Integrating Perception, Language and Problem Solving in a Cognitive Agent for a Mobile Robot

摘要

We are implementing a unified cognitive architecture for a mobile robot. Our goal is to endow a robot agent with the full range of cognitive abilities, including perception, use of natural language, learning and the ability to solve complex problems. The perspective of this work is that an architecture based on a unified theory of robot cognition has the best chance of attaining human-level performance. This agent architecture is an integration of three theories: a theory of cognition embodied in the Soar system, the RS formal model of sensorimotor activity and an algebraic theory of decomposition and reformulation. These three theories share a hierarchical structure that is the basis of their integration. These three component theories have been implemented and tested separately and their integration is currently underway. This paper describes these components and the overall architecture. The Cognitive Architecture Our robot cognitive architecture exhibits a hierarchy of sensory-motor modules in all facets of its operation, from abstract deliberative planning to realtime scheduling and reaction. It is being implemented by integrating two existing systems: RS and Soar, and tested on Pioneer P2 robots. RS is a formal model of robot computation that is based on the semantics of networks of port automata [5]. A port automaton (PA) is a finite-state automaton equipped with a set of synchronous communication ports