Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Human Decision-Making Behaviors - An Application to RoboCup Software Agents
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Cobot in LambdaMOO: An Adaptive Social Statistics Agent
Autonomous Agents and Multi-Agent Systems
Autonomous Learning of Stable Quadruped Locomotion
RoboCup 2006: Robot Soccer World Cup X
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Interactively shaping agents via human reinforcement: the TAMER framework
Proceedings of the fifth international conference on Knowledge capture
Combining manual feedback with subsequent MDP reward signals for reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Integrating reinforcement learning with human demonstrations of varying ability
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
JREP: Extending Repast Simphony for JADE Agent Behavior Components
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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In complex application systems, there are typically not only autonomous components which can be represented by agents, but humans may also play a role. The interaction between agents and humans can be learned to enhance the stability of a system. How can agents adopt strategies of humans to solve conflict situations? In this paper, we present a learning algorithm for agents based on communication with humans in conflict situations. The learning algorithm consists of four phases: 1 agents detect a conflict situation, 2 a conversation takes place between a human and agents, 3 agents involved in a conflict situation evaluate the strategy applied by the human, and 4 agents that have interacted with humans apply the best rated strategy in a similar conflict situation. We have evaluated this learning algorithm using a Jade/Repast simulation framework. The evaluation study shows that applying the communication-based approach agents adopted the problem solving strategy which has been applied most frequently by humans. We also developed a data mining-based approach which predicts the behavior patterns of humans while deciding a strategy for solving conflicts. A pilot study demonstrates that the data mining-based approach is less effective than the communication based learning approach.