Bunescu, Razvan C (Ohio University) | Carvalho, Vitor R. (Microsoft Live Labs) | Chomicki, Jan (University of Buffalo) | Conitzer, Vincent (Duke University) | Cox, Michael T. (BBN Technologies) | Dignum, Virginia (Utrecht University) | Dodds, Zachary (Harvey Mudd College) | Dredze, Mark (University of Pennsylvania) | Furcy, David (University of Wisconsin Oshkosh) | Gabrilovich, Evgeniy (Yahoo! Research) | Göker, Mehmet H. (PricewaterhouseCoopers) | Guesgen, Hans Werner (Massey University) | Hirsh, Haym (Rutgers University) | Jannach, Dietmar (Dortmund University of Technology) | Junker, Ulrich (ILOG) | Ketter, Wolfgang (Erasmus University) | Kobsa, Alfred (University of California, Irvine) | Koenig, Sven (University of Southern California) | Lau, Tessa (IBM Almaden Research Center) | Lewis, Lundy (Southern New Hampshire University) | Matson, Eric (Purdue University) | Metzler, Ted (Oklahoma City University) | Mihalcea, Rada (University of North Texas) | Mobasher, Bamshad (DePaul University) | Pineau, Joelle (McGill University) | Poupart, Pascal (University of Waterloo) | Raja, Anita (University of North Carolina at Charlotte) | Ruml, Wheeler (University of New Hampshire) | Sadeh, Norman M. (Carnegie Mellon University) | Shani, Guy (Microsoft Research) | Shapiro, Daniel (Applied Reactivity, Inc.) | Singh, Sarabjot Anand (University of Warwick) | Taylor, Matthew E. (University of Southern California) | Wagstaff, Kiri (Jet Propulsion Laboratory) | Smith, Trey (Carnegie Mellon University West) | Walsh, William (CombineNet) | Zhou, Ron (Palo Alto Research Center)
The program included the following fifteen workshops: Advancements in POMDP Solvers, AI Education Workshop, Coordination, Organization, Institutions and Norms in Agent Systems, Enhanced Messaging, Human Implications of Human-Robot Interaction, Intelligent Techniques for Web Personalization and Recommender Systems, Metareasoning: Thinking about Thinking, Multidisciplinary Workshop on Advances in Preference Handling, Search in Artificial Intelligence and Robotics, Spatial and Temporal Reasoning, Trading Agent Design and Analysis, Transfer Learning for Complex Tasks, What Went Wrong and Why: Lessons from AI Research and Applications, and Wikipedia and Artificial Intelligence: An Evolving Synergy. The goal of the Coordination, Organizations, Institutions and Norms in Multiagent Systems workshop was to examine and define the current state of the art research in agent systems research related to coordination, organizations institutions and norming. The Intelligent Techniques for Web Personalization and Recommender Systems workshop was scheduled as a joint event, bringing together researchers and practitioners from the fields of web personalization and recommender systems. The Search in Artificial Intelligence and Robotics workshop brought together search researchers to share their ideas and disseminate their latest research results.
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.
Cox, Michael T.
To construct a perpetual self-aware cognitive agent that can continuously operate with independence, an introspective machine must be produced. To assemble such an agent, it is necessary to perform a full integration of cognition (planning, understanding, and learning) and metacognition (control and monitoring of cognition) with intelligent behaviors. I outline some key computational requirements of metacognition by describing a multi- strategy learning system called Meta-AQUA and then discuss an integration of Meta-AQUA with a nonlinear state-space planning agent. I show how the resultant system, INTRO, can independently generate its own goals, and I relate this work to the general issue of self-awareness by machine.