Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.

With the growing importance of multiagent team-work, tools that can help humans analyze, evaluate, and understand team behaviors are also becoming increasingly important. To this end, we are creating isaac, a team analyst agent for post hoc, offline agent-team analysis. ISAAC'S novelty stems from a key design constraint that arises in team analysis: Multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. These heterogeneous team models are automatically acquired by machine learning over teams' external behavior traces, where the specific learning techniques are tailored to the particular model learned. Additionally, ISAAC uses multiple presentation techniques that can aid human understanding of the analyses. This article presents ISAAC'S general conceptual framework and its application in the RoboCup soccer domain, where ISAAC was awarded the RoboCup Scientific Challenge Award.

The global vision system runs at a speed of 35 hertz with a resolution of 320 240. The basic algorithm used is blob to students to prepare them for designing, analysis (Gonzalez and Woods 1992). To determine integrating, and maintaining highly complex the identity of each robot and its orientation, systems. Another objective of the project is to the robots have color patches on top as explore the interplay between AI, dynamics, well as the team color marker (blue or yellow and control theory. This article describes the Ping-Pong ball).

Each award winner and received a B.S. in electrical received a certificate and a check engineering from the Technion Haifa for $2500.

Induction and deduction are two opposite operations in data-mining applications. Induction extracts knowledge in the form of, say, rules or decision trees from existing data, and deduction applies induction results to interpret new data. An intelligent learning database (ILDB) system integrates machine-learning techniques with database and knowledge base technology. It starts with existing database technology and performs both induction and deduction. The integration of database technology, induction (from machine learning), and deduction (from knowledge-based sys-tems) plays a key role in the construction of ILDB systems, as does the design of efficient induction and deduction algorithms. This article presents a system structure for ILDB systems and discusses practical issues for ILDB applications, such as instance selection and structured induction.

As the English striker Gary Lineker famously said, "Football is a very simple game. For 90 minutes, 22 men go running after the ball, and at the end, the Germans win." Although the game is simple, analyzing it can be hard. Just what makes one team better than another? How much difference do tactics make? Is there really such a thing as a "lucky win?" Here, we try to answer these questions in the context of RoboCup. We take the giant set of log data produced by the simulator tournaments from 1997 to 1999 and feed it to a data-munching program that produces statistics on important game features. Using these statistics, we identify precisely what has improved in RoboCup and what still requires further work. Plus, because the data muncher can work in real time, we can also release it as a proxy server for RoboCup. This proxy server gives all RoboCup developers instant access to statistics while a game is in progress and is a promising step toward an important goal: understanding RoboCup.

In practice, by calculating the distance between ball center and robot geometrical center, the robot is commanded to rotate around the ball center. Figure 1 shows a picture of our player robot. Our fast robotics research centers to construct a team of image-processing algorithm can process as robots that could play indoor soccer with many as 16 frames a second and can recognize another team according certain rules and regulations. Our team became done using a wireless network under TCP champion among 21 teams in the middlesize-league (transmission control protocol) protocols. Therefore, player robot, a particular mechanics was we designed a special mechanics that provided designed and implemented that, together with a fast and flexible omnidirectional movement the motor's current feedbacks, to a good extent especially when looking for the ball and dribbling. Therefore, object finding and one castor wheel in the rear.

The Eighth Annual Mobile Robot Competition and Exhibition was held as part of the Sixteenth National Conference on Artificial Intelligence in Orlando, Florida, 18 to 22 July. The goals of these robot events are to foster the sharing of research and technology, allow research groups to showcase their achievements, encourage students to enter robotics and AI fields at both the undergraduate and graduate level, and increase awareness of the field. The 1999 events included two robot contests; a new, long-term robot challenge; an exhibition; and a National Botball Championship for high school teams sponsored by the KISS Institute. Each of these events is described in detail in this article.

The CMUNITED-99 simulator team became the 1999 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a combined score of 110-0. CMUNITED-99 builds on the successful CMUNITED-98 implementation but also improves on it in many ways. This article gives an overview of CMUNITED-99's improvements over CMUNITED-98.

RoboCup is an initiative designed to promote the full integration of AI and robotics research. Following the success of the first RoboCup in 1997 at Nagoya (Kitano 1998; Noda et al. 1998) and the second RoboCup in Paris in 1998, the Third Robot World Cup Soccer Games and Conferences, RoboCup-99, were held in Stockholm from 27 July to 4 August 1999 in conjunction with the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99). There were four different leagues: (1) the simulation league, (2) the small-size real robot league, (3) the middle-size real robot league, and (4) the Sony legged robot league. RoboCup-2000, the Fourth Robot World Cup Soccer Games and Conferences, will take place in Melbourne, Australia, in August 2000.