The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Diagnosis and debugging as contradiction removal
Proceedings of the second international workshop on Logic programming and non-monotonic reasoning
Optimization with genetic algorithm hybrids that use local searches
Adaptive individuals in evolving populations
Machine Learning
‘Classical’ Negation in Nonmonotonic Reasoning and Logic Programming
Journal of Automated Reasoning
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
REVISE: Logic Programming and Diagnosis
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
A Hybrid Genetic Algorithm for School Timetabling
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
An analysis of Lamarckian learning in changing environments
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
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We propose a multi-agent genetic algorithm to accomplish belief revision. The algorithm implements a new evolutionary strategy resulting from a combination of Darwinian and Lamarckian approaches. Besides encompassing the Darwinian operators of selection, mutation and crossover, it comprises a Lamarckian operator that mutates the genes in a chromosome that code for the believed assumptions. These self mutations are performed as a consequence of the chromosome phenotype's experience obtained while solving a belief revision problem. They are directed by a belief revision procedure which relies on tracing the logical derivations leading to inconsistency of belief, so as to remove the latter's support on the gene coded assumptions, by mutating the genes.