Robocup Symposium Talk 1

Tuesday, July 19, 15:40-16:30 (International Conference Hall)

An Adaptive Architecture for Physical Agents

Pat Langley

Computational Learning Laboratory
Center for the Study of Language and Information
Stanford University, USA

Abstract
In this talk I describe Icarus, an architecture for physical agents that unifies ideas from a number of traditions. The framework supports the reactive execution of routine skills, but it combines this ability with conceptual inference to ensure informed behavior, and it associates skills with goals to ensure relevant action. The architecture defaults to execution whenever it has an applicable skill that will achieve its current goal, but it falls back on means-ends problem solving when it encounters an impasse. Moreover, successful problem solving leads to the creation of new executable skills that let the system achieve its goals more directly in the future. Thus, Icarus incorporates insights from research on human problem solving, reactive control, logical inference, and analytical learning. The architecture differs from predecessors in its focus on physical agents, its commitment to hierarchical organizations of knowledge, and its approach to the cumulative acquisition of these structures. The framework is broadly consistent with results from cognitive pychology, but also providesa realistic platform for developing adaptive intelligent agents. I illustrate Icarus' capabilities on tasks from an in-city driving environment and report experiments on this and other domains. This talk describes joint work with Nima Asgharbeygi, Dongkyu Choi, Negin Nejati, Seth Rogers, Stephanie Sage, and Daniel Shapiro.

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