Human Discovery and Machine Learning

Human Discovery and Machine Learning

Christopher Dartnell (University of Montpellier II and LIRDEF, France), Éric Martin (University of New South Wales, Australia), Hélène Hagège (University of Montpellier II and LIRDEF, France) and Jean Sallantin (University of Montpellier II and LIRDEF, France)
DOI: 10.4018/jcini.2008100105


This article studies machine learning paradigms from the point of view of human cognition. Indeed, conceptions in both machine learning and human learning evolved from a passive to an active conception of learning. Our objective is to provide an interaction protocol suited to both humans and machines to enable assisting human discoveries by learning machines. We identify the limitations of common machine learning paradigms in the context of scientific discovery, and we propose an extension inspired by game theory and multiagent systems. We present individual cognitive aspects of this protocol as well as social considerations, and we relate encouraging results concerning a game implementing it.

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