Motivated Agents

Abstract

Agents are systems capable of perceiving their environment through sensors, reasoning about their sensory input using some characteristic reasoning process and acting in the world using their effectors. There exists a wide range of specific agent models that fit this general description. For example, learning agents are agents whose characteristic reasoning process is a learning algorithm. Planning agents are agents whose primary component is a planner. Cognitive agents are agents whose processes model those of the human mind. Agent models are generally context-free. That is, they are designed to be independent of the motor and perceptual system of the agent. This reduces the work required to place agents conforming to a particular model in a new domain. Unfortunately, introducing an agent to a particular problem domain usually requires extensive preparation of other information in order for the agent to function in that domain. For example, necessary preparation may include the definition of domain specific reward functions, goals, world models or examples of correct behaviour. With such extensive domain specific preparation are the resulting agents truly autonomous? Is it possible to build agents that do not require such extensive domain specific preparation? What general mechanisms and structures would such agents need to perform useful tasks in any domain? In this document we propose a course of research to develop and evaluate approaches to agent design which reduce o