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
A standard reference model for intelligent multimedia presentation systems
Computer Standards & Interfaces
Using decision tree confidence factors for multi-agent control
AGENTS '98 Proceedings of the second international conference on Autonomous agents
On the learnability and usage of acyclic probabilistic finite automata
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
The impact of diversity on performance in multi-robot foraging
Proceedings of the third annual conference on Autonomous Agents
Coordinating mobile robot group behavior using a model of interaction dynamics
Proceedings of the third annual conference on Autonomous Agents
Automated assistants to aid humans in understanding team behaviors
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Data Structures and Algorithms
Data Structures and Algorithms
Advanced Scout: Data Mining and Knowledge Discovery in NBA Data
Data Mining and Knowledge Discovery
Using Decision Trees for Agent Modeling: Improving Prediction Performance
User Modeling and User-Adapted Interaction
Building Dynamic Agent Organizations in Cyberspace
IEEE Internet Computing
Distributed Intelligent Agents
IEEE Expert: Intelligent Systems and Their Applications
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Inducing Cost-Sensitive Trees via Instance Weighting
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Designing Comprehensible Agents
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Generating Multimedia Presentations for RoboCup Soccer Games
RoboCup-97: Robot Soccer World Cup I
Dynamic Distributed Resource Allocation: A Distributed Constraint Satisfaction Approach
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
MIKE: An Automatic Commentary System for Soccer
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Execution monitoring in multi-agent environments
Execution monitoring in multi-agent environments
Journal of Artificial Intelligence Research
Computerized Real-Time Analysis of Football Games
IEEE Pervasive Computing
A network of sensor-based framework for automated visual surveillance
Journal of Network and Computer Applications
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
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Hybrid BDI-POMDP framework for multiagent teaming
Journal of Artificial Intelligence Research
Recognizing Team Formations in Multiagent Systems: Applications in Robotic Soccer
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
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
Analysis of multi-agent interactions with process mining techniques
MATES'06 Proceedings of the 4th German conference on Multiagent System Technologies
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Multi-agent teamwork is critical in a large number of agent applications, including training, education, virtual enterprises and collective robotics. The complex interactions of agents in a team as well as with other agents make it extremely difficult for human developers to understand and analyze agent-team behavior. It has thus become increasingly important to develop tools that can help humans analyze, evaluate, and understand team behaviors. However, the problem of automated team analysis is largely unaddressed in previous work. In this article, we identify several key constraints faced by team analysts. Most fundamentally, multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. In addition, effective ways of presenting the analysis to humans is critical and the presentation techniques depend on the model being presented. Finally, analysis should be independent of underlying team architecture and implementation.We also demonstrate an approach to addressing these constraints by building an automated team analyst called ISAAC for post-hoc, off-line agent-team analysis. ISAAC acquires multiple, heterogeneous team models 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. ISAAC also provides feedback on team improvement in two novel ways: (i) It supports principled “what-if” reasoning about possible agent improvements; (ii) It allows the user to compare different teams based on their patterns of interactions. This paper presents ISAAC's general conceptual framework, motivating its design, as well as its concrete application in two domains: (i) RoboCup Soccer; (ii) software agent teams participating in a simulated evacuation scenario. In the RoboCup domain, ISAAC was used prior to and during the RoboCup '99 tournament, and was awarded the RoboCup Scientific Challenge Award. In the evacuation domain, ISAAC was used to analyze patterns of message exchanges among software agents, illustrating the generality of ISAAC's techniques. We present detailed algorithms and experimental results from ISAAC's application.