An Explanation-based Reinforcement Learning Approach

该项目提出了一种新的强化学习（RL）算法，该算法由基于知识的用户反馈指导。该项目使用结构化知识库将用户反馈集成到传统的RL模型中。该项目的主要目标是减少终端用户和强化学习模型之间持续的知识驱动对话的障碍，提高少数学习能力（从更少的例子中概括的能力）。核心研究问题是：-由终端用户反馈支持的知识库驱动的强化学习（RL）方法能否提供少数学习能力？-哪些知识表示模型和形式主义可以支持泛化和用户反馈过程？如何使用语义解析方法来支持最终用户与知识库的交互？这项研究可以影响提供更透明的人工智能（AI）系统的能力，并且可以从更少的例子中进行概括。这两个属性是在需要信任的情况下（例如健康和法律的领域）应用AI的要求的中心。这项研究位于EPSRC“数据到知识”的优先领域。

This project proposes a novel reinforcement learning (RL) algorithm which is guided by a knowledge-based user feedback. The project integrates user feedback using structured knowledge bases into the traditional RL models. The main objective of the project is to reduce the barriers for a continuous, knowledge-driven dialogue between end-users and RL models, improving few-shot learning capabilities (the ability to generalize from fewer examples).The core research questions are:- Can a Knowledge Base driven reinforcement learning (RL) approach supported by end-user feedback deliver few-shot learning capabilities?- Which Knowledge Representation models and formalisms can support generalization and user feedback process?- How semantic parsing methods can be used to support the end-user interaction with the knowledge base?The research can impact the ability to deliver Artificial Intelligence (AI) systems which are more transparent, and which generalize from fewer examples. These two properties are at the center of the requirements for the application of AI within scenarios which require trust (e.g. health and legal domains).This research is positioned within the EPSRC `data to knowledge' priority area.