I developed what I scored, and those with the lowest scores could be removed before running C4.5 to build a decision tree with the remainder. I ran an experiment in which I removed the bottom 10 percent of the instances in a University of California, Irvine (UCI) data set. The resulting tree was smaller and more accurate (as measured by 10-fold CV) than the tree built on the full data set. Then I removed the bottom 20 percent of the instances and got a tree that was smaller than the last one and just as accurate. At that point I had the feeling that this was going to make a great paper for the International Conference on Machine Learning (ICML).

This was a large applied research effort that presented many opportunities for unusual experiences. In one such experience, I was called in, at the last minute, to help improve our ALV proposal. The proposal was a 300-page document that segued smoothly from problem description to corporate capabilities and managerial plan, omitting any mention of technical approach. This taught me a rule of thumb I have seen validated many times: the larger the project (in dollars and scope), the poorer the technical proposal. In a second experience, I was demonstrating a dynamic programming algorithm at a quarterly review.

In fact, DARPA, which funded the project, ways. Elves) (Scerri, Pynadath, and Tambe 2002; Finally, we will present some lessons Pynadath and Tambe 2003) and required learned and recent research that was motivated detailed information about the calendars by our experiences in deploying the of people using the system. Thus, we decided to deploy a new application of the Electric The Travel Elves introduced two major Elves, called the Travel Elves. This application advantages over traditional approaches to appeared to be ideal for wider deployment travel planning. First, the Travel Elves provided since it could be hosted entirely outside an interactive approach to making an organization and communication travel plans in which all of the data could be performed over wireless devices, required to make informed choices is such as cellular telephones. For example, when The mission of the Travel Elves (Ambite deciding whether to park at the airport or et al. 2002, Knoblock 2004) was to facilitate take a taxi, the system compares the cost planning a trip and to ensure that the of parking and the cost of a taxi given other resulting travel plan would execute selections, such as the airport, the specific smoothly. Initial deployment of the Travel parking lot, and the starting location Elves at DARPA went smoothly.

There have been many erroneous pre-scientific and common sense inductions. We want to understand why people believe in wrong theories. Our hypothesis is that mistaken inductions are due not only to the lack of facts, but also to the poor description of existing facts and to implicit knowledge which is transmitted socially. This paper presents several experiments the aim of which is to validate this hypothesis by using machine learning and data mining techniques to simulate the way people build erroneous theories from observations.

We hope you are planning to join us for AAAI-08 and IAAI-08 in Chicago, Illinois, July 13-17, 2008. The AAAI-08 program will feature Eric Horvitz's cal papers will be highlighted as The program will include a research AAAI presidential address, as well as exceptional papers during the conference-wide track, industry track, invited speakers, five outstanding invited talks. Registration information invited speakers include Alexei A. Efros July 16, and another 23 short and other program details will (Carnegie Mellon University) whose papers will be presented as posters. Using Lots of Data to Infer Geometric, and awards will continue for its aiide08.php Please Photometric and Semantic Scene Properties second year with all the Hollywood send inquiries to aiide08@aaai.org

(ESAS 2008).

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 was not quite able to 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 learned a lot, had fun, and are trying again with a new company. To others who aspire to commercialize their AI technology, I say: ";;Take a chance!";;

This special issue of AI Magazine is dedicated to the proposition that problems populate the path to insight, implying the experiences and lessons learned should be shared.

Technically the underlying optimization development of online commerce forced problem is either NP or P-space businesses to question the week-plus supply-chain complete (depending on the details of the planning cycles that had been domain). Furthermore, the problem mixes the norm. Finally, the year 2000 (Y2K) a dozen or so classic optimization problems problem caused an across-the-board from AI and operations research (OR), replacement of enterprise software, allowing and much of the expected savings from many businesses to update their global supply-chain optimization are lost if approach to supply-chain planning. The end result of all of these factors was This article describes our experience a huge upswing in demand for supplychain from four years of solving supply-chain planning tools from i2 Technologies planning and optimization problems and other vendors. When I joined i2 in across industries, and some of the lessons 1996 as optimization architect, the company we learned.

Narrowly focused task and domain specific AI has been applied successfully for more than twenty five years, and has produced immense value in industry and government. It doesn’t lead directly to artificial general intelligence (AGI), but it does have real problem solving value. It is useful to note that many of the reasons why some otherwise meritorious AI applications fail have nothing to do with the AI per se, but rather, with systems engineering and organizational issues. For example: the domain expert is pulled out to work on more critical projects; the application champion rotates out of his/her position; or the sponsor changes priorities. A system may not make it past an initial pilot test for logistical vs. substantive technical reasons. Some embedded AI systems may work well for years on a software platform that is orphaned and porting it would be prohibitively expensive. A system may work well in a pilot test, but it might not scale for huge numbers of users without extensive performance optimization. The core AI system may be great but the user interface could be suboptimal. The delivered application system might work well, but it could be hard to maintain internally. The system may work according to the sponsor’s requirements, but it might not be applied to the part of the problem that delivers the largest economic results; or the system might not produce enough visible organizational benefits to protect it in subsequent budget battles. Alternatively, the documented results may be quite strong, but may not be communicated effectively across organizational boundaries. All software projects are vulnerable to one or more of these problems. The fact that some software projects have a relatively small percentage of their total code in embedded AI methods doesn’t make them an exception. However, knowing about these potential problems could help AI project teams to be proactive about avoiding them whenever possible.