Knowledge Engineering for Planning-Based Hypothesis Generation
IBM T.J. Watson Research Center（IBM T.J. 沃森研究中心）
Shirin Sohrabi, Octavian Udrea, Anton V. Riabov
备注：
This paper appears in the Proceedings of the Automated Planning and Scheduling (ICAPS) Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)
In this paper, we address the knowledge engineering problems for hypothesis generation motivated by applications that require timely exploration of hypotheses under unreliable observations. We looked at two applications: malware detection and intensive care delivery. In intensive care, the goal is to generate plausible hypotheses about the condition of the patient from clinical observations and further refine these hypotheses to create a recovery plan for the patient. Similarly, preventing malware spread within a corporate network involves generating hypotheses from network traffic data and selecting preventive actions. To this end, building on the already established characterization and use of AI planning for similar problems, we propose use of planning for the hypothesis generation problem. However, to deal with uncertainty, incomplete model description and unreliable observations, we need to use a planner capable of generating multiple high-quality plans. To capture the model description we propose a language called LTS++ and a web-based tool that enables the specification of the LTS++ model and a set of observations. We also proposed a 9-step process that helps provide guidance to the domain expert in specifying the LTS++ model. The hypotheses are then generated by running a planner on the translated LTS++ model and the provided trace. The hypotheses can be visualized and shown to the analyst or can be further investigated automatically.
Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4
Max Planck Institute for Computer Science（马克斯·普朗克计算机科学研究所）；Universität Dortmund（多特蒙德大学）；The Australian National University（澳大利亚国立大学）；Deutsche Telekom Laboratories（德国电信实验室）；PUC-Rio（里约热内卢天主教大学）；Universität Freiburg（弗莱堡大学）
Dogfight Search: A Swarm-Based Optimization Algorithm for Complex Engineering Optimization and Mountainous Terrain Path Planning
College of Science, Liaoning Technical University（辽宁工程技术大学理学院）；School of Mathematical Sciences, Dalian University of Technology（大连理工大学数学科学学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Physics, University of Science and Technology of China（中国科学技术大学物理系）
A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
University of Bonn（波恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；Center for Robotics（机器人中心）；Computer Science Institute VI（计算机科学第六研究所）；Autonomous Intelligent Systems group（自主智能系统组）
The FIL Hypothesis: Inductive Biases Help with Kernel Engineering
NLP Department, MBZUAI（自然语言处理部门，MBZUAI）；Ubiquitous Knowledge Processing, TU Darmstadt（普遍知识处理，图腾斯达特大学）；Department of Computing, Imperial College London（计算部门，伦敦帝国理工学院）
An Integrated Development Environment for Planning Domain Modeling
Sun Yat-sen University（中山大学）
为了使规划领域和规划问题的任务描述对非规划专家更加全面，近年来，规划领域建模采用了可视化表示方法。然而，目前的可视化建模知识工程工具，如itSIMPLE（Vaquero et al. 2012）和VIZ（Vodrážka and Chrpa 2010），比传统的PDDL专家使用文本编辑器手工编码的方法效率更低，并且很少根据计划验证对计划域进行微调。针对这一问题，本文在SIMPLE和VIZ的启发下，提出了一个规划领域可视化建模集成开发环境KAVI，该环境利用抽象领域知识库提高了规划领域可视化建模的效率。通过集成规划器和规划验证器，KAVI提出了一种基于规划验证的规划域微调方法。
Design-to-Plan: A Large Language Model-Based Multi-Agent Framework for Manufacturing Process Planning from 3D CAD Models and 2D Engineering Drawings
Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR)（新加坡制造技术研究院（新加坡科学、技术与研究局））；Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)（先进再制造与技术中心（新加坡科学、技术与研究局））；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与宇航工程学院）
PRECEPT: Planning Resilience via Experience, Context Engineering & Probing Trajectories A Unified Framework for Test-Time Adaptation with Compositional Rule Learning and Pareto-Guided Prompt Evolution
ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design
Shanghai Jiao Tong University, School of Mathematical Sciences（上海交通大学数学科学学院）；DP Technology（DP技术）；University of Science and Technology of China（中国科学技术大学）；AI for Science Institute（AI for Science研究院）
