Sciences and Technologies, Email: patrick.hung@uoit.ca

Scientists Look at Promise, Peril of Technology. News" collection that can be found--complete'There's a whole generation California and the journal'Science' convened a Odyssey" and wanted to do artificial intelligence. Ten years behind them are "Star Meets Imagination' set a record, attracting Brown, the former chief scientist for the Xerox'You've got to ask, do we "Anyone what these issues are really going to mean to'If we That's the philosophy behind the new £1 faces the same challenge -- how to don't have the right kind of scientific literacy, million Connect science and technology make a story interesting and accessible, all scientific debate becomes ideological.' 'Movies don't have to artificial intelligence.... He says technology, ... The exhibition space has been divided be accurate,' he says.

We are developing Companion Cognitive Systems, a new kind of software that can be effectively treated as a collaborator. Aside from their potential utility, we believe this effort is important because it focuses on three key problems that must be solved to achieve human-level AI: Robust reasoning and learning, interactivity, and longevity. We describe the ideas we are using to develop the first architecture for Companions: analogical processing, grounded in cognitive science for reasoning and learning, sketching and concept maps to improve interactivity, and a distributed agent architecture hosted on a cluster to achieve performance and longevity. We outline some results on learning by accumulating examples derived from our first experimental version.

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.

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. 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. 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.

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 submission deadline is Canada!

Utrecht is more than 1,300 years old and located in the center of the Netherlands, about 40 minutes by train from Amsterdam. School (EASSS 2005) for about 120 students, which was organized by Europe's coordination network for agent systems (AgentLink) and was as successful as previous summer schools in Utrecht, Saarbruecken, Prague, Barcelona, Bologna, and Liverpool. Overall, in the theory and practice of AAMAS 2005 had 778 academic and autonomous agents and multiagent industrial participants from 44 countries systems. AAMAS 2005 is the fourth on six continents. The main room of this can with some justification AAMAS 2005 was held on July building, in which the Treaty of claim to be one of the most active.

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.

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.