Explicitly biased generalization
Computational Intelligence
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Using explicit affirmation and explicit negation, whilst allowing for a third logic value of undefinedness, can be useful in situations where decisions have to be taken on the basis of scarce, ambiguous, or downright contradictory information. In a three-valued setting, we consider an agent that learns a definition for both the target concept and its opposite, considering positive and negative examples as instances of two disjoint classes. Explicit negation is used to represent the opposite concept, while default negation is used to ensure consistency and to handle exceptions to general rules. Exceptions are represented by examples covered by the definition for a concept that belong to the training set for the opposite concept. One single agent exploring an environment may gather only so much information about it and that may not suffice to find the right explanations. In such case, a cooperative multi-agent strategy, where each agent explores a part of the environment and shares with the others its findings, might provide better results. We describe one such framework based on a distributed genetic algorithm enhanced by a Lamarckian operator for belief revision. The agents communicate their candidate explanations -- coded as chromosomes of beliefs -- by sharing them in a common pool. Another way of interpreting this communication is in the context of argumentation. In the process of taking all the arguments and trying to find a common ground or consensus we might have to change, or review, some of assumptions of each argument. The resulting framework we present is a collaborative perspective of argumentation where arguments are put together at work in order to find the possible 2-valued consensus of opposing positions of learnt concepts in an evolutionary pool in order to find the "best" explanation to the observations.