New Generation Computing

, Volume 21, Issue 3, pp 247–275

Belief revision via Lamarckian evolution


  • Evelina Lamma
    • Department of EngineeringUniversity of Ferrara
  • Fabrizio Riguzzi
    • Department of EngineeringUniversity of Ferrara
  • Luís Moniz Pereira
    • Centro de Inteligência Artificial (CENTRIA), Faculdade de Ciências e TecnologiaUniversidade Nova de Lisboa
Regular Papers

DOI: 10.1007/BF03037475

Cite this article as:
Lamma, E., Riguzzi, F. & Pereira, L.M. New Gener Comput (2003) 21: 247. doi:10.1007/BF03037475


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.


Evolutionary SystemsBelief RevisionLearningMulti-agent SystemsMulti-agent Communication
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© Ohmsha, Ltd. and Springer 2003