The Eleventh Annual AAAI Robot Competition and Exhibition was held at the National Conference on Artificial Intelligence in Edmonton, Alberta, Canada, in August 2002. This article describes each of the events that were held: Robot Challenge, Robot Exhibition, Robot Host, and Robot Rescue.

Robots in the Robot Host competition, part of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002) Mobile Robot Competition faced two challenges: (1) a serving task that was similar to the Hors d'Oeuvres, Anyone? Both tasks required moving carefully among people, politely offering them information or hors d'oeuvres, recognizing when the people are making a request, and answering the request.

The Eighteenth National Conference on Artificial Intelligence (AAAI-2002) Robot Challenge is part of an annual series of robot challenges and competitions. It is intended to promote the development of robot systems that interact intelligently with humans in natural environments. The Challenge task calls for a robot to attend the AAAI conference, which includes registering for the conference and giving a talk about itself. In this article, we review the task requirements, introduce the robots that participated at AAAI-2002 and describe the strengths and weaknesses of their performance.

The AAAI-2002 Robot Exhibition offered robotics researchers a venue for live demonstrations of their current projects. Researchers ranging from undergraduates working on their own to large multilab groups demonstrated robots that performed tasks ranging from improvisational comedy to urban search and rescue. This article describes their entries.

The AAAI-2002 Fall Symposium Series was held Friday through Sunday, 15 to 17 November 2002 at the Sea Crest Conference Center in North Falmouth, Massachusetts. The five symposia in the 2002 Fall Symposia Series were (1) Chance Discovery: The Discovery and Management of Chance Events; (2) Etiquette for Human-Computer Work; (3) Human-Robot Interaction; (4) Intent Inference for Users, Teams, and Adversaries; and (5) Personalized Agents. The highlights of each symposium were presented at a special plenary session. Association for the Advancement of Artificial Intelligence (AAAI) technical reports of most of the symposia will be made available to AAAI members.

In TAC-00, agent designs were primarily centered around designing algorithms a tripod are sometimes bundled with the camera to solve an NPcomplete optimization and sometimes auctioned separately. However, by the second year, it for the next generation of trading agents, became common knowledge that this problem autonomous bidding in simultaneous auctions was tractable for the TAC travel game parameters. During the second year, agent designs focused Simultaneous auctions, which characterize on estimating clearing prices, and some internet sites such as eBay.com, Agent design in and substitutable goods are on offer. Complementary TAC-02, however, cannot be described so succinctly.

This eclectic keepsake provides a sampling of what can be found (with links to the full articles) on the AI Topics web site. Please keep in mind that (1) the mere mention of anything here does not imply any endorsement whatsoever; (2) the excerpt might not reflect the overall tenor of the article; (3) although the articles were initially available online and without charge, few things that good last forever; and (4) the AI in the News collection -- updated, hyperlinked, and archived -- can be found by going to www.aaai.org/aitopics/ html/current.php.

The size and complexity of software and hardware systems have significantly increased in the past years. As a result, it is harder to guarantee their correct behavior. One of the most successful methods for automated verification of finite-state systems is model checking. Most of the current model-checking systems use binary decision diagrams (BDDs) for the representation of the tested model and in the verification process of its properties. Generally, BDDs allow a canonical compact representation of a boolean function (given an order of its variables). The more compact the BDD is, the better performance one gets from the verifier. However, finding an optimal order for a BDD is an NP-complete problem. Therefore, several heuristic methods based on expert knowledge have been developed for variable ordering. We propose an alternative approach in which the variable ordering algorithm gains 'ordering experience' from training models and uses the learned knowledge for finding good orders. Our methodology is based on offline learning of pair precedence classifiers from training models, that is, learning which variable pair permutation is more likely to lead to a good order. For each training model, a number of training sequences are evaluated. Every training model variable pair permutation is then tagged based on its performance on the evaluated orders. The tagged permutations are then passed through a feature extractor and are given as examples to a classifier creation algorithm. Given a model for which an order is requested, the ordering algorithm consults each precedence classifier and constructs a pair precedence table which is used to create the order. Our algorithm was integrated with SMV, which is one of the most widely used verification systems. Preliminary empirical evaluation of our methodology, using real benchmark models, shows performance that is better than random ordering and is competitive with existing algorithms that use expert knowledge. We believe that in sub-domains of models (alu, caches, etc.) our system will prove even more valuable. This is because it features the ability to learn sub-domain knowledge, something that no other ordering algorithm does.

Traditional information retrieval techniques can give poor results on the Web, with its vast scale and highly variable content quality. Recently, however, it was found that Web search results can be much improved by using the information contained in the link structure between pages. The two best-known algorithms which do this are HITS [1] and PageRank [2]. The latter is used in the highly successful Google search engine [3]. The heuristic underlying both of these approaches is that pages with many inlinks are more likely to be of high quality than pages with few inlinks, given that the author of a page will presumably include in it links to pages that s/he believes are of high quality.

Traditional information retrieval techniques can give poor results on the Web, with its vast scale and highly variable content quality. Recently, however, it was found that Web search results can be much improved by using the information contained in the link structure between pages. The two best-known algorithms which do this are HITS [1] and PageRank [2]. The latter is used in the highly successful Google search engine [3]. The heuristic underlying both of these approaches is that pages with many inlinks are more likely to be of high quality than pages with few inlinks, given that the author of a page will presumably include in it links to pages that s/he believes are of high quality.