Evaluating new options in the context of existing plans

Abstract This paper contributes to the foundations of a theory of rational choice for artificial agents in dynamic environments. Our work is developed within a theoretical framework, originally due to Bratman, that models resource-bounded agents as operating against the background of some current set of intentions, which helps to frame their subsequent reasoning. In contrast to the standard theory of rational choice, where options are evaluated in isolation, we therefore provide an analysis of situations in which the options presented to an agent are evaluated against a background context provided by the agent's current plans—commitments to future activities, which may themselves be only partially specified. The interactions between the new options and the background context can complicate the task of evaluating the option, rendering it either more or less desirable in context than it would have been in isolation.

[1]  Martha E. Pollack,et al.  Merging Plans with Quantitative Temporal Constraints, Temporally Extended Actions, and Conditional Branches , 2000, AIPS.

[2]  Avrim Blum,et al.  Fast Planning Through Planning Graph Analysis , 1995, IJCAI.

[3]  Malik Ghallab,et al.  Representation and Control in IxTeT, a Temporal Planner , 1994, AIPS.

[4]  David J. Israel,et al.  Plans and resource‐bounded practical reasoning , 1988, Comput. Intell..

[5]  Steve Hanks,et al.  Optimal Planning with a Goal-directed Utility Model , 1994, AIPS.

[6]  Daniel S. Weld,et al.  UCPOP: A Sound, Complete, Partial Order Planner for ADL , 1992, KR.

[7]  P. Haddawy,et al.  Eecient Decision-theoretic Planning: Techniques and Empirical Analysis , 1995 .

[8]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[9]  Ralph P. Grimaldi,et al.  Discrete and combinatorial mathematics , 1985 .

[10]  Bart Selman,et al.  The Role of Domain-Specific Knowledge in the Planning as Satisfiability Framework , 1998, AIPS.

[11]  Stephen F. Smith,et al.  Profile-Based Algorithms to Solve Multiple Capacitated Metric Scheduling Problems , 1998, AIPS.

[12]  Bart Selman,et al.  Pushing the Envelope: Planning, Propositional Logic and Stochastic Search , 1996, AAAI/IAAI, Vol. 2.

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

[14]  Subbarao Kambhampati,et al.  Planning as Refinement Search: A Unified Framework for Evaluating Design Tradeoffs in Partial-Order Planning , 1995, Artif. Intell..

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

[16]  Peter Haddawy,et al.  Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis , 1995, UAI.

[17]  Martha E. Pollack,et al.  The Uses of Plans , 1992, Artif. Intell..

[18]  B. Selman The Role of Domain-speciic Knowledge in the Planning as Satissability Framework , 1998 .

[19]  Reid G. Simmons,et al.  Search Control of Plan Generation in Decision-Theoretic Planners , 1998, AIPS.

[20]  Daniel S. Weld An Introduction to Least Commitment Planning , 1994, AI Mag..

[21]  Michael E. Bratman,et al.  Intention, Plans, and Practical Reason , 1991 .

[22]  Howard Raiffa,et al.  Decision analysis: introductory lectures on choices under uncertainty. 1968. , 1969, M.D.Computing.

[23]  Peter Haddawy,et al.  E cient Decision-Theoretic Planning : Techniques and Empirical Analysis , 1995 .

[24]  Henry Kautz,et al.  Pushing the envelope: planning , 1996 .

[25]  Stephen F. Smith,et al.  Generating Feasible Schedules under Complex Metric Constraints , 1994, AAAI.

[26]  Qiang Yang,et al.  Intelligent planning - a decomposition and abstraction based approach , 1997, Artificial intelligence.