The Epistemic Planning Domain Definition Language: Official Guideline

• URL: http://arxiv.org/abs/2601.20969v2
• Date: Tue, 03 Feb 2026 17:32:48 GMT
• Title: The Epistemic Planning Domain Definition Language: Official Guideline
• Authors: Alessandro Burigana, Francesco Fabiano,
• Abstract summary: We introduce the Epistemic Planning Domain Definition Language (EPDDL)<n>EPDDL provides a unique PDDL-like representation that captures the entire DEL semantics.
• Score: 46.476594987947344
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Epistemic planning extends (multi-agent) automated planning by making agents' knowledge and beliefs first-class aspects of the planning formalism. One of the most well-known frameworks for epistemic planning is Dynamic Epistemic Logic (DEL), which offers an rich and natural semantics for modelling problems in this setting. The high expressive power provided by DEL make DEL-based epistemic planning a challenging problem to tackle both theoretically, and in practical implementations. As a result, existing epistemic planners often target different DEL fragments, and typically rely on ad hoc languages to represent benchmarks, and sometimes no language at all. This fragmentation hampers comparison, reuse, and systematic benchmark development. We address these issues by introducing the Epistemic Planning Domain Definition Language (EPDDL). EPDDL provides a unique PDDL-like representation that captures the entire DEL semantics, enabling uniform specification of epistemic planning tasks. Our main contributions are: 1. A formal development of abstract event models, a novel representation for epistemic actions used to define the semantics of our language; 2. A formal specification of EPDDL's syntax and semantics grounded in DEL with abstract event models. Through examples of representative benchmarks, we illustrate how EPDDL facilitates interoperability, reproducible evaluation, and future advances in epistemic planning.

Related papers

• Large Language Models Can Take False First Steps at Inference-time Planning [2.6100783621884625]
  Large language models (LLMs) have been shown to acquire sequence-level planning abilities during training.<n>Planing behavior exhibited at inference time often appears short-sighted and inconsistent with these capabilities.<n>We propose a Bayesian account for this gap by grounding planning behavior in the evolving generative context.
  arXiv  Detail & Related papers  (2026-02-03T01:54:55Z)
• On the Limit of Language Models as Planning Formalizers [4.145422873316857]
  Large Language Models have been found to create plans that are neither executable nor verifiable in grounded environments.<n>An emerging line of work demonstrates success in using the LLM as a formalizer to generate a formal representation of the planning domain in some language.<n>This formal representation can be deterministically solved to find a plan.
  arXiv  Detail & Related papers  (2024-12-13T05:50:22Z)
• Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning [94.76546523689113]
  We introduce CodePlan, a framework that generates and follows textcode-form plans -- pseudocode that outlines high-level, structured reasoning processes. CodePlan effectively captures the rich semantics and control flows inherent to sophisticated reasoning tasks. It achieves a 25.1% relative improvement compared with directly generating responses.
  arXiv  Detail & Related papers  (2024-09-19T04:13:58Z)
• LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments [70.91258869156353]
  We introduce LangSuitE, a versatile and simulation-free testbed featuring 6 representative embodied tasks in textual embodied worlds. Compared with previous LLM-based testbeds, LangSuitE offers adaptability to diverse environments without multiple simulation engines. We devise a novel chain-of-thought (CoT) schema, EmMem, which summarizes embodied states w.r.t. history information.
  arXiv  Detail & Related papers  (2024-06-24T03:36:29Z)
• A Semantic Approach to Decidability in Epistemic Planning (Extended Version) [72.77805489645604]
  We use a novel semantic approach to achieve decidability. Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity. We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
  arXiv  Detail & Related papers  (2023-07-28T11:26:26Z)
• DELPHIC: Practical DEL Planning via Possibilities (Extended Version) [76.75197961194182]
  This work aims to push the envelop of practical DEL planning. We propose an equivalent semantics defined using, as main building block, so-called possibilities. To substantiate this claim, we implement both approaches in ASP and we set up an experimental evaluation to compare DELPHIC with the traditional, Kripke-based approach.
  arXiv  Detail & Related papers  (2023-07-28T10:09:45Z)
• Neuro-Symbolic Causal Language Planning with Commonsense Prompting [67.06667162430118]
  Language planning aims to implement complex high-level goals by decomposition into simpler low-level steps. Previous methods require either manual exemplars or annotated programs to acquire such ability from large language models. This paper proposes Neuro-Symbolic Causal Language Planner (CLAP) that elicits procedural knowledge from the LLMs with commonsense-infused prompting.
  arXiv  Detail & Related papers  (2022-06-06T22:09:52Z)
• HDDL 2.1: Towards Defining an HTN Formalism with Time [0.0]
  Real world applications of planning, like in industry and robotics, require modelling rich and diverse scenarios. Their resolution usually requires coordinated and concurrent action executions. In several cases, such planning problems are naturally decomposed in a hierarchical way and expressed by a Hierarchical Task Network formalism. This paper opens discussions on the semantics and the syntax needed to extend HDDL, and illustrate these needs with the modelling of an Earth Observing Satellite planning problem.
  arXiv  Detail & Related papers  (2022-06-03T21:22:19Z)
• Modelling Multi-Agent Epistemic Planning in ASP [66.76082318001976]
  This paper presents an implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature.
  arXiv  Detail & Related papers  (2020-08-07T06:35:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.