Normal forms play an important role in computer science. Examples of areas where normal forms have proved fruitful include logic, where normal forms of formulae are used both for the proof of theoretical results and in automated theorem proving, and relational databases [7], where normal forms have been the driving force in the development of database theory and principles of good data modelling. In computer science, usually normal forms are supported by transformations, operational procedures that transform initial objects (such as programs or logical theories) to their normal form. Such transformations are important for two main reasons: 1. They support the understanding and assimilation of new concepts because they allow one to concentrate on certain forms and key features only. Thus transformations can be useful as theoretical tools.

Robotic soccer is an ideal task to demonstrate new techniques and explore new problems. Moreover, problems and solutions can easily be communicated because soccer is a well-known game. Our intention in building a robotic soccer team and participating in RoboCup-98 was, first, to demonstrate the usefulness of the self-localization methods we have developed. Second, we wanted to show that playing soccer based on an explicit world model is much more effective than other methods. Third, we intended to explore the problem of building and maintaining a global team world model. As has been demonstrated by the performance of our team, we were successful with the first two points. Moreover, robotic soccer gave us the opportunity to study problems in distributed, cooperative sensing.

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.

They perform many different tasks, from giving tours to collecting trash. Many have distinguished themselves (usually with first-or second-place finishes) at various indoor and outdoor mobile robot competitions. Each case study is self-contained and includes detailed descriptions of important algorithms, including pseudo-code. Thus this volume serves as a recipe book for the design of successful mobile robot appli cations. Common themes include navigation and mapping, computer vision, and architecture.

We view robotic soccer as an example of a periodic team synchronization (PTS) domain. By perceiving the with no adverse effects on the achievement world, each fully distributed agent builds a of G. Then, based can be thought of as times at which the on a complex set of behaviors, it chooses an team is "offline." In general (that is, when the agents are Although acting autonomously, each agent "online"), the domain is dynamic and real time, contributes to the overall team's goal. Agents receive sensory p at time t.

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.

Although our previous and processes the images, giving the positions team had accurate navigation, it was not easily of each robot and the ball. This information is interruptible, which is necessary for operating sent to an off-board controller and distributed in a highly dynamic environment. The final design includes a battery of inherent mechanical inaccuracies and module supplying three independent unforeseen interventions from other agents. It also includes a single board RoboCup competition in Paris (Stone, Veloso, containing all the required electronic circuitry and Riley 1999; Kitano et al. 1997). These improvements by an array of four infrared sensors, which include a robust low-level control algorithm, which handles a moving target with is enabled or disabled by the software control.

The Workshop on Intelligent Information Integration (III), organized in conjunction with the Sixteenth International Joint Conference on Artificial Intelligence, was held on 31 July 1999 in Stockholm, Sweden. Approximately 40 people participated, and nearly 20 papers were presented. This packed workshop schedule resulted from a large number of submissions that made it difficult to reserve discussion time without rejecting an unproportionately large number of papers. Participants included scientists and practitioners from industry and academia. Topics included query planning, applications of III, mediator architectures, and the use of ontologies for III.

A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonably-sized training sets. Typically such bias is supplied by hand through the skill and insights of experts. In this paper a model for automatically learning bias is investigated. The central assumption of the model is that the learner is embedded within an environment of related learning tasks. Within such an environment the learner can sample from multiple tasks, and hence it can search for a hypothesis space that contains good solutions to many of the problems in the environment. Under certain restrictions on the set of all hypothesis spaces available to the learner, we show that a hypothesis space that performs well on a sufficiently large number of training tasks will also perform well when learning novel tasks in the same environment. Explicit bounds are also derived demonstrating that learning multiple tasks within an environment of related tasks can potentially give much better generalization than learning a single task.

We introduce a temporal model for reasoning on disjunctive metric constraints on intervals and time points in temporal contexts. This temporal model is composed of a labeled temporal algebra and its reasoning algorithms. The labeled temporal algebra defines labeled disjunctive metric point-based constraints, where each disjunct in each input disjunctive constraint is univocally associated to a label. Reasoning algorithms manage labeled constraints, associated label lists, and sets of mutually inconsistent disjuncts. These algorithms guarantee consistency and obtain a minimal network. Additionally, constraints can be organized in a hierarchy of alternative temporal contexts. Therefore, we can reason on context-dependent disjunctive metric constraints on intervals and points. Moreover, the model is able to represent non-binary constraints, such that logical dependencies on disjuncts in constraints can be handled. The computational cost of reasoning algorithms is exponential in accordance with the underlying problem complexity, although some improvements are proposed.