Artificial Intelligence
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Autonomous Agents and Multi-Agent Systems
Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2000: Robot Soccer World Cup IV
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Making a strong business case for multiagent technology
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology)
Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology)
2APL: a practical agent programming language
Autonomous Agents and Multi-Agent Systems
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 in BDI multi-agent systems
CLIMA IV'04 Proceedings of the 4th international conference on Computational Logic in Multi-Agent Systems
Enhancing the Adaptation of BDI Agents Using Learning Techniques
International Journal of Agent Technologies and Systems
Extending BDI plan selection to incorporate learning from experience
Robotics and Autonomous 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
Declarative planning in procedural agent architectures
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. In particular, the so-called context conditions of plans, on which the whole model relies for plan selection, are restricted to be boolean formulas that are to be specified at design/implementation time. To address these limitations, we propose a novel BDI programming framework that, by suitably modeling context conditions as decision trees, allows agents to learn the probability of success for plans based on previous execution experiences. By using a probabilistic plan selection function, the agents can balance exploration and exploitation of their plans. We develop and empirically investigate two extreme approaches to learning the new context conditions and show that both can be advantageous in certain situations. Finally, we propose a generalization of the probabilistic plan selection function that yields a middle-ground between the two extreme approaches, and which we thus argue is the most flexible and simple approach.