A theory of diagnosis from first principles
Artificial Intelligence
Toward diagnosis as an emergent behavior in a network ecosystem
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
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In this paper we describe the spatial multiagent student diagnosis and we apply it to student modelling in a multiagent learning environment. The spatial multiagent diagnosis is an approach where multiple agents share a n-dimensional space of possible diagnoses, self-organise themselves and reach together an state characterising a particular diagnosis. Usually a diagnosis task is run by a powerful entity able to observe, make hypothesis and come up with a diagnosis describing the state of the process being observed. Diagnosis may however be a complex task when it comprehends the observation of multiple controls, sensors and variables. In this context, it is appropriated to conceive the diagnosis task as a group decision problem, where agents (representing sensors, control and variables) collectively take decisions in order to characterise the state of the observed process. Following this perspective, the proposed approach is described and its evaluation is as well discussed in this paper.