Artificial Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An agent-based approach for building complex software systems
Communications of the ACM
Machine Learning
Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2000: Robot Soccer World Cup IV
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
An adaptive plan-based dialogue agent: integrating learning into a BDI architecture
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
On the relationship between MDPs and the BDI architecture
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Commercial applications of agents: lessons, experiences and challenges
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Making a strong business case for multiagent technology
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Decision-making in an embedded reasoning system
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Learning HTN method preconditions and action models from partial observations
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning context conditions for BDI plan selection
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Enhancing the Adaptation of BDI Agents Using Learning Techniques
International Journal of Agent Technologies and Systems
Integrating learning into a BDI Agent for environments with changing dynamics
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
An operational semantics for the goal life-cycle in BDI agents
Autonomous Agents and Multi-Agent Systems
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An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. We describe a novel BDI execution framework that models context conditions as decision trees, rather than boolean formulae, allowing agents to learn the probability of success for plans based on experience. By using a probabilistic plan selection function, the agents can balance exploration and exploitation of their plans. We extend earlier work to include both parameterised goals and recursion and modify our previous approach to decision tree confidence to include large and even non-finite domains that arise from such consideration. Our evaluation on a pre-existing program that relies heavily on recursion and parametrised goals confirms previous results that naive learning fails in some circumstances, and demonstrates that the improved approach learns relatively well.