 189 211
30. R. Korf 1993 Linear-space best-first search Artificial Intelligence 62 1 41 78
31. Mudgal, A., Tovey, C., & Koenig, S. (2004). Analysis of greedy robot-navigation methods. In Proceedings of the Conference on Artificial Intelligence and Mathematics.
32. Pearl, J. (1985). Heuristics: Intelligent search strategies for computer problem solving. Addison-Wesley.
33. Pemberton, J., & Korf, R. (1992). Making locally optimal decisions on graphs with cycles. Technical Report 920004. Los Angeles, CA: Computer Science Department, University of California at Los Angeles.
34. Russell, S., & Wefald, E. (1991). Do the right thing-Studies in limited rationality. MIT Press.
35. Shue, L., Li, S., & Zamani, R. (2001). An intelligent heuristic algorithm for project scheduling problems. In Proceedings of the Annual Meeting of the Decision Sciences Institute.
36. Shue, L., & Zamani, R. (1993). An admissible heuristic search algorithm. In Proceedings of the International Symposium on Methodologies for Intelligent Systems (pp. 69-75).
37. Stentz, A. (1995). The focussed D*algorithm for real-time replanning. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1652-1659).
38. Sun, X., & Koenig, S. (2007). The Fringe-Saving A*search algorithm-A feasibility study. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 2391-2397).
39. J. Svennebring S. Koenig 2004 Building terrain-covering ant robots Autonomous Robots 16 3 313 332
40. Thayer, S., Digney, B., Diaz, M., Stentz, A., Nabbe, B., & Hebert, M. (2000). Distributed robotic mapping of extreme environments. In Proceedings of the SPIE: Mobile Robots XV and Telemanipulator and Telepresence Technologies VII (Vol. 4195, pp. 84-95).
41. K. Trovato 1990 Differential A*: 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 2 245 268
42. I. Wagner M. Lindenbaum A. Bruckstein 1999 Distributed covering by ant-robots using evaporating traces IEEE Transactions on Robotics and Automation 15 5 918 933
43. V. Yanovski I. Wagner A. Bruckstein 2003 A distributed ant algorithm for efficiently patrolling a network Algorithmica 37 165 186