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Modular Models of Intelligence – Review, Limitations and Prospects

Modular Models of Intelligence – Review, Limitations and Prospects AI applications are increasingly moving to modular agents, i.e.,systems that independently handle parts of the problem based on smalllocally stored information (Grosz and Davis 1994), (Russell and Norvig 1995). Many suchagents minimize inter-agent communication by relying on changes in theenvironment as their cue for action. Some early successes of thismodel, especially in robotics (``reactive agents''), have led to adebate over this class of models as a whole. One of theissues on which attention has been drawn is that of conflicts betweensuch agents. In this work we investigate a cyclic conflict thatresults in infinite looping between agents and has a severedebilitating effect on performance. We present some new results inthe debate, and compare this problem with similar cyclicity observedin planning systems, meta-level planners, distributed agent models andhybrid reactive models. The main results of this work are: http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Modular Models of Intelligence – Review, Limitations and Prospects

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References (50)

Publisher
Springer Journals
Copyright
Copyright © 2002 by Kluwer Academic Publishers
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1023/A:1015098212815
Publisher site
See Article on Publisher Site

Abstract

AI applications are increasingly moving to modular agents, i.e.,systems that independently handle parts of the problem based on smalllocally stored information (Grosz and Davis 1994), (Russell and Norvig 1995). Many suchagents minimize inter-agent communication by relying on changes in theenvironment as their cue for action. Some early successes of thismodel, especially in robotics (``reactive agents''), have led to adebate over this class of models as a whole. One of theissues on which attention has been drawn is that of conflicts betweensuch agents. In this work we investigate a cyclic conflict thatresults in infinite looping between agents and has a severedebilitating effect on performance. We present some new results inthe debate, and compare this problem with similar cyclicity observedin planning systems, meta-level planners, distributed agent models andhybrid reactive models. The main results of this work are:

Journal

Artificial Intelligence Review – Springer Journals

Published: Oct 10, 2004

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