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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.
@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/entities/document/10.4230/DagSemProc.05051.3},
URN = {urn:nbn:de:0030-drops-4192},
doi = {10.4230/DagSemProc.05051.3},
annote = {Keywords: Rational agent, agent architecture, belief base, Bayesian networks}
}