ABSTRACT
Abduction is the process of generating a best explanation for a set of observations. Symbolic models of abductive reasoning tend to be far too search-intensive (e.g., Peng & Reggia, 1990), whereas connectionist models (e.g., Thagard, 1989) have difficulty explaining higher level abductive reasoning, such as the generation and revision of explanatory hypotheses. This chapter proposes a hybrid learning model for abduction that tightly integrates a symbolic Soar model (Newell, 1990) for generating and revising hypotheses with Echo, a connectionist model for evaluating explanations (Thagard, 1989). In this model, Soar’s symbolic knowledge compilation mechanism, chunking, acquires rules for forming and revising hypotheses and for taking actions based on the evaluations of these hypotheses. Thus, chunking models the problem solver’s shift from slow, deliberate reasoning to quick, automatic reasoning. To complement this, Echo learns to provide better hypothesis evaluations by acquiring explanatory strengths based on the frequencies of events from past experience. Since Echo does not have a learning mechanism, it was extended by adding the Rescorla-Wagner learning rule (Rescorla and Wagner, 1972). This hybrid model is motivated and supported by experimental results from the literature.
