Regular decision processes: A model for non-markovian domains

Ronen I. Brafman, Giuseppe De Giacomo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We introduce and study Regular Decision Processes (RDPs), a new, compact, factored model for domains with non-Markovian dynamics and rewards. In RDPs, transition and reward functions are specified using formulas in linear dynamic logic over finite traces, a language with the expressive power of regular expressions. This allows specifying complex dependence on the past using intuitive and compact formulas, and provides a model that generalizes MDPs and k-order MDPs. RDPs can also approximate POMDPs without having to postulate the existence of hidden variables, and, in principle, can be learned from observations only.

Original languageAmerican English
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
EditorsSarit Kraus
Pages5516-5522
Number of pages7
ISBN (Electronic)9780999241141
DOIs
StatePublished - 1 Jan 2019
Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
Duration: 10 Aug 201916 Aug 2019

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2019-August

Conference

Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Country/TerritoryChina
CityMacao
Period10/08/1916/08/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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