This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life, The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they (...) are contradictory, Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is acccpted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherende and incoherence relations are encoded by excitatory and inbihitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lovoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy. (shrink)

In the current research on multi-agent systems (MAS), many theoretical issues related to sociocultural processes have been touched upon. These issues are in fact intellectually profound and should prove to be significant for MAS. Moreover, these issues should have equally significant impact on cognitive science, if we ever try to understand cognition in the broad context of sociocultural environments in which cognitive agents exist. Furthermore, cognitive models as studied in cognitive science can help us in a substantial way to better (...) probe multi-agent issues, by taking into account essential characteristics of cognitive agents and their various capacities. In this paper, we systematically examine the interplay among social sciences, MAS, and cognitive science. We try to justify an integrated approach for MAS which incorporates different perspectives. We show how a new cognitive model, CLARION, can embody such an integrated approach through a combination of autonomous learning and assimilation. (shrink)

This article proposes a number of models to examine through which mechanisms a population of autonomous agents could arrive at a repertoire of perceptually grounded categories that is sufficiently shared to allow successful communication. The models are inspired by the main approaches to human categorisation being discussed in the literature: nativism, empiricism, and culturalism. Colour is taken as a case study. Although we take no stance on which position is to be accepted as final truth with respect to human categorisation (...) and naming, we do point to theoretical constraints that make each position more or less likely and we make clear suggestions on what the best engineering solution would be. Specifically, we argue that the collective choice of a shared repertoire must integrate multiple constraints, including constraints coming from communication. Key Words: autonomous agents; colour categorisation; colour naming; connectionism; cultural evolution; genetic evolution; memes; origins of language; self-organisation; semiotic dynamics; symbol grounding. (shrink)