Reports of the Workshops of the Thirty-First AAAI Conference on Artificial Intelligence

The AAAI-17 workshop program included 17 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 4-5, 2017 at the Hilton San Francisco Union Square in San Francisco, California, USA. This report contains summaries of 12 of the workshops, and brief abstracts of the remaining 5

In cooperative robot teams, communications can speed up execution, reduce duplication, and prevent interference. Although many systems use explicit communications, persistent intra-team digital communications is not guaranteed. One approach to this challenge is to use implicit communication to infer state rather than using digital messages. We investigate using implicit communication in the form of observation to infer state to coordinate a robot team in a coverage task. We demonstrate how pruning and backtracking a search tree can improve multi-robot coverage. Experiments were conducted to compare team performance of a robot team using observation-based cooperation to one that uses explicit communications.

The 19th robotics program at the annual AAAI conference was held in Atlanta, Georgia in July 2010. In this article we give a summary of three components of the exhibition: small scale manipulation challenge: robotic chess; the learning by demonstration challenge, and the education track. We also describe the participating teams, highlight the research questions they tackled and briefly describe the systems they demonstrated.

This year, the Robotics Exhibition included two such robotics challenge problems: manipulation and learning by demonstration. In the Small-Scale Manipulation Challenge four teams demonstrated systems playing robotic chess. This exhibit was organized by David Touretzky and Monica D. Anderson. In the Learning by Demonstration Challenge, three teams demonstrated systems learning a block-sorting task. This exhibit was organized by Sonia Chernova. Additionally, this year marked another successful turnout for the Robotics Education Track, organized by Zachary Dodds, which highlights student-and educator-led robotics projects. In this article we give a summary of these three components of the exhibition.

For example, baby boomers are aging. Researchers are actively pursuing interdisciplinary research that enables robots to function autnomously within arbitrary environments alongside people. The goal of the AAAI 2010 Workshop on Enabling Intelligence through Middleware was to examine both the successes and opportunities to provide tools that enable a larger pool of researchers to experiment with embodied, intelligent algorithms. The half-day workshop, attended by over 80 people, was held as part of the Twenty-Fourth AAAI Conference on Artificial Intelligence in Atlanta Georgia on July 12, 2010. The workshop consisted of two parts: (1) invited talks and (2) middleware presentations.

The AAAI 2008 Workshop on Mobility and Manipulation (held during the Twenty-Third AAAI Conference on Artificial Intelligence) showcased advances in mobility and manipulation through a half-day workshop and an exhibition. The workshop focused on possible solutions to both technical and organizational challenges to mobility and manipulation research. This article presents the highlights of that discussion along with the content of the accompanying exhibits.

Moving toward true robot autonomy may require new paradigms, hardware, and ways of thinking. The goal of the AAAI 2008 Workshop on Mobility and Manipulation was not only to demonstrate current research successes to the AAAI community but also to road-map future mobility and manipulation challenges that create synergies between artificial intelligence and robotics. The half-day workshop included both a session on the exhibits and a panel discussion. The panel consisted of five prominent researchers who led a discussion of future directions for mobility and manipulation research. Andrew Ng of Stanford University (along with students Ashutosh Saxena and Ellen Klingbeil) focuses on opening arbitrary doors through learning a few visual keypoints, such as the location and type of door handle.