Although there have been approaches that are capable of learning action models from plan traces, there is no work on learning action models from textual observations, which is pervasive and much easier to collect from real-world applications compared to plan traces. In this paper we propose a novel approach to learning action models from natural language texts by integrating Constraint Satisfaction and Natural Language Processing techniques. Specifically, we first build a novel language model to extract plan traces from texts, and then build a set of constraints to generate action models based on the extracted plan traces. After that, we iteratively improve the language model and constraints until we achieve the convergent language model and action models. We empirically exhibit that our approach is both effective and efficient.
Active Jammer Localization via Acquisition-Aware Path Planning
1Dept. of Computer Science, University of Massachusetts Boston, Boston, MA, USA 2Dept. of Electrical \& Computer Engineering, Northeastern University, Boston, MA, USA
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
Institute for Artificial Intelligence（人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；Baozun（宝尊）
Neuro-Symbolic Skill Discovery for Conditional Multi-Level Planning
Department of Computer Science and Technology The University of Cambridge（计算机科学与技术系 剑桥大学）；Department of Computer Engineering Bogazici University（计算机工程系 boazici大学）；SISREC Osaka University（Osaka大学SISREC）
