Case-based planning: viewing planning as a memory task
Case-based planning: viewing planning as a memory task
Unified theories of cognition
Learning plans without a priori knowledge
Adaptive Behavior
Hybrid Intelligent Systems
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Learning procedural knowledge to better coordinate
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Concurrent reactive plans: anticipating and forestalling execution failures
Concurrent reactive plans: anticipating and forestalling execution failures
Learning as abductive deliberations
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
A hybrid architecture combining reactive plan execution and reactive learning
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Planning with iFALCON: Towards A Neural-Network-Based BDI Agent Architecture
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Extending BDI plan selection to incorporate learning from experience
Robotics and Autonomous Systems
A hybrid agent architecture integrating desire, intention and reinforcement learning
Expert Systems with Applications: An International Journal
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This paper presents motivations and current related work in the field of plan learning. Additionally, two approaches that achieve plan learning are presented. The two presented approaches are centred on the BDI framework of agency and have particular focus on plans, which, alongside goals, are the means to fulfil intentions in most pragmatic and theoretical realisations of the BDI framework. The first approach is a hybrid architecture that combines a BDI plan extractor and executor with a generic low-level learner. The second approach uses hypotheses to suggest incremental refinements of a priori plans. Both approaches achieve plan generation that is a result of experiential learning. We conclude by discussing issues related to these two approaches, and from other related work.