ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Analogical learning in a turn-based strategy game
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The fun begins with retrieval: explanation and CBR
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Multi-agent case-based reasoning for cooperative reinforcement learners
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Imitating inscrutable enemies: learning from stochastic policy observation, retrieval and reuse
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Integrated learning for goal-driven autonomy
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Agents commonly reason and act over extended periods of time. In some environments, for an agent to solve even a single problem requires many decisions and actions. Consider a robot or animat situated in a real or virtual world, acting to achieve some distant goal; or an agent that controls a sequential process such as a factory production line; or a conversational diagnostic system or recommender system. Equally, over its life time, a long-lived agent will make many decisions and take many actions, even if each problem-solving episode requires just one decision and one action. In spam detection, for example, each incoming email requires a single classification decision before it moves to its designated folder; but continuous operation requires numerous decisions and actions.