The paradigms of transformational planning, case-based planning, and plan debugging all involve a process known as plan adaptation - modifying or repairing an old plan so it solves a new problem. In this paper we provide a domain-independent algorithm for plan adaptation, demonstrate that it is sound, complete, and systematic, and compare it to other adaptation algorithms in the literature. Our approach is based on a view of planning as searching a graph of partial plans. Generative planning starts at the graph's root and moves from node to node using plan-refinement operators. In planning by adaptation, a library plan - an arbitrary node in the plan graph - is the starting point for the search, and the plan-adaptation algorithm can apply both the same refinement operators available to a generative planner and can also retract constraints and steps from the plan. Our algorithm's completeness ensures that the adaptation algorithm will eventually search the entire graph and its systematicity ensures that it will do so without redundantly searching any parts of the graph.
Enhancing Demand-Oriented Regionalization with Agentic AI and Local Heterogeneous Data for Adaptation Planning
College of Design, Construction and Planning, University of Florida（佛罗里达大学设计、建设与规划学院）；Department of Electrical and Computer Engineering, University of Florida（佛罗里达大学电气与计算机工程系）；Florida Institute for Built Environment Resilience (FIBER), University of Florida（佛罗里达大学环境韧性研究所）
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；State Key Laboratory of General Artificial Intelligence, BIGAI（北京通用人工智能研究院通用人工智能国家重点实验室）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Tsinghua University（清华大学）
Repeated Deceptive Path Planning against Learnable Observer
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；National Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institution of Automation, Chinese Academy of Sciences（中国科学院复杂系统认知与决策智能国家重点实验室）
Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning
School of Computer Science, The University of Sheffield, UK（谢菲尔德大学计算机科学学院，英国）；Cancer Research UK National Biomarker Centre, The University of Manchester, UK（英国曼彻斯特大学癌症研究UK国家生物标志物中心）；School of Chemical, Materials and Biological Engineering, The University of Sheffield, UK（谢菲尔德大学化学、材料和生物工程学院，英国）；Machine Learning group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands（荷兰阿姆斯特丹Centrum Wiskunde & Informatica机器学习组）；Institute for Ecological Economics, Vienna University of Economics and Business, Austria（奥地利维也纳经济与商业大学生态经济研究所）
A Novel Multi-Agent Scheduling Mechanism for Adaptation of Production Plans in Case of Supply Chain Disruptions
Huawei Technologies Co. Ltd.（华为技术有限公司）；City University of Applied Sciences Bremen（不来梅城市应用科学大学）；Actoron GmbH（Actoron有限公司）；Brandenburg University of Applied Sciences（勃兰登堡应用科学大学）
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
Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
The Hong Kong University of Science and Technology（香港科技大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；East China University of Science and Technology（华东理工大学）
Faithful Chart Generation for Multimodal Deep Research: Frame-Evidence Co-Adaptation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Amsterdam（阿姆斯特丹大学）
