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) …
Agmon, Noa (University of Texas at Austin) | Agrawal, Vikas (Infosys Labs) | Aha, David W. (Naval Research Laboratory) | Aloimonos, Yiannis (University of Maryland, College Park) | Buckley, Donagh (EMC) | Doshi, Prashant (University of Georgia) | Geib, Christopher (University of Edinburgh) | Grasso, Floriana (University of Liverpool) | Green, Nancy (University of North Carolina Greensboro) | Johnston, Benjamin (University of Technology, Sydney) | Kaliski, Burt (VeriSign, Inc.) | Kiekintveld, Christopher (University of Texas at El Paso) | Law, Edith (Carnegie Mellon University) | Lieberman, Henry (Massachusetts Institute of Technology) | Mengshoel, Ole J. (Carnegie Mellon University) | Metzler, Ted (Oklahoma City University) | Modayil, Joseph (University of Alberta) | Oard, Douglas W. (University of Maryland, College Park) | Onder, Nilufer (Michigan Technological University) | O'Sullivan, Barry (University College Cork) | Pastra, Katerina (Cognitive Systems Research Insitute) | Precup, Doina (McGill University) | Ramachandran, Sowmya (Stottler Henke Associates, Inc.) | Reed, Chris (University of Dundee) | Sariel-Talay, Sanem (Istanbul Technical University) | Selker, Ted (Carnegie Mellon University) | Shastri, Lokendra (Infosys Technologies Ltd.) | Smith, Stephen F. (Carnegie Mellon University) | Singh, Satinder (University of Michigan at Ann Arbor) | Srivastava, Siddharth (University of Wisconsin, Madison) | Sukthankar, Gita (University of Central Florida) | Uthus, David C. (Naval Research Laboratory) | Williams, Mary-Anne (University of Technology, Sydney)
The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.
Based on the experience in manufacturing production scheduling problems which the AI community has amassed over the last ten years, a workshop was held to provide a forum for discussion of the issues encountered in the design of AI-based scheduling systems. Several topics were addressed including: the relative virtues of expert system, deep method, and interactive approaches, the balance between predictive and reactive components in a scheduling system, the maintenance of convenient scheduling descriptions, the application of the ideas of chaos theory to scheduling, the state of the art in schedulers which learn, and the practicality and desirability of a set of benchmark scheduling problems. This article expands on these issues, abstracts the papers which were presented, and summarizes the lengthy discussions that took place.
Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevent knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.