Decision-Theoretic Planning: Structural Assumptions and Computational Leverage

TLDR

Planning under uncertainty is a central problem in automated sequential decision making, addressed across AI planning, decision analysis, operations research, control theory, and economics, and many such problems can be modeled as Markov decision processes that exhibit structure in reward/value functions, state transition/observation functions, and feature relationships. This paper surveys MDP-related methods and representations for classical and decision‑theoretic planning, synthesizing them into a unifying framework that eases the computational burden of constructing policies or plans. The authors describe structural properties of MDPs that can be exploited to construct optimal or approximate policies, focusing on abstraction, aggregation, and decomposition techniques based on AI‑style representations. Specialized representations and algorithms that exploit structural properties can achieve computational leverage, and AI techniques using structured, intensional representations exemplify this advantage.

Abstract

Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives adopted in these areas often differ in substantial ways, many planning problems of interest to researchers in these fields can be modeled as Markov decision processes (MDPs) and analyzed using the techniques of decision theory. This paper presents an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI. It also describes structural properties of MDPs that, when exhibited by particular classes of problems, can be exploited in the construction of optimal or approximately optimal policies or plans. Planning problems commonly possess structure in the reward and value functions used to describe performance criteria, in the functions used to describe state transitions and observations, and in the relationships among features used to describe states, actions, rewards, and observations. Specialized representations, and algorithms employing these representations, can achieve computational leverage by exploiting these various forms of structure. Certain AI techniques -- in particular those based on the use of structured, intensional representations -- can be viewed in this way. This paper surveys several types of representations for both classical and decision-theoretic planning problems, and planning algorithms that exploit these representations in a number of different ways to ease the computational burden of constructing policies or plans. It focuses primarily on abstraction, aggregation and decomposition techniques based on AI-style representations.