Abstract
Consider the situation where an intelligent agent accepts as input a complete plan, i.e., a sequence of states (or operators) that should be followed in order to achieve a goal. For some reason, the given plan cannot be implemented by the agent, who then goes about trying to find an alternative plan that is as close as possible to the original. To achieve this, a search algorithm that will find similar alternative plans is required, as well as some sort of comparison function that will determine which alternative plan is closest to the original. In this paper, we define a number of distance metrics between plans, and characterize these functions and their respective attributes and advantages. We then develop a general algorithm based on best-first search that helps an agent efficiently find the most suitable alternative plan. We also propose a number of heuristics for the cost function of this best-first search algorithm. To explore the generality of our idea, we provide three different problem domains where our approach is applicable: physical roadmap path finding, the blocks world, and task scheduling. Experimental results on these various domains support the efficiency of our algorithm for finding close alternative plans.
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References
Ambros-Ingerson, J., & Steel, S. (1998). Integrating planning, execution and monitoring. In Proceedings of AAAI-88 (pp. 735–740). St. Paul, Minnesota.
Arkin, E., Chew, L. P., Huttenlovher, D. P., Kedem, K., & Mitcjell, J. S. B. (1990). An efficiently computable metric for comparing polygonal shapes. In Proceedings of the first ACM-SIAM Symposium on Discrete Algorithms (pp. 209–216).
Atallah M.J. (1983). A linear time algorithm for the Hausdorff distance between convex polygons. Information Processing Letters 17, 207–209
Barber, C. B., Dobkin, D. P., & Huhdanpaa, H. (1993). The Quickhull algorithm for convex hull. Geometry Center Technical Report GCG53, University of Minnesota.
Brock, O., & Oussama, K. (2000). Real-time replanning in high-dimensional configuration spaces using sets of homotopic paths. In Proceedings of the IEEE international conference on robotics and automation (pp. 550–555) San Francisco, USA.
Ephrati E., Rosenschein J.S. (1993). Planning to please: Following another agent’s intended plan. Group Decision and Negotiation 2(3): 219–235
Felner, A. (1995). Searching for an alternative plan. Master’s thesis, Department of Computer Science, The Hebrew University, Jerusalem, Israel.
Felner, A., Pomeransky, A., & Rosenschein, J. S. (2003). Searching for an alternative plan. In Proceedings of the second international joint conference on autonomous agents and multi-agent systems (pp. 33–40). Melbourne, Australia.
Haigh, K. Z., & Veloso, M. (1995). Route planning by analogy. In Proceedings of the international conference on case-based reasoning.
Hart P.E., Nilsson N.J., Raphael B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics SSC-4(2): 100–107
Hausdorff F. (1914). Grundzuege der Mengenlehre. Viet, Leipzig
Jain A., Meeran, S. (1998). A state-of-the-art review of job-shop scheduling techniques.
Jensen, R. M., & Veloso, M. M. (1999). OBDD-based universal planning: Specifying and solving planning problems for synchronized agents in non-deterministic domains. Artificial Intelligence Today, Recent Trends and Developments, p 212–248.
Karger D., Stein C., Wein J. (1997). Scheduling algorithms. In: Atallah M.J.(ed) Handbook of algorithms and theory of computation. CRC Press
Koenig, S., & Likhachev, M. (2002). D * lite. In Proceedings of the eighteenth national conference on artificial intelligence (pp. 476–483). Edmonton, Canada.
Koenig, S., & Likhachev, M. (2002). Incremental A *. In Advances in neural information processing systems 14 (NIPS). Cambridge, MA: MIT Press.
Leung, J., & Anderson, J. H. (2004). Handbook of scheduling. CRC Press.
Li, W., & Zhang, M. (1999). Distributed task plan: A model for designing autonomous mobile agents. In Proceedings of the international conference on artificial intelligence (pp. 336–342). Las-Vegas.
Liu, B. (1996). Intelligent route finding: Combining knowledge, cases and an efficient search algorithm. In Proceedings of ECAI-96, (pp. 380–384). Budapest, Hungary.
Mannila H. (1985). Measures of presortedness and optimal sorting algorithms. IEEE Transactions on Computers 34(4): 318–325
McMaster R.B. (1986). A statistical analysis of mathematical measures for linear simplification. The American Cartographer 13(2): 103–117
McMaster R.B. (1989). The integration of simplification and smoothing algorithms in line generalization. Cartographica 26, 101–121
McMaster, R. B. (2001). Measurement in generalization. In Proceedings of the twentieth international cartography conference. (pp. 20–82).
Myers, K. L., & Lee, Thomas J. (1999). Generating qualitatively different plans through metatheoretic biases. In Proceedings of the sixteenth national conference on artificial intelligence. (pp. 570–576). Menlo Park, CA, USA: American Association for Artificial Intelligence.
Nebel B., Koehler J. (1995). Plan reuse versus plan generation. Artificial Intelligence 76, 427–454
Okabe A., Boots B., Sugihara K. Spatial tessellations, concepts, and applications of voronoi diagrams. Chichester, UK: Wiley
Russell, S., & Norvig, P. (2005). Artificial intelligence, a modern approach, 2nd edn. Prentice Hall.
Sgall J. (1997). Line scheduling—a survey, on-line algorithms. Lecture Notes in Computer Science. Berlin, Springer-Verlag
Sorensen, K. (2003). Distance measures based on the edit distance for permutation type representations. In Proceedings of the workshop on analysis and design of representations and operators (pp. 29–35). Chicago, USA.
Sorensen, K., Reimann, M., & Prins, C. (1994). Permutation distance measures for memetic algorithms with population management. In Proceedings of MIC sixth metaheuristics international conference. Vienna, Austria.
Stentz, A. (1994). Optimal and efficient path planning for partially-known environments. In Proceedings of ICRA (pp. 3310–3317).
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A preliminary version of this paper appeared in the International Joint Conference on Autonomous Agents and Multiagent Systems (8).
An erratum to this article can be found at http://dx.doi.org/10.1007/s10458-007-9012-y
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Felner, A., Stern, R., Rosenschein, J.S. et al. Searching for close alternative plans. Auton Agent Multi-Agent Syst 14, 211–237 (2007). https://doi.org/10.1007/s10458-006-9006-1
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DOI: https://doi.org/10.1007/s10458-006-9006-1