Teams of artificially intelligent planetary rovers have tremendous potential for space exploration, allowing for reduced cost, increased flexibility and increased reliability. However, having these multiple autonomous devices acting simultaneously leads to a problem of coordination: to achieve the best results, the they should work together. Due to the large distances and harsh environments, a rover must be able to perform a wide variety of tasks with a wide variety of potential teammates in uncertain and unsafe environments. Instead, this article examines tackling this problem through the use of coordinated reinforcement learning: rather than being programmed what to do, the rovers iteratively learn through trial and error to take take actions that lead to high overall system return.
Ten Years of AAMAS: Introduction to the Special Issue Liz Sonenberg, Peter Stone, Kagan Tumer, Pinar Yolum Abstract The articles in this special issue have been specifically commissioned to provide a snapshot of current activity in the autonomous agents and multiagent systems communities. The articles in this special issue have been specifically commissioned to provide a snapshot of current activity in the autonomous agents and multiagent systems communities.
Karlgren, Jussi, Kanerva, Pentti, Gamback, Bjorn, Forbus, Kenneth D., Tumer, Kagan, Stone, Peter, Goebel, Kai, Sukhatme, Gaurav S., Balch, Tucker, Fischer, Bernd, Smith, Doug, Harabagiu, Sanda, Chaudri, Vinay, Barley, Mike, Guesgen, Hans, Stahovich, Thomas, Davis, Randall, Landay, James
The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2002 Spring Symposium Series, held Monday through Wednesday, 25 to 27 March 2002, at Stanford University. The nine symposia were entitled (1) Acquiring (and Using) Linguistic (and World) Knowledge for Information Access; (2) Artificial Intelligence and Interactive Entertainment; (3) Collaborative Learning Agents; (4) Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction; (5) Intelligent Distributed and Embedded Systems; (6) Logic-Based Program Synthesis: State of the Art and Future Trends; (7) Mining Answers from Texts and Knowledge Bases; (8) Safe Learning Agents; and (9) Sketch Understanding.