Artificial Intelligence (IAAI-08) will be system.

As a result, all sessions attracted significant interest and participation. After the success of this event, the OPS organizing committee plans to merge this event as a regular special session to the AIIDE conference including recognized keynotes, technical discussion, and, possibly, demo sessions. An additional (Maersk Institute, University of Southern aim of these events is to yield a better Denmark). To learn approaches for optimizing player satisfaction about the latest news about this series in interactive entertainment of events, subscribe to the Google systems. This was the second in parallel to the conference.

The conference time was dominated by academic presentations. Attempts to solicit papers from industry were largely unsuccessful; most industry AI people do not have the time or experience to write papers that will survive a rigorous review process. AIIDE'07, we opted for 10 invited It featured 10 (!) invited The new format digital entertainment systems seems to have met with universal with an emphasis on commercial computer approval. Historically This year we added a collocated there has been a disconnect between workshop, the second annual Workshop the work done in academe and that on Optimizing Player Satisfaction. The interacting, so that the academics The invited talks are always the conference series began in 2005, but it can better understand the highlight of AIIDE, especially this year.

The AAAI-07 workshop program was held Sunday and Monday, July 22-23, in Vancouver, British Columbia, Canada. The program included the following thirteen workshops: (1) Acquiring Planning Knowledge via Demonstration; (2) Configuration; (3) Evaluating Architectures for Intelligence; (4) Evaluation Methods for Machine Learning; (5) Explanation-Aware Computing; (6) Human Implications of Human-Robot Interaction; (7) Intelligent Techniques for Web Personalization; (8) Plan, Activity, and Intent Recognition; (9) Preference Handling for Artificial Intelligence; (10) Semantic e-Science; (11) Spatial and Temporal Reasoning; (12) Trading Agent Design and Analysis; and (13) Information Integration on the Web.

Horvitz noted two emerging trends at the conference and in the AI field. Second is the work in scaling AI to be more integrative. Instead of the ongoing great successes of AI researches on "wedges" of AI expertise and reasoning, there's increasing work on delivering more depth and breadth of capabilities such as sensing, learning, and reasoning. "This is very hard," notes Horvitz, "(but already) I see bits and pieces here and there." Game Playing Competition, the Poker AAI's Twenty-second Conference (AAAI-07) continued a longstanding the 1,025 attendees to choose Competition, and the Human Versus tradition of excellence.

Marvin Lee Minsky, a founder of the field of artificial intelligence and professor at MIT, celebrated his 80th birthday on August 9, 2007. This article seizes an opportune time to honor Marvin and his contributions and influence in artificial intelligence, science, and beyond. The article provides readers with some personal insights of Minsky from Danny Hillis, John McCarthy, Tom Mitchell, Erik Mueller, Doug Riecken, Aaron Sloman, and Patrick Henry Winston -- all members of the AI community that Minsky helped to found. The article continues with a brief resume of Minsky's research, which spans an enormous range of fields. It concludes with a short biographical account of Minsky's personal history.

John McCarthy's contributions to computer science and artificial intelligence are legendary. He invented Lisp, made substantial contributions to early work in timesharing and the theory of computation, and was one of the founders of artificial intelligence and knowledge representation. This article, written in honor of McCarthy's 80th birthday, presents a brief biography, an overview of the major themes of his research, and a discussion of several of his major papers.

This article presents some fundamental ideas about representing knowledge and dealing with meaning in computer representations. I will describe the issues as I currently understand them and describe how they came about, how they fit together, what problems they solve, and some of the things that the resulting framework can do. The ideas apply not just to graph-structured "node-and-link" representations, sometimes called semantic networks, but also to representations referred to variously as frames with slots, entities with relationships, objects with attributes, tables with columns, and records with fields and to the classes and variables of object-oriented data structures. I will start by describing some background experiences and thoughts that preceded the writing of my 1975 paper, "What's in a Link," which introduced many of these issues. After that, I will present some of the key ideas from that paper with a discussion of how some of those ideas have matured since then. Finally, I will describe some practical applications of these ideas in the context of knowledge access and information retrieval and will conclude with some thoughts about where I think we can go from here.

Automated planning technology has become mature enough to be useful in applications that range from game-playing to control of space vehicles. In this article, Dana Nau discusses where automated-planning research has been, where it is likely to go, where he thinks it should go, and some major challenges in getting there. The article is an updated version of Nau's invited talk at AAAI-05 in Pittsburgh, Pennsylvania.

In the reverse direction, artificial intelligence brings a fresh perspective to some of the questions addressed by social choice. From a computational perspective, may not be feasible. The agent wants a cheap, we can look at how computationally we low-mileage Ferrari, but no such car exists. As we shall see later in may therefore look for the most preferred outcome this article, computational intractability may among those that are feasible. With multiple actually be advantageous in this setting. For agents, their goals may be conflicting. We may therefore look for the outcome an election is possible in theory, but computationally that is most preferred by the agents. Preferences difficult to perform in practice. From a are thus useful in many areas of artificial representational perspective, we can look at intelligence including planning, sche dhow we represent preferences, especially when uling, multiagent systems, combinatorial auctions, the number of outcomes is combinatorially and game playing.