Abstract
This paper presents Greedy Regression Tables (GRT), a new domain independent heuristic for STRIPS worlds. The heuristic can be used to guide the search process of any state-space planner, estimating the distance between each intermediate state and the goals. At the beginning of the problem solving process a table is created, the records of which contain the ground facts of the domain, among with estimates for their distances from the goals. Additionally, the records contain information about interactions that occur while trying to achieve different ground facts simultaneously. During the search process, the heuristic, using this table, extracts quite accurate estimates for the distances between intermediate states and the goals. A simple best-first search planner that uses this heuristic has been implemented in C++ and has been tested on several “classical” problem instances taken from the bibliography and on some new taken from the AIPS-98 planning competition. Our planner has proved to be faster in all of the cases, finding also in most (but not all) of the cases shorter solutions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barett, A., Weld, D.S.: Partial order planning: Evaluating possible efficiency gains. J. Artificial Intelligence 67, 71–112 (1994)
Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. In: 14th International Joint Conference on Artificial Intelligence (IJCAI 1995), Montreal, Canada, pp. 636–642 (1995)
Bonet, B., Loerincs, G., Geffner, H.: A robust and fast action selection mechanism for planning. In: 14th International Conference of the American Association of Artificial Intelligence (AAAI 1997), Providence, Rhode Island, pp. 714–719 (1997)
Bonet, B., Geffner, H.: HSP: Heuristic Search Planner. Entry at the Artificial Intelligence Planning Systems (AIPS 1998) Planning Competition, Pittsburgh (1998)
Fikes, R.E., Nilsson, N.J.: Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2, 189–208 (1971)
Gupta, N., Nau, D.S.: On the complexity of blocks world planning. Artificial Intelligence 56(2-3), 223–254 (1992)
Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. on Systems Science and Cybernetics 4(2), 100–107 (1968)
Kautz, H., Selman, B.: Pushing the envelope: Planning, propositional logic and stochastic search. In: 13th International Conference of the American Association of Artificial Intelligence (AAAI 1996), Portland, Oregon, pp. 1194–1201 (1996)
Kautz, H., Selman, B.: BLACKBOX: A New Approach to the Application of Theorem Proving to Problem Solving. In: Working notes of the Workshop on Planning as Combinatorial Search, held in conjunction with Artificial Intelligence Planning Systems (AIPS 1998), Pittsburgh (1998)
Minton, S., Bresina, J., Drummond, M.: Total-order and partial-order planning: A comparative analysis. J. of Artificial Intelligence Research 2, 227–261 (1994)
Pearl, J.: Heuristics: intelligent strategies for computer problem solving. Addison- Wesley, MA (1984)
Slaney, J., Thiebaux, S.: Linear-time near-optimal planning in the blocks world. In: 13th International Conference of the American Association of Artificial Intelligence (AAAI 1996), Portland, Oregon (1996)
Veloso, M.: Learning by Analogical Reasoning in General Problem Solving. PhD Dissertation, Computer Science Dept., CMU Tech. Report, CMU-CS-92-174 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Refanidis, I., Vlahavas, I. (2000). GRT: A Domain Independent Heuristic for STRIPS Worlds Based on Greedy Regression Tables. In: Biundo, S., Fox, M. (eds) Recent Advances in AI Planning. ECP 1999. Lecture Notes in Computer Science(), vol 1809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720246_27
Download citation
DOI: https://doi.org/10.1007/10720246_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67866-3
Online ISBN: 978-3-540-44657-6
eBook Packages: Springer Book Archive