Qualitative MDPs and POMDPs: An Order-Of-Magnitude Approximation

12/12/2012
by   Blai Bonet, et al.
0

We develop a qualitative theory of Markov Decision Processes (MDPs) and Partially Observable MDPs that can be used to model sequential decision making tasks when only qualitative information is available. Our approach is based upon an order-of-magnitude approximation of both probabilities and utilities, similar to epsilon-semantics. The result is a qualitative theory that has close ties with the standard maximum-expected-utility theory and is amenable to general planning techniques.

READ FULL TEXT

page 1

page 2

page 3

page 4

02/14/2012

Order-of-Magnitude Influence Diagrams

In this paper, we develop a qualitative theory of influence diagrams tha...
06/27/2022

Utility Theory for Sequential Decision Making

The von Neumann-Morgenstern (VNM) utility theorem shows that under certa...
01/23/2013

A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes

We present a technique for speeding up the convergence of value iteratio...
04/19/2020

Faster Algorithms for Quantitative Analysis of Markov Chains and Markov Decision Processes with Small Treewidth

Discrete-time Markov Chains (MCs) and Markov Decision Processes (MDPs) a...
02/04/2014

Qualitative Order of Magnitude Energy-Flow-Based Failure Modes and Effects Analysis

This paper presents a structured power and energy-flow-based qualitative...
09/26/2013

Qualitative Possibilistic Mixed-Observable MDPs

Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POM...
08/31/2021

Approximation Methods for Partially Observed Markov Decision Processes (POMDPs)

POMDPs are useful models for systems where the true underlying state is ...