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Review of Pattie Maes: Modeling Adaptive Autonomous Agents
Abstract
This paper will examine and compare the recent autonomous agent architectures (behavior-based/bottom-up architectures) as defined by Pattie Maes from the work done by Brooks, Wilson, and Meyer against the more traditional AI architectures (knowledgebased /top-down architectures). This will be followed by an analysis and comparison of the three main types of learning architectures (Reinforcement Learning, Classifier Systems, and Model Builders). Finally, the paper will be concluded by my personal thoughts and where I think the future of AI is heading. 1 Introduction The concept of AI has been around even longer than computers have existed. However we have never been able to bring AI to the level of competency that we had originally envisioned. It has only been within the last couple of decades that significant progress has been made in the field of AI. Maes' Modeling Adaptive Autonomous Agents [1] examines the various "new age" AI approaches and contrasts them against older and more tr...
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A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
One category of researchers in artificial life is concerned with modeling and building so-called adaptive autonomous agents. Autonomous agents are systems that inhabit a dynamic, unpredictable environment in which they try to satisfy a set of time-dependent goals or motivations. Agents are said to be adaptive if they improve their competence at dealing with these goals based on experience. Autonomous agents constitute a new approach to the study of artificial intelligence (AI) which is highly inspired by biology, in particular ethology, the study of animal behavior. Research in autonomous agents has brought about a new wave of excitement into the field of AI. This paper reflects on the state of the art of this new approach.