License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05051.3
URN: urn:nbn:de:0030-drops-4192
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2006/419/
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Lloyd, John W. ; Sears, Tim D.

An Architecture for Rational Agents

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05051.LloydJohn1.Other.419.pdf (0.2 MB)

Abstract

This paper is concerned with designing architectures for rational agents.
In the proposed architecture, agents have belief bases that are theories
in a multi-modal, higher-order logic.
Belief bases can be modified by a belief acquisition algorithm
that includes both symbolic, on-line learning and conventional knowledge base
update as special cases.
A method of partitioning the state space of the agent in two different ways
leads to a Bayesian network and associated influence diagram for selecting actions.
The resulting agent architecture exhibits a tight integration between logic,
probability, and learning.
This approach to agent architecture is illustrated by a user agent
that is able to personalise its behaviour according to the user's
interests and preferences.

BibTeX - Entry

@InProceedings{lloyd_et_al:DagSemProc.05051.3,
  author =	{Lloyd, John W. and Sears, Tim D.},
  title =	{{An Architecture for Rational Agents}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2006/419},
  URN =		{urn:nbn:de:0030-drops-4192},
  doi =		{10.4230/DagSemProc.05051.3},
  annote =	{Keywords: Rational agent, agent architecture, belief base, Bayesian networks}
}

Keywords: Rational agent, agent architecture, belief base, Bayesian networks
Collection: 05051 - Probabilistic, Logical and Relational Learning - Towards a Synthesis
Issue Date: 2006
Date of publication: 19.01.2006

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