Collaborating Authors

STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving


Reprinted in Readings in Planning, edited by J. Allen, J. Hendler, and A. Tate, Morgan Kaufmann Publishers, San Mateo, California, 1990. Also Reprinted in Computation and Intelligence: Collected Readings, edited by George F. Luger, AAAI Press, 1995. See also: Artificial Intelligence, Volume 2, Issues 3–4, Winter 1971, Pages 189–208 In IJCAI-71: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE. British Computer Society, London.. Revised version in Artificial Intelligence, 2(3), pp 189-208.

Learning and executing generalized robot plans


"In this paper we describe some major new additions to the STRIPS robot problem-solving system. The first addition is a process for generalizing a plan produced by STRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters.The generalized plan, stored in a convenient format called a triangle table, has two important functions. The more obvious function is as a single macro action that can be used by STRIPS—either in whole or in part—during the solution of a subsequent problem. Perhaps less obviously, the generalized plan also plays a central part in the process that monitors the real-world execution of a plan, and allows the robot to react "intelligently" to unexpected consequences of actions.We conclude with a discussion of experiments with the system on several example problems."Artificial Intelligence 3:251-288

Formally Verified SAT-Based AI Planning Artificial Intelligence

In the realm of planning, this approach was pioneered by Howey, Long, and Fox As witnessed by the different planning competitions (Long who developed VAL (Howey, Long, and Fox 2004) that, 2000; Coles et al. 2012; Vallati et al. 2015), planning algorithms given a planning problem and potential solution, certifies and systems are becoming more and more scalable that the solution actually solves the given problem. Also, and efficient, which makes them suited for more realistic certifying unsolvability for planning was tackled by Eriksson, applications. Given that many applications of planning Röger, and Helmert (2017) who provided unsolvability are safety-critical, increasing the trustworthiness of certificates and checkers for state-space search algorithms planning algorithms and systems--i.e. the likelihood that and by Eriksson and Helmert (2020) for property they compute correct results--could be instrumental in their directed SATbased planning.

Planning in a Hierarchy of Abstraction Spaces


A problem domain can be represented as a hierarchy of abstraction spaces in which successively finer levels of detail are introduced. The problem solver ABSTRIPS, a modification of STRIPS, can define an abstraction space hierarchy from the STRIPS representation of a problem domain, and it can utilize the hierarchy in solving problems. Examples of the system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides. Then some further Implications of the hierarchical planning approach are explored.Later journal article in Artificial Intelligence 5:115-135 (1974). Available for a fee. In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.