A hybrid model of learning to solve physics problems

Abstract

The current state of the debate between proponents of connectionism and defenders of traditional, symbolic modelling is used as the basis of an argument for the potential usefulness of hybrid models, in which principles of both connectionist and symbolic models are combined. The possible merits of hybrid models are illustrated by showing how a hybrid model of aspects of expertise development in physics problem solving might be superior to existing models. In the hybrid model, which is capable of solving simple mechanics problems and of learning from its own solutions, a distributed memory module is linked to a classical production system. In the distributed memory module, associations between problem statements and useful subgoals are stored. These subgoals serve to guide the inference process carried out by the production system, making the system's solutions more efficient as learning proceeds. The results of a simulation experiment indicate that the trained system's solutions closely correspond to those generated by human experts, and that the hybrid model probably offers a better account of the development and nature of expert reasoning than existing symbolic models.