Intelligent scheduling
Using decision tree confidence factors for multi-agent control
AGENTS '98 Proceedings of the second international conference on Autonomous agents
AI Magazine
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Towards Modeling Other Agents: A Simulation-Based Study
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
RoboCup 2000: Robot Soccer World Cup IV
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Effective redundant constraints for online scheduling
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Layered learning in multiagent systems
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Assessing the value of future and present options in real-time planning
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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In highly dynamic environments with uncertainty the elaboration of long or rigid plans is useless because the constructed plans are frequently dismissed by the arrival or new unexpected situations; in these cases, a “second-best” plan could rescue the situation. We present a new real-time planning method where we take into consideration the number and quality of future options of the next action to choose, in contrast to most planning methods that just take into account the intrinsic value of the chosen plan or the maximum valued future option. We apply our method to the Robocup simulated soccer competition, which is indeed highly dynamic and involves uncertainty. We propose a specific architecture for implementing this method in the context of a player agent in the Robocup competition, and we present experimental evidence showing the potential of our method.