Bonasso, R. Peter, Dean, Thomas
This article is the content of an invited talk given by the authors at the Thirteenth National Conference on Artificial Intelligence (AAAI-96). The piece begins with a short history of the competition, then discusses the technical challenges and the political and cultural issues associated with bringing it off every year. We also cover the science and engineering involved with the robot tasks and the educational and commercial aspects of the competition. We finish with a discussion of the community formed by the organizers, participants, and the conference attendees.
Doyle, Jon, Dean, Thomas
The Association for the Advancement of Artificial Intelligence held its 1996 Spring Symposia Series on March 27 to 29 at Stanford University. This article contains summaries of the eight symposia that were conducted: (1) Acquisition, Learning, and Demonstration: Automating Tasks for Users; (2) Adaptation, Coevolution, and Learning in Multiagent Systems; (3) Artificial Intelligence in Medicine: Applications of Current Technologies; (4) Cognitive and Computational Models of Spatial Representation; (5) Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature; (6) Computational Issues in Learning Models of Dynamic Systems; (7) Machine Learning in Information Access; and (8) Planning with Incomplete Information for Robot Problems.
Dean, Thomas, Bonasso, R. Peter
The first Robotics Exhibition and Competition sponsored by the Association for the Advancement of Artificial Intelligence was held in San Jose, California, on 14-16 July 1992 in conjunction with the Tenth National Conference on AI. This article describes the history behind the competition, the preparations leading to the competition, the threedays during which 12 teams competed in the three events making up the competition, and the prospects for other such competitions in the future.
Dean, Thomas | Kanazawa., Keiji
The robot's causal theory consists of two distinct types of rules, which we will refer to as projection rules and persistence rules. Even if a robot is able to consult a clock in order to verify the exact time of occurrence of an observed event, most information the robot is given is imprecise (e.g., a client states that a truck will pick up an order at around noon, One of the most important sources of uncertainty involves predicting how long a condition lasts once it becomes true (i.e., how long an observed That is exactly what we do here. In our implementation, we consider only two types of survivor functions: exponential decay functions and piecewise linear functions. Piecewise linear functions are described in Appendix 11.