Incremental Discovery of Hidden Structure: Applications in Theory of Elementary Particles

No. 1: Agents, AI in Art and Entertainment, Knowledge Representation, and Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Discovery

Discovering hidden structure is a challenging, universal research task in Physics, Chemistry, Biology, and other disciplines. Not only must the elements of hidden structure be postulated by the discoverer, but they can only be verified by indirect evidence, at the level of observable objects. In this paper we describe a framework for hidden structure discovery, built on a constructive definition of hidden structure. This definition leads to operators that build models of hidden structure step by step, postulating hidden objects, their combinations and properties, reactions described in terms of hidden objects, and mapping between the hidden and the observed structure. We introduce the operator dependency diagram, which shows the order of operator application and model evaluation. Different observational knowledge supports different evaluation criteria, which lead to different search systems with verifiable sequences of operator applications. Isomorphfree structure generation is another issue critical for efficiency of search. We apply our framework in the system GELL-MANN, that hypothesizes hidden structure for elementary particles and we present the results of a large scale search for quark models.