Proceedings of the Annual Meeting of the Cognitive Science Society

OOPS is a reactive planner which integrates sensory perception and action selection. A principal feature of the OOPS architecture is the use and discovery of cheap, diagnostic features to indicate opportunities, which are then verified in more expensive computations. This diagnostic relationship is established analytically and refined using tools from decision theory. The use and refinement of diagnostic features depends upon assumptions of conditional independence. However, in the case of a multiplanning agent (one which simultaneously pursues several goals), while conditional independence holds true for features vis-avis individual opportunities, plans may interact in the world, and conditional independence may not hold. In this paper we discuss how the knowledge needed to avoid detrimental action interactions can be incorporated into OOPS's inexpensive diagnostic computations, with benefits for robustness, performance, and learning.