An adaptive architecture for physical agents

In this paper we describe ICARUS, an adaptive architecture for intelligent physical agents. We contrast the framework´s assumptions with those of earlier architectures, taking examples from an in-city driving task to illustrate our points. Key differences include: primacy of perception and action over problem solving, separate memories for categories and skills, a hierarchical organization on both memories, strong correspondence between long-term and short-term structures, and cumulative learning of skill hierarchies. We support claims for ICARUS´ generality by reporting our experience with driving and three other domains. In closing, we discuss limitations of the current architecture and propose extensions that would remedy them.