Computing the minimum Hausdorff distance between two point sets on a line under translation
Information Processing Letters
Clustering seasonality patterns in the presence of errors
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Coach planning with opponent models for distributed execution
Autonomous Agents and Multi-Agent Systems
Know thine enemy: a champion robocup coach agent
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Feature selection for activity recognition in multi-robot domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
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The RoboCup robot soccer Small Size League has been running since 1997 with many teams successfully competing and very effectively playing the games. Teams of five robots, with a combined autonomous centralized perception and control, and distributed actuation, move at high speeds in the field space, actuating a golf ball by passing and shooting it to aim at scoring goals. Most teams run their own pre-defined team strategies, unknown to the other teams, with flexible game-state dependent assignment of robot roles and positioning. However, in this fast-paced noisy real robot league, recognizing the opponent team strategies and accordingly adapting one's own play has proven to be a considerable challenge. In this work, we analyze logged data of real games gathered by the CMDragons team, and contribute several results in learning and responding to opponent strategies. We define episodes as segments of interest in the logged data, and introduce a representation that captures the spatial and temporal data of the multi-robot system as instances of geometrical trajectory curves. We then learn a model of the team strategies through a variant of agglomerative hierarchical clustering. Using the learned cluster model, we are able to classify a team behavior incrementally as it occurs. Finally, we define an algorithm that autonomously generates counter tactics, in a simulation based on the real logs, showing that it can recognize and respond to opponent strategies.