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This section presents a definition of computational reflection applicable to any model of computation, whether it be procedural, deductive, imperative, message-passing or other.We define computational reflection to k the behavior exhibited by a reflective system, where a refktive system is a computational system which is about itself in a causally connected way.In order to substantiate this definition, we next discuss relevant concepts such as computational system, about-ness and causal connection.

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Software agents help automate a variety of tasks including those involved in buying and selling products over the Internet. This paper surveys several of these agent-mediated electronic commerce systems by describing their roles in the context of a Consumer Buying Behavior (CBB) model. The CBB model we present augments traditional marketing models with concepts from Software Agents research to accommodate electronic markets. We then discuss the variety of Artificial Intelligence techniques that support agent mediation and conclude with future directions of agent-mediated electronic commerce research.

One category of research in Artificial Life is concerned with modeling and building so-called adaptive autonomous agents, which 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. It attempts to extract its main ideas, evaluates what contributions have been made so far, and identifies its current limitations and open problems. Keywords autonomous agents, behaviorbased artificial intelligence, artificial creatures, action selection, learning from experience agents, while Meyer [45] aims to give an overview of the research performed so far.Finally, a third reason is that, since the approach has been around for a number of years now, it is time to perform a critical evaluation. This paper discusses the basic problems of research in adaptive autonomous agents. It also presents an overview and evaluation of the state of the art of the field. In particular it identifies some of the more general and more specific open problems that still remain to be solved. Overview papers are necessarily biased. This paper is biased toward the research in adaptive autonomous agents that has taken place at the AI Laboratory and Media Laboratory of the Massachusetts Institute of Technology.The paper is structured as follows: Section 2 introduces the concept of an adaptive autonomous agent and defines the basic problems the field is trying to solve. Section 3 discusses the guiding principles of research in adaptive autonomous agents. Section 4 identifies the common characteristics of solutions that have been proposed. Section 5 discusses some example state of the art agents stemming from three different application domains: mobile robotics, interface agents, and scheduling systems. Section 6 presents a critical overview of the state of the art. It discusses the main architectures that have been proposed for building agents. In particular, it addresses progress made in models of action selection and models of learning from experience. Section 7 presents some overall conclusions.2 What is an Adaptive Autonomous Agent? An agent is a system that tries to fulfill a set of goals in a complex, dynamic environment. An agent is situated in the environment: It can sense the environment through its sensors and act upon the environment using its actuators. An agent's goals can take many different forms: they can be "end goals" or particular states the agent tries to achieve, they can be a selective reinforcement or reward that the agent attempts to maximize, they can be internal needs or motivations that the agent has t...

It is still an open question whether software agentsshould be person$ed in the inteflace. In order to study the effects of faces and facial expressions in the inteflace, a series of experiments was conducted to compare subjects' responses to and evaluation of diyerent faces and facial expressions.The experimental results obtained demonstrate that: 1) personijied interfaces help users engage in a task, and are well suited for an entertainment domain; 2) people's impressions of a face in a task are different from ones of the face in isolation. Perceived intelligence of a face is determined not by the agent's appearance but by its competence; 3) there is a dichotomy between user groups which have opposite opinions about person$ . Thus, agent-based interfaces should be flexible to rt the diversity of users' preferences and the nature of tasks.

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