RI: Small: Scaling Up Inference in Dynamic Systems with Logical Structure

随机推理问题自然地出现在许多与世界交互的应用中，如自然语言处理(NLP)、机器人控制、社会网络分析和环境工程。当前现实世界的应用程序需要可扩展到数千个或更多交互的推理机制。该项目正在扩大和提高动态部分可观测领域中的自动推理和学习的精度，并随后应用于决策，通过结合逻辑方法和概率方法的互补计算优势。正在开发的推理和学习算法正在通过理论和实验手段进行评估，目的是改进移动机器人的文本叙述问题回答和环境状态估计。社会效益的例子包括使人们能够访问和检查隐藏在大量文本信息中的细节，以及实现更有帮助和更自主的移动机器人。

Stochastic inference problems arise naturally in many applications that interact with the world, such as natural language processing (NLP), robot control, analysis of social networks, and environmental engineering. Current real-world applications require inference mechanisms that can scale to thousands and more interactions. This project is scaling up and improving precision of automated inference and learning in dynamic partially observable domains, with later application to decision making, by combining the complementary computational strengths of logical and probabilistic methods. The inference and learning algorithms being developed are being assessed by theoretical and experimental means, with aims of improving question answering from text narratives and environment-state estimation for mobile robots. Examples of societal benefits are enabling people to access and examine details hidden in large amounts of textual information as well as enabling more helpful and autonomous mobile robots.