Formalizing the PRODIGY planning algorithm

The PRODIGY project is primarily concerned with the integration of planning and learning. Members of the PRODIGY research group have developed many learning algorithms for improving planning efficiency and plan quality, and for automatically acquiring knowledge about the properties of planning domains. The details of the PRODIGY planning algorithm, however, have not been described in the literature. We present a formal description of the planning algorithm used in the current version of the PRODIGY system. The algorithm is based on an interesting combination of backward-chaining planning with simulation of plan execution. The backward-chainer selects goal-relevant operators and then the planner simulates the application of these operators to the current state of the world. The system can use different backward-chaining algorithms, two of which are presented in the paper.

[1]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[2]  Yolanda Gil,et al.  Applying a General-Purpose Planning and Learning Architecture to Process Planning * , 1994 .

[3]  Xuemei Wang,et al.  Learning Planning Operators by Observation and Practice , 1994, AIPS.

[4]  Austin Tate,et al.  Generating Project Networks , 1977, IJCAI.

[5]  John C. Reynolds,et al.  School of Computer Science , 1992 .

[6]  Eugene Fink,et al.  Integrating planning and learning: the PRODIGY architecture , 1995, J. Exp. Theor. Artif. Intell..

[7]  Steven Minton,et al.  Learning search control knowledge , 1988 .

[8]  Yolanda Gil,et al.  Acquiring domain knowledge for planning by experimentation , 1992 .

[9]  Craig A. Knoblock Automatically Generating Abstractions for Planning , 1994, Artif. Intell..

[10]  Manuela M. Veloso,et al.  Learning strategy knowledge incrementally , 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.

[11]  Manuela M. Veloso,et al.  Planning and Learning by Analogical Reasoning , 1994, Lecture Notes in Computer Science.

[12]  David A. McAllester,et al.  Systematic Nonlinear Planning , 1991, AAAI.

[13]  Manuela Veloso Nonlinear problem solving using intelligent casual-commitment , 1989 .

[14]  Manuela M. Veloso,et al.  Linkability: Examining Causal Link Commitments in Partial-order Planning , 1994, AIPS.

[15]  David Chapman,et al.  Planning for Conjunctive Goals , 1987, Artif. Intell..

[16]  Manuela M. Veloso,et al.  The Need for Different Domain-independent Heuristics , 1994, AIPS.

[17]  Oren Etzioni,et al.  PRODIGY4.0: The Manual and Tutorial , 1992 .

[18]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.