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Todays computer interfaces are one-size-fits-all. Users with little programming experience have very limited opportunities to customize an interface to their task and work habits. Furthermore, the overhead induced by generic interfaces will be proportionately greater on small form-factor PDAs, embedded applications and wearable devices. Automatic personalization may greatly enhance user productivity, but it requires advances in customization (explicit, user-initiated change) and adaptation (interface-initiated change in response to routine user behavior). In order to improve customization, we must make it easier for users to direct these changes. In order to improve adaptation, we must better predict user behavior and navigate the inherent tension between the dynamism of automatic adaptation and the stability required in order for the user to predict the computers behavior and maintain control. This paper surveys a decade's work on customization and adaptation at the University of Washington, distilling the lessons we have learned. 1

information gathering agent we have generated a new approach to sophisticated information gathering agent construction. The new architecture removes some researchgrade AI components, replaces others, and reframes the problem space to support stronger domain problem solving. The end result is an architecture that will support crisis response information gathering, travel planning, and music-centered digital library information gathering. In this paper we discuss the new architecture, provide rationale for change and restructuring, and identify the new technologies. Examples are framed in the travel planning domain as this application is the closest to actual deployment. The advantage of using agent technology in these applications is that it enables the software to meet realtime deadlines, respond to the dynamics of the Internet environment, reason about client preference, and plan to achieve the objectives. An agent approach will also facilitate future work in distributing the computation to multiple agents. 1

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 agentmediated 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

In this article we review knowledge representation formalisms that lend themselves to the representation of capabilities of intelligent agents. The aim of representing capabilities is, of course, that we want to reason about them. The reasoning task we are most interested in is capability brokering, i.e. the task of finding an agent which has a capability that can be used to address a given problem. Thus, the first area we review here is agent cooperation and communication from which the problem originates.