Lloyd Greenwald

Components, Curriculum, and Community: Robots and Robotics in Undergraduate AI Education

This editorial introduction presents an overview of the robotic resources available to AI educators and provides context for the articles in this special issue. We set the stage by addressing the trade-offs among a number of established and emerging hardware and software platforms, curricular topics, and robot contests used to motivate and teach undergraduate AI.

Using Educational Robotics to Motivate Complete AI Solutions

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 hands-on active learning to additional Al classrooms provides value both to the students and to the future of the field itself.

The AAAI 2005 Mobile Robot Competition and Exhibition

The Fourteenth Annual AAAI Mobile Robot Competition and Exhibition was held at the National Conference on Artificial Intelligence in Pittsburgh, Pennsylvania, in July 2005. This year marked a change in the venue format from a conference hall to a hotel, which changed how the robot event was run. As a result, the robots were much more visible to the attendees of the AAAI conference than in previous years. This allowed teams that focused on human-robot interaction to have many more opportunities to interact with people. This article describes the events that were held at the conference, including the Scavenger Hunt, Open Interaction, Robot Challenge, and Robot Exhibition.

The 2004 AAAI Spring Symposium Series

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford Universitys Department of Computer Science, presented the 2004 Spring Symposium Series, Monday through Wednesday, March 22-24, at Stanford University. The titles of the eight symposia were (1) Accessible Hands-on Artificial Intelligence and Robotics Education; (2) Architectures for Modeling Emotion: Cross-Disciplinary Foundations; (3) Bridging the Multiagent and Multirobotic Research Gap; (4) Exploring Attitude and Affect in Text: Theories and Applications; (5) Interaction between Humans and Autonomous Systems over Extended Operation; (6) Knowledge Representation and Ontologies for Autonomous Systems; (7) Language Learning: An Interdisciplinary Perspective; and (8) Semantic Web Services. Each symposium had limited attendance. Most symposia chairs elected to create AAAI technical reports of their symposium, which are available as paperbound reports or (for AAAI members) are downloadable on the AAAI members-only Web site. This report includes summaries of the eight symposia, written by the symposia chairs.

Package routing in transportation networks with fixed vehicle schedules

We consider a special case of a general problem involving the deployment of vehicles to transport packages in transportation networks. In this special case, the schedules of the vehicles are fixed and packages are routed by transferring them between vehicles as these vehicles make stops according to their fixed schedules. We show that this problem is hard and explore approximation algorithms for its solution. In particular, we cast this problem as a multicommodity flow problem with a mixed integerlinear program formulation. We then apply combinatorial optimization techniques based on solving the relaxed linear programming formulation of the problem to obtain provable feasibility and expected performance guarantees, where performance is measured in terms of the sum of the time in transit over all packages. We investigate the sensitivity of the performance guarantees to certain scaling factors and other limitations of this technique.