The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Well founded semantics for logic programs with explicit negation
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Diagnosis and debugging as contradiction removal
Proceedings of the second international workshop on Logic programming and non-monotonic reasoning
SLX—a top-down derivation procedure for programs with explicit negation
ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
Optimization with genetic algorithm hybrids that use local searches
Adaptive individuals in evolving populations
A survey of paraconsistent semantics for logic programs
Handbook of defeasible reasoning and uncertainty management systems
Well-founded abduction via tabled dual programs
Proceedings of the 1999 international conference on Logic programming
Machine Learning
Reasoning with Logic Programming
Reasoning with Logic Programming
‘Classical’ Negation in Nonmonotonic Reasoning and Logic Programming
Journal of Automated Reasoning
Abduction over 3-Valued Extended Logic Programs
LPNMR '95 Proceedings of the Third International Conference on Logic Programming and Nonmonotonic Reasoning
REVISE: Logic Programming and Diagnosis
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Prolegomena to Logic Programming for Non-monotonic Reasoning
NMELP '96 Selected papers from the Non-Monotonic Extensions of Logic Programming
Cultural transmission of information in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
The Journal of Machine Learning Research
Evolution and incremental learning in the iterated prisoner's dilemma
IEEE Transactions on Evolutionary Computation
Adaptive reasoning for cooperative agents
INAP'09 Proceedings of the 18th international conference on Applications of declarative programming and knowledge management
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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 n -queen problem and on a problem of digital circuit diagnosis. In the case of the n-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.