This article introduces Pyro, an open-source Python robotics toolkit for exploring topics in AI and robotics. We present key abstractions that allow Pyro controllers to run unchanged on a variety of real and simulated robots. We demonstrate Pyro's use in a set of curricular modules. We then describe how Pyro can provide a smooth transition for the student from symbolic agents to real-world robots, which significantly reduces the cost of learning to use robots. Finally we show how Pyro has been successfully integrated into existing AI and robotics courses.

The 2005 Autonomous Agents and Multiagent Systems Conference (AAMAS 2005) was held July 25-29, 2005, at the University of Utrecht, the Netherlands. This report reviews the activities of that conference, including the workshop and tutorial programs, the main conference and poster tracks, the industry paper track, the demonstration track and sponsor demonstration sessions, the invited talks, exhibition, doctoral mentoring program, as well the sponsorship and scholarships activities.

Robotics projects coupled with agent-oriented trends in artificial intelligence education have the potential to make introductory AI courses at liberal arts schools the gateway for a large new generation of AI practitioners. However, this vision's achievement requires programming libraries and low-cost platforms that are readily accessible to undergraduates and easily maintainable by instructors at sites with few dedicated resources. This article presents and evaluates one contribution toward implementing this vision: the RCXLisp library. The library was designed to support programming of the Lego Mindstorms platform in AI courses with the goal of using introductory robotics to motivate undergraduates' understanding of AI concepts within the agent-design paradigm. The library's evaluation reflects four years of student feedback on its use in a liberal-arts AI course whose audience covers a wide variety of majors. To help establish a context for judging RCXLisp's effectiveness this article also provides a sketch of the Mindstormsbased laboratory in which the library is used.

Although the Lego RCX's has helped guide Sony's own choice of Hitachi H8 microcontroller lists at 16 megahertz next-generation AIBO features and software and 32 kilobytes of memory, the overhead support. As for two-legged platforms, the University of the firmware and interpreter yield of Freiburg has already prototyped a about 10 kilobytes and 500 hertz throughput soccer team of Robosapiens running from for a typical user--slightly better with alternative handheld computers.

The Representations for Multimodal Generation Workshop was held on April 23-25, 2005, at Reykjavik University, Reykjavik, Iceland. The overall goal of this workshop is to further the state of research on multimodal generation by enabling (and getting) people in the field to work together on building systems capable of real-time face-to-face dialog with people. This report summarizes the activities and progress of that meeting.

Robotics is a remarkable domain that may be successfully employed in the classroom both to motivate students to tackle hard AI topics and to provide students experience applying AI representations and algorithms to real-world problems. This article uses two example robotics problems to illustrate these themes. We show how the robot obstacle-detection problem can motivate learning neural networks and Bayesian networks. We also show how the robot-localization problem can motivate learning how to build complete solutions based on particle filtering. Since these lessons can be replicated on many low-cost robot platforms they are accessible to a broad population of AI students. We hope that by outlining our educational exercises and providing pointers to additional resources we can help reduce the effort expended by other educators. We believe that expanding handson active learning to additional AI classrooms provides value both to the students and to the future of the field itself.

From a computer science and artificial intelligence perspective, robotics often appears as a collection of disjoint, sometimes antagonistic subfields. The lack of a coherent and unified presentation of the field negatively affects teaching, especially to undergraduates. This article presents an alternative synthesis of the various subfields of AI robotics and shows how these traditional subfields fit into the whole. Finally, it presents a curriculum based on these ideas.

This homework introduces students the material in the course. For the written component to the concept of human/robot interaction of this homework, students have to and learning on a real robot. The students manually calculate a posterior probability of program their AIBOs to play a guessing game the robot's position given a uniform prior distribution by which one player (either the human or the of robot poses in a grid world. AIBO) guesses a sequence of colored markers Mounting a Charging Station. Students use the that the other player (AIBO or human, respectively) object-detection code written in previous makes up ahead of time. The AIBO communicates homework assignments to find a colored bull'seye to the human by a predefined set of and tower beacon. These two landmarks allow the robot to compute the distance and orientation motions. When guessing the colored sequence, of a charging station. The robot needs the AIBO has to reason about the patterns of to search for and then climb onto the charging the colors as well as about the clues given to it station.

We address the problem of learning a symmetric positive definite matrix. The central issue is to design parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Ratherthan treating the most general case, we focus on two key applications that exemplify our methods: Online learning with a simple square loss and finding a symmetric positive definite matrix subject to symmetric linear constraints. The updates generalize the Exponentiated Gradient (EG) update and AdaBoost, respectively: the parameter is now a symmetric positive definite matrix of trace one instead of a probability vector (which in this context is a diagonal positive definite matrix with trace one). The generalized updates use matrix logarithms and exponentials topreserve positive definiteness. Most importantly, we show how the analysis of each algorithm generalizes to the non-diagonal case. We apply both new algorithms, called the Matrix Exponentiated Gradient (MEG) update and DefiniteBoost, to learn a kernel matrix from distance measurements.

The emerging Semantic Web focuses on bringing knowledge representationlike capabilities to Web applications in a Web-friendly way. The ability to put knowledge on the Web, share it, and reuse it through standard Web mechanisms provides new and interesting challenges to artificial intelligence. In this paper, I explore the similarities and differences between the Semantic Web and traditional AI knowledge representation systems, and see if I can validate the analogy "The Semantic Web is to KR as the Web is to hypertext."