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Introduction to Reinforcement Learning
Interactive Case-Based Planning for Forest Fire Management
Applied Intelligence
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ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Factored MDP elicitation and plan display
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
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MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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A natural language argumentation interface for explanation generation in Markov decision processes
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
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UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
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ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
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A Markov Decision Process (MDP) policy presents, for each state, an action, which preferably maximizes the expected utility accrual over time. In this article, we present a novel explanation system for MDP policies. The system interactively generates conversational English-language explanations of the actions suggested by an optimal policy, and does so in real time. We rely on natural language explanations in order to build trust between the user and the explanation system, leveraging existing research in psychology in order to generate salient explanations. Our explanation system is designed for portability between domains and uses a combination of domain-specific and domain-independent techniques. The system automatically extracts implicit knowledge from an MDP model and accompanying policy. This MDP-based explanation system can be ported between applications without additional effort by knowledge engineers or model builders. Our system separates domain-specific data from the explanation logic, allowing for a robust system capable of incremental upgrades. Domain-specific explanations are generated through case-based explanation techniques specific to the domain and a knowledge base of concept mappings used to generate English-language explanations.