Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Gaining insight through case-based explanation
Journal of Intelligent Information Systems
Factored MDP elicitation and plan display
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Generating Explanations Based on Markov Decision Processes
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
ACM Transactions on Interactive Intelligent Systems (TiiS)
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A Markov Decision Process (MDP) policy presents, for each state, an action, which preferably maximizes the expected reward accrual over time. In this paper, we present a novel system that generates, in real time, natural language explanations of the optimal action, recommended by an MDP while the user interacts with the MDP policy. 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 for the end user. 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 policy-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 for our natural language model.