C4.5: programs for machine learning
C4.5: programs for machine learning
Agents that learn to explain themselves
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
Using decision tree confidence factors for multi-agent control
AGENTS '98 Proceedings of the second international conference on Autonomous agents
The impact of diversity on performance in multi-robot foraging
Proceedings of the third annual conference on Autonomous Agents
Advanced Scout: Data Mining and Knowledge Discovery in NBA Data
Data Mining and Knowledge Discovery
Distributed Intelligent Agents
IEEE Expert: Intelligent Systems and Their Applications
Inducing Cost-Sensitive Trees via Instance Weighting
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Generating Multimedia Presentations for RoboCup Soccer Games
RoboCup-97: Robot Soccer World Cup I
Journal of Artificial Intelligence Research
The RoboCup synthetic agent challenge 97
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Designing comprehensible agents
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Has a consensus NL generation architecture appeared, and is it psycholinguistically plausible?
INLG '94 Proceedings of the Seventh International Workshop on Natural Language Generation
RoboCup 2001: Robot Soccer World Cup V
Discovering tactical behavior patterns supported by topological structures in soccer agent domains
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Logfile Player and Analyzer for RoboCup 3D Simulation
RoboCup 2006: Robot Soccer World Cup X
A Deeper Look at 3D Soccer Simulations
RoboCup 2007: Robot Soccer World Cup XI
Discovering behavior patterns in multi-agent teams
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Behavior recognition and opponent modeling for adaptive table soccer playing
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
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Multi-agent teamwork is critical in a large number of agent applications, including training, education, virtual enterprises and collective robotics. Tools that can help humans analyze, evaluate, and understand team behaviors are becoming increasingly important as well. We have taken a step towards building such a tool by creating an automated analyst agent called ISAAC for post-hoc, off-line agent-team analysis. ISAAC's novelty stems from a key design constraint that arises in team analysis: multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. These heterogeneous team models are automatically acquired via machine learning over teams' external behavior traces, where the specific learning techniques are tailored to the particular model learned. Additionally, ISAAC employs multiple presentation techniques that can aid human understanding of the analyses. This paper presents ISAAC's general conceptual framework, motivating its design, as well as its concrete application in the domain of RoboCup soccer. In the RoboCup domain, ISAAC was used prior to and during the RoboCup'99 tournament, and was awarded the RoboCup scientific challenge award.