Prior knowledge elicitation and policy explanation for decision-theoretic planning and learning

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Consider spoken-dialogue managers, mobile robot controllers, automated monitoring/prompting systems for seniors with dementia or any other complex system that must accomplish a fairly complicated task. The conception of such systems is particularly challenging due to the noisy nature of the sensors (e.g., noisy speech recognition, noisy sonars) as well as the uncertain and interdependent effects of system actions (e.g., uncertain effect of prompts on seniors, interdependent and noisy motor controls in robotics). As a result, it is generally impossible to design complex robust systems by hand coding control policies. The fields of decision-theoretic planning and learning have made significant advances in the development of automated techniques to generate robust control policies that could revolutionize the next generation of computer systems. Instead of programming a policy directly, an algorithm is used to optimize a policy based on a model or simulator of the system and its environment. However, eliciting the domain knowledge necessary to specify a model or simulator, and validating/explaining the resulting policy are two major bottlenecks ignored by the research community that are holding back the adoption of this disruptive technology. Knowledge elicitation and policy explanation are particularly challenging since non-technical domain experts tend to have partial and imprecise knowledge, and often need high-level explanations of the policy where technical details are abstracted away to better convey the intuition. Hence, the objectives of this research are i) to design general and principled techniques to elicit and encode partial/imprecise domain knowledge about the system, the environment and the desired policy, ii) to develop algorithms that can exploit as much domain knowledge as possible to improve scalability, and iii) to create generic tools to validate and explain the decisions made by a policy at an appropriate level for developers and non-technical experts.