Belief revision via Lamarckian evolution |
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Authors: | Evelina Lamma Fabrizio Riguzzi Luís Moniz Pereira |
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Affiliation: | (1) Department of Engineering, University of Ferrara, Via Saragat 1, 44100 Ferrara, Italy;(2) Centro de Inteligência Artificial (CENTRIA), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal |
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Abstract: | We present a system for performing belief revision in a multi-agent environment. The system is called GBR (Genetic Belief
Revisor) and it is based on a genetic algorithm. In this setting, different individuals are exposed to different experiences.
This may happen because the world surrounding an agent changes over time or because we allow agents exploring different parts
of the world. The algorithm permits the exchange of chromosomes from different agents and combines two different evolution
strategies, one based on Darwin’s and the other on Lamarck’s evolutionary theory. The algorithm therefore includes also a
Lamarckian operator that changes the memes of an agent in order to improve their fitness. The operator is implemented by means
of a belief revision procedure that, by tracing logical derivations, identifies the memes leading to contradiction. Moreover,
the algorithm comprises a special crossover mechanism for memes in which a meme can be acquired from another agent only if
the other agent has “accessed” the meme, i.e. if an application of the Lamarckian operator has read or modified the meme.
Experiments have been performed on the η-queen problem and on a problem of digital circuit diagnosis. In the case of the η-queen
problem, the addition of the Lamarckian operator in the single agent case improves the fitness of the best solution. In both
cases the experiments show that the distribution of constraints, even if it may lead to a reduction of the fitness of the
best solution, does not produce a significant reduction.
Evelina Lamma, Ph.D.: She is Full Professor at the University of Ferrara. She got her degree in Electrical Engineering at the University of Bologna
in 1985, and her Ph.D. in Computer Science in 1990. Her research activity centers on extensions of logic programming languages
and artificial intelligence. She was coorganizers of the 3rd International Workshop on Extensions of Logic Programming ELP92,
held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, held in Bologna in September
1999. Currently, she teaches Artificial Intelligence and Fondations of Computer Science.
Fabrizio Riguzzi, Ph.D.: He is Assistant Professor at the Department of Engineering of the University of Ferrara, Italy. He received his Laurea from
the University of Bologna in 1995 and his Ph.D. from the University of Bologna in 1999. He joined the Department of Engineering
of the University of Ferrara in 1999. He has been a visiting researcher at the University of Cyprus and at the New University
of Lisbon. His research interests include: data mining (and in particular methods for learning from multirelational data),
machine learning, belief revision, genetic algorithms and software engineering.
Luís Moniz Pereira, Ph.D.: He is Full Professor of Computer Science at Departamento de Informática, Universidade Nova de Lisboa, Portugal. He received
his Ph.D. in Artificial Intelligence from Brunel University in 1974. He is the director of the Artificial Intelligence Centre
(CENTRIA) at Universidade Nova de Lisboa. He has been elected Fellow of the European Coordinating Committee for Artificial
Intelligence in 2001. He has been a visiting Professor at the U. California at Riverside, USA, the State U. NY at Stony Brook,
USA and the U. Bologna, Italy. His research interests include: knowledge representation, reasoning, learning, rational agents
and logic programming. |
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Keywords: | Evolutionary Systems Belief Revision Learning Multi-agent Systems Multi-agent Communication |
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