Optimal control of infinite horizon partially observable decision processes modelled as generators of probabilistic regular languages

Ishanu Chattopadhyay, Asok Ray

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Decision processes with incomplete state feedback have been traditionally modelled as partially observable Markov decision processes. In this article, we present an alternative formulation based on probabilistic regular languages. The proposed approach generalises the recently reported work on language measure theoretic optimal control for perfectly observable situations and shows that such a framework is far more computationally tractable to the classical alternative. In particular, we show that the infinite horizon decision problem under partial observation, modelled in the proposed framework, is λ-approximable and, in general, is not harder to solve compared to the fully observable case. The approach is illustrated via two simple examples.

Original languageEnglish (US)
Pages (from-to)457-483
Number of pages27
JournalInternational Journal of Control
Volume83
Issue number3
DOIs
StatePublished - Mar 1 2010

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications

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