Assisting Scientific Discovery with an Adaptive Problem Solver
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This paper is an attempt to design an interaction protocol for a multi-agent learning platform to assist a human community in their task of scientific discovery. Designing tools to assist Scientific Discovery offers a challenging problematic, since the problems studied by scientists are not yet solved, and valid models are not yet available. It is therefore impossible to create a problem solver to simulate a given phenomenon and explain or predict facts. We propose to assist scientists with learning machines considered as adaptive problem solvers, to build interactively a consistent model suited for reasoning, simulating, predicting, and explaining facts.
The interaction protocol presented in this paper is based on Angluin’s “Learning from Different Teachers”  and we extend the original protocol to make it operational to assist scientists solve open problems. The main problem we deal with is that this learning model supposes the existence of teachers having previously solved the problem. These teachers are able to answer the learner’s queries whereas this is not the case in the context of Scientific Discovery in which it is only possible to refute a model by finding experimental processes revealing contradictions. Our first contribution is to directly use Angluin’s interaction protocol to let a machine learn a program that approximates the theory of a scientist, and to help him improve this theory. Our second contribution is to attenuate Angluin’s protocol to take into account a social cognition level during which multiple scientists interact with each other by the means of publications and refutations of rival theories. The program learned by the machine can be included in a publication to avoid false refutations coming from a wrong interpretation of the theory.
KeywordsProblem Solver Deontic Logic Interaction Protocol Paraconsistent Logic Valid Sequence
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- 2.Nobrega, G.M.D., Cerri, S.: A contradiction driven approach to theory information: Conceptual issues pragmatics in human learning, potentialities. Journal of the Brazilian Computer Society 9, 37–55 (2003)Google Scholar
- 3.Angluin, D.: Queries and concept learning. Machine Learning 2, 319–342 (1988)Google Scholar
- 5.Popper, K.R.: Conjectures and Refutations: The Growth of Scientific Knowledge. Harper and Row (1963)Google Scholar
- 6.Cavaillès, J.: Sur la logique et la théorie de la science. Librairie Philosophique J. VRIN (1997)Google Scholar
- 7.Beziau, J.Y.: La logique paraconsistante. Logiques classiques et non classiques, essai sur les fondements de la logique (1997)Google Scholar
- 8.Nakamatsu, K., Kato, T., Suzuki, A.: Basic ideas of defeasible deontic traffic signal control based on a paraconsistent logic program evalpsn. Advances in Intelligent Systems and Robotics (2003)Google Scholar
- 9.Angluin, D.: Negative results for equivalence queries. Machine Learning 5, 121–150 (1990)Google Scholar
- 12.Sallantin, J.: La découverte scientifique assistée par des agents rationnels. Revue des sciences et technologie de l’information, 15–30 (2003)Google Scholar
- 13.Liquière, M.: Structural machine learning with galois lattice and graphs. In: International Conference on Machine Learning - ICML (1998)Google Scholar
- 14.Chavalarias, D.: La thèse de Popper est-elle réfutable? Memoire de dea, CREA - CNRS/Ecole Polytechnique (1997)Google Scholar
- 15.Gardner, M.: Mathematical games. Scientific American (1959)Google Scholar
- 16.Cole, M., Engeström, Y.: A cultural historical approach to distributed cognition. Distributed Cognition, 1–46 (1993)Google Scholar
- 17.Garland, A., Alterman, R.: Multiagent learning through collective memory. In: Adaptation, Coevolution and Learning in Multiagent Systems: Papers from the 1996 AAAI Spring Symposium, pp. 33–38 (1996)Google Scholar
- 18.Dunbar, K.: How scientists really reason: Scientific reasoning in real-world laboratories. Mechanisms of Insight (1995)Google Scholar