Organizations are adaptive systems that continually attempt to push the limits of their own effectiveness to approach perfection. This approach is true of the "mom and pop" store that is threatened by the growth of shopping malls. It is true of the gigantic corporation that is threatened by public regulation and private competition. It is particularly true of organizations that are confronted with complex tasks, the vagaries of uncertainty, and the high and visible costs of irreversible error. The cause of organization ineffectiveness or, indeed, failure is often perceived to be human frailty (Perrow 1984).
The Sixth International Conference on Enterprise Information Systems (ICEIS) was held in Porto, Portugal; previous venues were in Spain, France, and the United Kingdom. Since its inception in 1999, ICEIS has grown steadily, and is now one of the largest international conferences in the area of information systems. In 2004, more than 600 papers were submitted to the conference and its ten satellite workshops. One of the interesting features of this conference is the high number of invited speakers. In 2004, eighteen keynote speakers were featured at ICEIS and its workshops.
The Find-the-Remote event was considered the most challenging of the events in the 1997 AAAI Mobile Robot Competition and Exhibition. It required a broad range of both hardware and software capabilities. I discuss the rules and rationale for the event as well as the results. It involved fetching a known set of objects from unknown, but constrained, locations in a known environment. In real life, such functions might be useful for in-home care of the elderly or the physically disabled. This event was extremely difficult because it forced teams to implement both manipulation (the grasping and moving of objects) and visual object recognition. Furthermore, it explicitly required teams to implement them for a wide range of objects. It therefore eliminated a broad range of special-purpose sensing and manipulation strategies that would be specific to one or another class of objects. It also required that objects be lifted from a variety of surfaces (real furniture) at a variety of heights.
The Third Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises was held from 17-19 April 1994 in Morgantown, West Virginia, hosted by the Concurrent Engineering Research Center at West Virginia University. This report summarizes this year's workshop and outlines the philosophy behind this annual event. This report summarizes this year's workshop and outlines the philosophy behind this annual event. The WET ICE workshop, now in its third year, has become a fixture of the collaborative computing scene. A more specialized event than the Computer-Supported Cooperative Work gathering, which takes in everyone from anthropologists to futurists, this workshop focuses on hardware and software that enables agents of all kinds to interact in a variety of ways to accomplish some task--quickly, correctly, and easily.
MIT Artificial Intelligence Laboratory The MIT AI Laboratory has a long tradition of research in most aspects of Artificial Intelligence. Currently, the major foci include computer vision, manipulation, learning, Englishlanguage understanding, VLSI design, expert engineering problem solving, commonsense reasoning, computer architecture, distributed problem solving, models of human memory, programmer apprentices, and human education. Understanding Visual Images Professor Berthold K. P. Horn and his students have studied intensively the image irradiance equation and its applications. The reflectance and albedo map representations have been introduced to make surface orientation, illumination geometry, and surface reflectivity explicit. Recent work has centered on modelling the effects of the atmosphere which distort intensity values and make classification of terrain and related computations using the albedo map inaccurate.
Over the past decade, the commercial games industry has come to realize the importance of AI to its next-generation products. Similarly, the academic community now recognizes the interesting research challenges of game AI. AAAI responded to this interest with the creation in 2005 of the Artificial Intelligence and Interactive Digital Entertainment conference series. The third AIIDE conference was held in June 2007 and was a great success. It featured 10 (!) invited speakers and attracted an excellent mix of academic researchers and industry practitioners.
We report part of the design experience gained in X-Media, a system for knowledge management and sharing. Consolidated techniques of interaction design (scenario-based design) had to be revisited to capture the richness and complexity of intelligent interactive systems. We show that the design of intelligent systems requires methodologies (faceted scenarios) that support the investigation of intelligent features and usability factors simultaneously. Interaction designers become mediators between intelligent technology and users and have to facilitate reciprocal understanding. However, which design process should be followed to achieve such success is not clear: is a user-centered system design process enough, or should a new practice be developed to address the specificity of systems able to take autonomous decisions? From the very beginning it was clear that a participatory approach with both users and technologists discussing and contributing to the system design was not an easy goal: ambiguity in terminology and gaps in understanding could not be easily overcome. A new role had to be devised, that of a mediator that moves between the two parties, facilitates the communication, and helps each group see the potential in what the other has to offer. A number of tools to facilitate the mediation and preserve the original intended meaning (so as to avoid "translation mistakes" while moving from one group to the other) were devised. Mediating between the parties meant an increase in the number of design iterations, as, for example, discussing with users a potential solution generated new ideas for additional intelligent features that had to be discussed with the AI experts and then validated with users. The rest of this article presents and discusses this experience in more detail.
Interface personalization aims to streamline the process of working in a feature-rich application by providing the user with an adapted interface tailored specifically to his or her needs. The mixed-initiative customization assistance (MICA) system explores a middle ground between two opposing approaches to personalization: (1) an adaptable approach, where personalization is fully user controlled, and (2) and adaptive approach, where personalization is fully system controlled. We overview MICA's strategy for providing user-adaptive recommendations to help users decide how to personalize their interfaces. In doing so, we focus primarily on how MICA handles threats to usability that are often found in adaptive interfaces including obtrusiveness and lack of understandability and control. We also describe how we evaluated MICA and highlight results from these evaluations.