Iterative Model Refinement of Recommender MDPs Based on Expert Feedback
| Autor: | Pascal Poupart, Omar Zia Khan, John Mark Agosta |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Iterative and incremental development
Computer science business.industry Model parameters Recommender system Parameter space Machine learning computer.software_genre Constraint (information theory) Consistency (database systems) Reinforcement learning Artificial intelligence Markov decision process business computer |
| Zdroj: | Advanced Information Systems Engineering ISBN: 9783642387081 ECML/PKDD (1) |
| DOI: | 10.1007/978-3-642-40988-2_11 |
| Popis: | In this paper, we present a method to iteratively refine the parameters of a Markov Decision Process by leveraging constraints implied from an expert's review of the policy. We impose a constraint on the parameters of the model for every case where the expert's recommendation differs from the recommendation of the policy. We demonstrate that consistency with an expert's feedback leads to non-convex constraints on the model parameters. We refine the parameters of the model, under these constraints, by partitioning the parameter space and iteratively applying alternating optimization. We demonstrate how the approach can be applied to both flat and factored MDPs and present results based on diagnostic sessions from a manufacturing scenario. |
| Databáze: | OpenAIRE |
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