Modularity and Specialized Learning: Reexamining Behavior-Based Artificial Intelligence

Abstract

Learning, like any search, is only tractable for situated, resource-constrained agents if it is tightly focused. Adaptation is only worth the risks inherent in changing a complicated intelligence if it is very likely to improve the agent’s performance on its goal tasks. Modularity is one tool for providing the information a learning system needs: it facilitates the use of a specialized representation suitable to a particular learning task, and provides for specialized perception to inform that representation. This paper begins by examining why behavior-based artificial intelligence, a well-known modular theory of intelligent design, has not so-far been used systematically to support such an approach. It then describes a new design methodology, behavior-oriented design (BOD), which does. Examples, drawn from both mobile robotics and models of learning in non-human primates, show the sorts of information such an approach can support, including both explicit and implicit anticipatory representations.