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Artificial Intelligence
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Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
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The Belief-Desire-Intention Model of Agency
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Learning from observation using primitives
Learning from observation using primitives
Evolving models from observed human performance
Evolving models from observed human performance
Building intelligent collaborative interface agents with the ICAGENT development framework
Autonomous Agents and Multi-Agent Systems
Robust recognition of physical team behaviors using spatio-temporal models
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Multiagent learning is not the answer. It is the question
Artificial Intelligence
Collaborative context-based reasoning
Collaborative context-based reasoning
Automatic annotation of team actions in observations of embodied agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Confidence-based policy learning from demonstration using Gaussian mixture models
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
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Expert Systems with Applications: An International Journal
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Reinforcement learning for robot soccer
Autonomous Robots
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Agents with shared mental models for enhancing team decision makings
Decision Support Systems - Special issue: Intelligence and security informatics
Autonomous Agents and Multi-Agent Systems
Social-based planning model for multiagent systems
Expert Systems with Applications: An International Journal
Towards Addressing Model Uncertainty: Robust Execution-Time Coordination for Teamwork
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Discovery of High-Level Behavior From Observation of Human Performance in a Strategic Game
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Building High-Performing Human-Like Tactical Agents Through Observation and Experience
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A framework for Multi-Agent Based Clustering
Autonomous Agents and Multi-Agent Systems
An Interaction Protocol for Mutual Assistance in Agent Teamwork
CISIS '12 Proceedings of the 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)
Observations as a Method to Evaluate a Computer-Based Approach to Learning Algorithms
ICALT '12 Proceedings of the 2012 IEEE 12th International Conference on Advanced Learning Technologies
Generating useful test data for complex linked employer-employee datasets
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
Teaching and leading an ad hoc teammate: Collaboration without pre-coordination
Artificial Intelligence
A syntactic approach to robot imitation learning using probabilistic activity grammars
Robotics and Autonomous Systems
Hi-index | 12.05 |
This paper describes an approach to creating a simulated team of agents through observation of another team performing a collaborative task. Simulated human teamwork can be used for a number of purposes, such as automated teammates for training purposes and realistic opponents in games as well as in military training simulation. Current simulated teamwork representations require that the team member behaviors be manually programmed into the agents, often requiring much time and effort. None of the currently documented techniques for multi-agent learning employ observational learning and a context-aware framework to automatically build agents that replicate the collaborative behaviors observed. Machine learning techniques for learning from observation and learning by demonstration have proven successful at observing the behavior of humans or other software agents and creating a behavior function for a single agent. This technique described here known as COLTS combines current research in teamwork simulation and learning from observation to effectively train a multi-agent system capable of displaying effective team behavior in limited domains. The paper describes the background and the related work by others as well as a detailed description of the learning method. A prototype built to evaluate the developed approach as well as the extensive experimentation conducted is also described. The results indicate success in the selected experiments.