Reacting, Planning, and Learning in an Autonomous Agent

Abstract

Abstract We present an autonomous agent architecture and its component subsystems that integrate important abilities needed for robust, flexible performance in dynamic environments. These abilities involve appropriate reaction to environmental situations given the agent’s goals; selective attention to multiple, competing goals; planning new action routines when innovation beyond designer-provided routines is necessary; and learning the effects of actions so that the planner can use them to build ever more reliable plans. The teleo-reactive format allows actions to be closely coupled to continuous environmental feedback and is also especially compatible with conventional AI planning and learning mechanisms. The workings of the proposed architecture and its subsystems are illustrated in a simulated robot domain. We conclude by noting areas where future work is needed.