If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Anand, Sarabjot Singh (University of Warwick) | 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.) | Smith, Trey (Carnegie Mellon University West) | Taylor, Matthew E. (University of Southern California) | Wagstaff, Kiri (Jet Propulsion Laboratory) | Walsh, William (CombineNet) | Zhou, Ron (Palo Alto Research Center)
Cox, Michael T., Zhang, Chen
In state-space planning, search consists of backward and forward chaining through the effects and preconditions of operator representations. Although search is an acceptable mechanism to use in performing automated planning, we present an alternative model to present to the user at the interface of a mixed-initiative planning assistant. That is, we propose to model planning as a goal-manipulation task. This article empirically examines user performance under both the search and the goal-manipulation models of planning and shows that many users do better with the latter.
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.