AgentSpeak(L): BDI agents speak out in a logical computable language
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
A methodology and modelling technique for systems of BDI agents
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Belief-desire-intention agent architectures
Foundations of distributed artificial intelligence
JAM: a BDI-theoretic mobile agent architecture
Proceedings of the third annual conference on Autonomous Agents
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Multiagent Systems
Introduction to Multiagent Systems
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
UML Distilled: A Brief Guide to the Standard Object Modeling Language
UML Distilled: A Brief Guide to the Standard Object Modeling Language
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Using the UML 2.0 activity diagram to model agent plans and actions
Proceedings of the fourth 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)
UML 2.0 and agents: how to build agent-based systems with the new UML standard
Engineering Applications of Artificial Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Jason induction of logical decision trees: a learning library and its application to commitment
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Combining adaptive goal-driven agents with mixed multi-unit combinatorial auctions
Proceedings of the 13th International Conference on Computer Systems and Technologies
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Dealing with changing situations is a major issue in building agent systems. When the time is limited, knowledge is unreliable, and resources are scarce, the issue becomes more challenging. The BDI (Belief-Desire-Intention) agent architecture provides a model for building agents that addresses that issue. The model can be used to build intentional agents that are able to reason based on explicit mental attitudes, while behaving reactively in changing circumstances. However, despite the reactive and deliberative features, a classical BDI agent is not capable of learning. Plans as recipes that guide the activities of the agent are assumed to be static. In this paper, an architecture for an intentional learning agent is presented. The architecture is an extension of the BDI architecture in which the learning process is explicitly described as plans. Learning plans are meta-level plans which allow the agent to introspectively monitor its mental states and update other plans at run time. In order to acquire the intricate structure of a plan, a process pattern called manipulative abduction is encoded as a learning plan. This work advances the state of the art by combining the strengths of learning and BDI agent frameworks in a rich language for describing deliberation processes and reactive execution. It enables domain experts to specify learning processes and strategies explicitly, while allowing the agent to benefit from procedural domain knowledge expressed in plans.