JAM: a BDI-theoretic mobile agent architecture
Proceedings of the third annual conference on Autonomous Agents
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
Integrated cognitive architectures: a survey
Artificial Intelligence Review
A hybrid agent architecture integrating desire, intention and reinforcement learning
Expert Systems with Applications: An International Journal
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This paper presents a model of a learning mechanism for situated agents. The learning is described explicitly in terms of plans and conducted as intentional actions within the BDI (Beliefs, Desires, Intentions) agent model. Actions of learning direct the task-level performance towards improvements or some learning goals. The agent is capable of modifying its own plans through a set of actions on the run. The use of domain independent patterns of actions is introduced as a strategy for constraining the search for the appropriate structure of plans. The model is demonstrated to represent Q-learning algorithm, however different variation of pattern can enhance the learning.