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
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
Maximizing future options: an on-line real-time planning method
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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Highly dynamic environments with uncertainty make inadequate long or rigid plans, because they are frequently dismissed by the arrival or new unexpected situations. In these environments, most approaches eliminate planning altogether, and evaluate just the current situation. We are interested in on-line planning, where execution and planning are interleaved, and short plans are continuously re-evaluated. Now, the plan evaluation itself could be an important issue. We have proposed in our recent work to evaluate plans taking into account the quantity and quality of future options, not just the single best future option. In this paper we present a detailed evaluation of real-time planning performance, changing the importance given to the current situation, to the best future option, and to the set of future options respective evaluations, in the context of the simulated soccer Robocup competition. Our results show that a well-tuned combination of the mentioned factors could outperform any of them alone.