We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users' current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%.

(MDAI 2007).

The computer The articles collected for this special 82-85. "Lexington is the home of Massachusetts Bolitho and Dr. Martin Klein, who set a superintelligent electronic'brain' collage in 1956. Please note that: (1) an the machine to music, emphasized that which reduces to a bare minimum the human excerpt may not reflect the overall tenor of their accomplishment was'an experiment element in the complex problem of the article, nor contain all of the relevant of mathematical importance only.' It is tracking and destroying an attacking airplane. Science News more popularly known -- can sight the approach - Jon Glick, Webmaster, AI TOPICS Letter. "A gambling of an attacker, compute its course, As is now well known, in operation at the Bell Telephone Laboratories, kill.

Most, but not all, of the clues and answers relate to AI. An answer may be an acronym or an abbreviation even though not noted in the clue. Some liberties have been taken, but only because the puzzle is meant to be fun and interesting.

We Papers submitted to the II track should For a complete list of deadlines, program are delighted to announce this permanent highlight synergistic effects of integrating information, and to check for change as we expand the venue components from distinct areas further updates, please visit the AAAIfor the conference throughout the of AI to achieve intelligent behavior.

The celebration then moved to web and integrated intelligence, as on Artificial Intelligence and Boston where a huge turnout of AAAI well as the nectar and senior member the Nineteenth Innovative Applications fellows--from founding luminaries to papers, is a significant factor in this of Artificial Intelligence Conference 2006 fellow inductees--reported a trend." Senior member papers are a commemorated fifty years of great weekend meeting prior to the way to collect reflections about areas artificial intelligence research in AAAI conference full of discussions of work by leaders in the field.

The special tracks chair was Barry O'Sullivan, FLAIRS Conference (FLAIRS-19) was Reversals via Representational Refinements"; held 11-13 May 2006 at the Crowne Bob Morris of the NASA Ames The 20th International FLAIRS Conference (FLAIRS-20) will be held May 7 - 9, 2007 at the Casa Marina Resort, which is directly on the beach in Key West, Florida, USA. FLAIRS-20 will feature technical papers, special tracks, and invited speakers on artificial intelligence. The conference is hosted by the Florida Artificial Intelligence Research General Chair Society, in cooperation with AAAI. Geoff Sutcliffe In addition to the general conference, FLAIRS offers numerous special conference tracks. Special tracks provide researchers in focused areas the opportunity to meet and present University of Miami their work.

The Workshop program of the Twenty-First Conference on Artificial Intelligence was held July 16-17, 2006 in Boston, Massachusetts. The program was chaired by Joyce Chai and Keith Decker. The titles of the 17 workshops were AIDriven Technologies for Service-Oriented Computing; Auction Mechanisms for Robot Coordination; Cognitive Modeling and Agent-Based Social Simulations, Cognitive Robotics; Computational Aesthetics: Artificial Intelligence Approaches to Beauty and Happiness; Educational Data Mining; Evaluation Methods for Machine Learning; Event Extraction and Synthesis; Heuristic Search, Memory- Based Heuristics, and Their Applications; Human Implications of Human-Robot Interaction; Intelligent Techniques in Web Personalization; Learning for Search; Modeling and Retrieval of Context; Modeling Others from Observations; and Statistical and Empirical Approaches for Spoken Dialogue Systems.

The development of improved languages and machines was essential. He offered tribute to many early pioneering activities such as J. C. R. Lickleiter developing time-sharing, Nat Rochester designing IBM computers, and Frank Rosenblatt working with Trenchard More was sent to the summer project for two separate weeks by the University of Rochester. Marvin Minsky, Claude Shannon, and never liked the use of "artificial" or It is interesting to speculate informed future work. The attendees of AI was launched not by agreement Dating the beginning of any movement did not come at the same time on methodology or choice of problems is difficult, but the Dartmouth and most kept to their own research or general theory, but by the Summer Research Project of 1956 is agenda. McCarthy emphasized that shared vision that computers can be often taken as the event that initiated nevertheless there were important research made to perform intelligent tasks.

This article is my personal account on the work at Stanford on Stanley, the winning robot in the DARPA Grand Challenge. Between July 2004 and October 2005, my then-postdoc Michael Montemerlo and I led a team of students, engineers, and professionals with the single vision of claiming one of the most prestigious trophies in the field of robotics: the DARPA Grand Challenge (DARPA 2004). The Grand Challenge, organized by the U.S. government, was unprecedented in the nation's history. It was the first time that the U.S. Congress had appropriated a cash price for advancing technological innovation. My team won this prize, competing with some 194 other teams. Stanley was the fastest of five robotic vehicles that, on October 8, 2005, successfully navigated a 131.6-mile-long course through California's Mojave Desert. This essay is not about the technology behind our success; for that I refer the interested reader to recent articles on the technical aspects of Stanley. Instead, this is my personal story of leading the Stanford Racing Team. It is the story of a team of people who built an autonomous robot in record time. It is also a success story for the field of artificial intelligence, as Stanley used some state of the art AI methods in areas such as probabilistic inference, machine learning, and computer vision. Of course, it is also the story of a step towards a technology that, one day, might fundamentally change our lives.