Improving Learning Using Causality and Abduction

Marco Botta, S. Ravotto, Lorenza Saitta, S. B. Sperotto

Abstract: This paper presents a system which learns and maintains a diagnostic knowledge base using a causal model of the domain, a body of phenomenological theory and a set of examples. The phenomenological knowledge is used deductively, the causal model is used abductively and the examples are used inductively. The problems of imperfection and intractability of the theory are handled by allowing the system to make assumptions during its reasoning. In this way, robust knowledge can be learned with limited complexity and limited number of examples. An example in the domain of heat transfer is presented.