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.

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

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.

Intelligent-agent technology is one of the most exciting, active areas of research and development in computer science and information technology today. The First Asia-Pacific Conference on Intelligent- Agent Technology (IAT'99) attracted researchers and practitioners from diverse fields such as computer science, information systems, business, telecommunications, manufacturing, human factors, psychology, education, and robotics to examine the design principles and performance characteristics of various approaches in agent technologies and, hence, fostered the cross-fertilization of ideas on the development of autonomous agents and multiagent systems among different domains.

The Bar Ilan Symposia on the Foundations of Artificial Intelligence are a series of research meetings held in Israel every two years. I report here on the sixth meeting, held in June 1999.

Sony has provided a robot platform for research and development in physical agents, namely, fully autonomous legged robots. In this article, we describe our work using Sony's legged robots to participate at the RoboCup-98 legged robot demonstration and competition. Robotic soccer represents a challenging environment for research in systems with multiple robots that need to achieve concrete objectives, particularly in the presence of an adversary. Furthermore, RoboCup offers an excellent opportunity for robot entertainment. We introduce the RoboCup context and briefly present Sony's legged robot. We developed a vision-based navigation and a Bayesian localization algorithm. Team strategy is achieved through predefined behaviors and learning by instruction.

The information it provides a dynamic, real-time environment units resulting from such an analysis in which it is still relatively easy for tasks to be encode a deeper understanding of the timevarying classified, monitored, and assessed. Moreover, scene to be described. They include a commentary system has severe time restrictions spatial relations for the explicit characterization imposed by the flow of the game and is of spatial arrangements of objects as well thus a good test bed for research into real-time as representations of recognized object movements.

The Robot World Cup Soccer Games and Conferences (RoboCup) are a series of competitions and events designed to promote the full integration of AI and robotics research. Following the first RoboCup, held in Nagoya, Japan, in 1997, RoboCup-98 was held in Paris from 2-9 July, overlapping with the real World Cup soccer competition. RoboCup-98 included competitions in three leagues: (1) the simulation league, (2) the real robot small-size league, and (3) the real robot middle-size league. Champion teams were cmunited-98 in both the simulation and the real robot small-size leagues and cs-freiburg (Freiburg, Germany) in the real robot middle-size league. RoboCup-98 also included a Scientific Challenge Award, which was given to three research groups for their simultaneous development of fully automatic commentator systems for the RoboCup simulator league. Over 15,000 spectators watched the games, and 120 international media provided worldwide coverage of the competition.

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan.

Arguably one of the most important types of information processing is the capacity for probabilistic reasoning. The properties of undirectedproDabilistic models represented as symmetric networks have been studied extensively using methods from statistical mechanics (Hertz et aI, 1991). Detailed analyses of these models are possible by exploiting averaging phenomena that occur in the thermodynamic limit of large networks. In this paper, we analyze the limit of large, multilayer networks for probabilistic models represented as directed acyclic graphs. These models are known as Bayesian networks (Pearl, 1988; Neal, 1992), and they have different probabilistic semantics than symmetric neural networks (such as Hopfield models or Boltzmann machines). We show that the intractability of exact inference in multilayer Bayesian networks Inference in Multilayer Networks via Large Deviation Bounds 261 does not preclude their effective use. Our work builds on earlier studies of variational methods (Jordan et aI, 1997).