Proceedings of the Annual Meeting of the Cognitive Science Society

The ability to set and achieve a wide range of goals is one of the principal hallmarks of intelligence. The issue of goals, and of how they can be achieved, has been one of the major foci of Artificial Intelligence (AI), and the understanding of how to construct systems that can accomplish a wide range of goals has been one of the major breakthroughs provided by the study of symbolic processing systems in AI. Neural networks, however, have not shared this focus on the issue of goals to any significant extent. This article provides a progress report on an effort to incorporate such an ability into neural networks. The approach we have taken here is to implement a symbolic problem solver within a neural network; specifically we are creating Neuro-Soar, a neural-network reimplementation of the Soar architecture. Soar is particularly appropriate for this purpose because of its well-established goal-oriented abilities, and its mapping onto levels of human cognition — in particular, the ways in which it already either shares, or is compatible with, a number of key characteristics of neural networks.