Proceedings of the Annual Meeting of the Cognitive Science Society

The CHILDLIKE system is designed to learn about objects, object qualities, relationships among objects, and words that refer to them. Once sufficient visual-linguistic associations have been established, they can be used as foundations for a) further learning involving language alone and b) reasoning about the effect of different actions on perceived objects and relations, and internally sensed need levels. Here, we address the issue of learning efficient rules for action selection. A trial-and-error (or reinforcement) learning algorithm is used to acquire and refine action-related rules. Learning takes place via generation of hypotheses to guide movement through sequences of states, as well as modifications to two entities: the weight associated with each action, which encodes the uncertainty underlying the action, and the potential value (or vector) of each state which encodes the desirability of the state with respect to the current needs. CHILDLIKE is described, and issues relating to the handling of uncertainty, generalization of rules and the role of a short-term memory are also briefly addressed.