
TL;DR
This paper explores Deterministic POMDPs, a subclass with deterministic actions and observations, analyzing their properties, relation to search problems, and computational complexity to facilitate more efficient planning algorithms.
Contribution
It provides fundamental insights into Deterministic POMDPs, their relation to AND/OR search problems, and their computational complexity, highlighting their potential for efficient planning.
Findings
Deterministic POMDPs have unique properties distinct from general POMDPs.
They are related to AND/OR search problems.
The paper characterizes their computational complexity.
Abstract
We study a subclass of POMDPs, called Deterministic POMDPs, that is characterized by deterministic actions and observations. These models do not provide the same generality of POMDPs yet they capture a number of interesting and challenging problems, and permit more efficient algorithms. Indeed, some of the recent work in planning is built around such assumptions mainly by the quest of amenable models more expressive than the classical deterministic models. We provide results about the fundamental properties of Deterministic POMDPs, their relation with AND/OR search problems and algorithms, and their computational complexity.
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Taxonomy
TopicsAI-based Problem Solving and Planning · Formal Methods in Verification · Software Testing and Debugging Techniques
