We present GenePlan (GENeralized Evolutionary Planner), a novel framework that leverages large language model (LLM) assisted evolutionary algorithms to generate domain-dependent generalized planners for classical planning tasks described in PDDL. By casting generalized planning as an optimization problem, GenePlan iteratively evolves interpretable Python planners that minimize plan length across diverse problem instances. In empirical evaluation across six existing benchmark domains and two new domains, GenePlan achieved an average SAT score of 0.91, closely matching the performance of the state-of-the-art planners (SAT score 0.93), and significantly outperforming other LLM-based baselines such as chain-of-thought (CoT) prompting (average SAT score 0.64). The generated planners solve new instances rapidly (average 0.49 seconds per task) and at low cost (average $1.82 per domain using GPT-4o).
Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Industry-education Integration, University of Chinese Academy of Sciences（中国科学院大学产教融合学院）
Generating Symbolic World Models via Test-time Scaling of Large Language Models
Westlake University（西湖大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Max Planck Institute for Intelligent Systems, Tübingen（智能系统马克斯·普朗克研究所（图宾根））；Environmental Systems Research Institute, Inc.（环境系统研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；SEED, Bytedance（字节跳动SEED团队）；The Hong Kong University of Science and Technology（香港科技大学）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔兰大学）；Center for European Research in Trusted Artificial Intelligence (CERTAIN)（欧洲可信人工智能研究中心）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）
Aligning LLM+PDDL Symbolic Plans with Human Objective Specifications through Evolutionary Algorithm Guidance
College of Engineering and Computer Science, University of Central Florida（中央佛罗里达大学工程与计算机科学学院）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
Drexel University（德雷塞尔大学）；University of Genoa（热那亚大学）
PDDL+ is an extension of PDDL that enables modelling planning domains with mixed discrete-continuous dynamics. In this paper we present a new approach to PDDL+ planning based on Constraint Answer Set Programming (CASP), i.e. ASP rules plus numerical constraints. To the best of our knowledge, ours is the first attempt to link PDDL+ planning and logic programming. We provide an encoding of PDDL+ models into CASP problems. The encoding can handle non-linear hybrid domains, and represents a solid basis for applying logic programming to PDDL+ planning. As a case study, we consider the EZCSP CASP so…
In recent years research in the planning community has moved increasingly toward s application of planners to realistic problems involving both time and many typ es of resources. For example, interest in planning demonstrated by the space res earch community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technolo…
Saint Joseph’s University（圣约瑟夫大学）；King’s College London（伦敦国王学院）；University of Genoa（热那亚大学）；Drexel University（德雷塞尔大学）
CASP is an extension of ASP that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP CASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the EZCSP solver, other …
The University of Texas at Austin（得克萨斯大学奥斯汀分校）；Cogitai, Inc.（科吉泰公司）；SUNY Binghamton（纽约州立大学宾汉姆顿分校）
Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this article, we empirically compare the performance of state-of-the-art planners that use either the Planning Domain Description Language (PDDL), or Answer Set Program…
I comment on the PDDL 2.1 language and its use in the planning competition, focusing on the choices made for accommodating time and concurrency. I also discuss some methodological issues that have to do with the move toward more expressive planning languages and the balance needed in planning research between semantics and computation.
In this paper we present pddl+, a planning domain description language for modelling mixed discrete-continuous planning domains. We describe the syntax and modelling style of pddl+, showing that the language makes convenient the modelling of complex time-dependent effects. We provide a formal semantics for pddl+ by mapping planning instances into constructs of hybrid automata. Using the syntax of HAs as our semantic model we construct a semantic mapping to labelled transition systems to complete the formal interpretation of pddl+ planning instances. An advantage of building a mapping from pddl…
PDDL2.1 was designed to push the envelope of what planning algorithms can do, and it has succeeded. It adds two important features: durative actions,which take time (and may have continuous effects); and objective functions for measuring the quality of plans. The concept of durative actions is flawed; and the treatment of their semantics reveals too strong an attachment to the way many contemporary planners work. Future PDDL innovators should focus on producing a clean semantics for additions to the language, and let planner implementers worry about coupling their algorithms to problems expres…
From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation
Institute of Automation Technology, Helmut Schmidt University Hamburg（海德堡-施密特大学汉堡自动化技术研究所）；Chair of Automation Technology, Ruhr University Bochum（博尔塔伦大学博德姆自动化技术教授团）
Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning
Department of Computer, Automation and Management Engineering, Sapienza University of Rome（Sapienza大学计算机、自动化与管理工程系）；Department of Electronics and Automation, Mohamed Khider University of Biskra（Mohamed Khider大学电子与自动化系）；International University of Rome UNINT, Rome（罗马国际大学UNINT）
Palo Alto Research Center（帕洛阿尔托研究中心）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）
This paper studies how a domain-independent planner and combinatorial search can be employed to play Angry Birds, a well established AI challenge problem. To model the game, we use PDDL+, a planning language for mixed discrete/continuous domains that supports durative processes and exogenous events. The paper describes the model and identifies key design decisions that reduce the problem complexity. In addition, we propose several domain-specific enhancements including heuristics and a search technique similar to preferred operators. Together, they alleviate the complexity of combinatorial sea…
