_:n5ef7f5fe4de24c949a735e88598ee3d5xb0
<http://schema.org/description>
"Planning under uncertainty is a central problem in the study of automated\nsequential decision making, and has been addressed by researchers in many\ndifferent fields, including AI planning, decision analysis, operations\nresearch, control theory and economics. While the assumptions and perspectives\nadopted in these areas often differ in substantial ways, many planning problems\nof interest to researchers in these fields can be modeled as Markov decision\nprocesses (MDPs) and analyzed using the techniques of decision theory. This\npaper presents an overview and synthesis of MDP-related methods, showing how\nthey provide a unifying framework for modeling many classes of planning\nproblems studied in AI. It also describes structural properties of MDPs that,\nwhen exhibited by particular classes of problems, can be exploited in the\nconstruction of optimal or approximately optimal policies or plans. Planning\nproblems commonly possess structure in the reward and value functions used to\ndescribe performance criteria, in the functions used to describe state\ntransitions and observations, and in the relationships among features used to\ndescribe states, actions, rewards, and observations. Specialized\nrepresentations, and algorithms employing these representations, can achieve\ncomputational leverage by exploiting these various forms of structure. Certain\nAI techniques -- in particular those based on the use of structured,\nintensional representations -- can be viewed in this way. This paper surveys\nseveral types of representations for both classical and decision-theoretic\nplanning problems, and planning algorithms that exploit these representations\nin a number of different ways to ease the computational burden of constructing\npolicies or plans. It focuses primarily on abstraction, aggregation and\ndecomposition techniques based on AI-style representations."
<https://cdnjs.deepai.org/publication/decision-theoretic-planning-structural-assumptions-and-computational-leverage>