This article describes the virtual humans developed as part of the Mission Rehearsal Exercise project, a virtual reality-based training system.

The American Association for Artificial Intelligence was pleased to present the AAAI 2006 Fall Symposium Series, held Friday through Sunday, October 13-15, at the Hyatt Regency Crystal City in Washington, DC. The titles were (1) Aurally Informed Performance: Integrating Machine Listening and Auditory Presentation in Robotic Systems; (2) Capturing and Using Patterns for Evidence Detection; (3) Developmental Systems; (4) Integrating Reasoning into Everyday Applications; (5) Interaction and Emergent Phenomena in Societies of Agents; (6) Semantic Web for Collaborative Knowledge Acquisition; and (7) Spacecraft Autonomy: Using AI to Expand Human Space Exploration. This symposium brought together a number of researchers who are concerned with performance issues that robots face that depend, in some way, on sound. Many commercially marketed robotic platforms, as well as others that are moving from the laboratory into specialized public settings, already have rudimentary speech communication interfaces, and some are even being engineered for specific types of auditory tasks. In general, though, the ability of robots to monitor the auditory scene before them and to execute interactive behaviors informed by the interpretation or production of sound information remains far behind the broad and mostly transparent skills of human beings.

Another popular event at the DC was the student-mentor dinner, held this year at Elephant Walk, which provided an opportunity for students and researchers to interact in an informal setting. We report on the eleventh annual SIGART/AAAI Doctoral Consortium, held in conjunction with the National Conference on Artificial Intelligence (AAAI-06). We discuss highlights and innovations of this year's consortium and include pointers to the consortium website. At the DC, Ph.D. students in artificial intelligence presented their proposed research and received feedback from a panel of researchers and other students. The primary goal of the DC is to give students feedback on their proposed dissertation research at a critical time, by independent, knowledgeable reviewers external to their institutions.

The SNePS knowledge representation, reasoning, and acting system has several features that facilitate metacognition in SNePSbased agents. The most prominent is the fact that propositions are represented in SNePS as terms rather than as sentences, so that propositions can occur as arguments of propositions and other expressions without leaving first-order logic. The SNePS acting subsystem is integrated with the SNePS reasoning subsystem in such a way that: there are acts that affect what an agent believes; there are acts that specify knowledge-contingent acts and lack-ofknowledge acts; there are policies that serve as "daemons," triggering acts when certain propositions are believed or wondered about. The GLAIR agent architecture supports metacognition by specifying a location for the source of self-awareness and of a sense of situatedness in the world. Several SNePSbased agents have taken advantage of these facilities to engage in self-awareness and metacognition.

Mixed-initiative systems are a popular approach to building intelligent systems that can collaborate naturally and effectively with people. But true collaborative behavior requires an agent to possess a number of capabilities, including reasoning, communication, planning, execution, and learning. We describe an integrated approach to the design and implementation of a collaborative problem-solving assistant based on a formal theory of joint activity and a declarative representation of tasks. This approach builds on prior work by us and by others on mixed-initiative dialogue and planning systems. We've all had the bad experience of working with someone who had to be told everything he or she needed to do (or worse, we had to do it for them).

Mixed-initiative assistants are agents that interact seamlessly with humans to extend their problem-solving capabilities or provide new capabilities. Developing such agents requires the synergistic integration of many areas of AI, including knowledge representation, problem solving and planning, knowledge acquisition and learning, multiagent systems, discourse theory, and human-computer interaction. This paper introduces seven aspects of mixed-initiative reasoning (task, control, awareness, communication, personalization, architecture, and evaluation) and discusses them in the context of several state-of-the-art mixed-initiative assistants. The goal is to provide a framework for understanding and comparing existing mixed-initiative assistants and for developing general design principles and methods. Mixed initiative assumes an efficient, natural interleaving of contributions by users and automated agents that is determined by their relative knowledge and skills and the problem-solving ...

The Fourth International Conference on Informatics in Control, Automation, and Robotics (ICINCO 2007) was held at the University of Angers from 9 through 12 May 2007. This conference sought to bring together researchers, engineers, and practitioners interested in the application of informatics to control, automation, and robotics, with an emphasis on intelligent systems and various AI technologies, such as expert systems, evolutionary computing, neural networks, and others, in connection to signal processing, systems modeling, and control. Beside the presentation of papers addressing these general topics, several specific themes were discussed during the conference in specialized forums, including special sessions, panels, and workshops, as described in this report. The conference was coorganized by the Institute for Systems and Technologies of Information, Control, and Communication (INSTICC) and the University of Angers, through the Laboratoire d'Ingénierie des Systèmes AI Magazine Volume 28 Number 4 (2007) ( AAAI) The conference was also held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI). The ICINCO 2007 conference program included oral presentations (full papers and short papers), as well as posters, organized in three simultaneous tracks: "Intelligent Control Systems and Optimization," "Robotics and Automation," and "Systems Modeling, Signal Processing, and Control."

I consider how to represent and reason with users' preferences. While areas of economics like social choice and game theory have traditionally considered such topics, I will argue that computer science and artificial intelligence bring some fresh perspectives to the study of representing and reasoning with preferences. For instance, I consider how we can elicit preferences efficiently and effectively. With one agent, the agent's desired goal may not be feasible. The agent wants a cheap, low-mileage Ferrari, but no such car exists.

Extempo Systems, Inc., was founded in 1995 to commercialize intelligent characters. Our team built innovative software and novel applications for several markets. We had some early-adopting customers during the Internet boom, but the company could not survive the significant downturn in corporate IT spending when the bubble burst. In 2004, Extempo ceased operations and was formally liquidated. Although our commercial venture failed, we advanced the technology for intelligent characters and learned a lot about how (not) to take them to market.

In many cooperative multiagent domains, the effect of local interactions between agents can be compactly represented as a network structure. Given that agents are spread across such a network, agents directly interact only with a small group of neighbors. A distributed constraint optimization problem (DCOP) is a useful framework to reason about such networks of agents. Given agents' inability to communicate and collaborate in large groups in such networks, we focus on an approach called k-optimality for solving DCOPs. In this approach, agents form groups of one or more agents until no group of k or fewer agents can possibly improve the DCOP solution; we define this type of local optimum, and any algorithm guaranteed to reach such a local optimum, as k-optimal.