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

决策理论规划和学习的先验知识获取和政策解释

• 批准号: 312388-2008
• 负责人: Poupart, Pascal
• 金额: $ 1.82万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=457399
• 关键词: Prior; knowledge; elicitation; policy; explanation

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.

考虑使用痴呆症的老年人或任何其他必须完成相当复杂的任务的老年人的口语管理器，移动机器人控制器，自动监视/提示系统。 由于传感器的嘈杂性（例如，语音识别，嘈杂的声纳）以及系统动作的不确定和相互依存的影响（例如，提示对机器人中的互依赖和嘈杂的运动员的提示对机器人中的嘈杂效果），这种系统的概念尤其具有挑战性。 结果，通常不可能通过手动编码控制策略设计复杂的鲁棒系统。 决策理论计划和学习的领域在开发自动化技术方面取得了重大进步，以生成可以彻底改变下一代计算机系统的强大控制政策。 算法不是直接对策略进行编程，而是用于优化基于系统及其环境的模型或模拟器的策略。 但是，引起指定模型或模拟器所需的领域知识，并验证/解释由此产生的策略是研究社区所忽略的两个主要瓶颈，这阻碍了这种颠覆性技术的采用。 知识的启发和政策解释尤其具有挑战性，因为非技术领域专家倾向于具有部分和不精确的知识，并且通常需要对政策的高级解释，在这些政策中，将技术细节抽象出来以更好地传达直觉。 因此，这项研究的目标是i）设计一般和原则性的技术来激发和编码有关系统，环境和所需政策的部分/不精确域知识，ii）开发算法，这些算法可以利用尽可能多的领域知识来利用尽可能提高可扩展性的知识，以及III），以验证和解释由适当级别的策略来验证和解释专家的一般决策。