Towards Ontology-Mediated Planning with OWL DL Ontologies (Extended Version)

• URL: http://arxiv.org/abs/2308.08200v1
• Date: Wed, 16 Aug 2023 08:05:53 GMT
• Title: Towards Ontology-Mediated Planning with OWL DL Ontologies (Extended Version)
• Authors: Tobias John and Patrick Koopmann
• Abstract summary: We present a new approach in which the planning specification and ontology are kept separate, and are linked together using an interface. This allows planning experts to work in a familiar formalism, while existing domains can be easily integrated and extended by experts. The idea is to rewrite the whole-mediated planning problem into a classical planning problem to be processed by existing planning tools.
• Score: 7.995360025953931
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While classical planning languages make the closed-domain and closed-world assumption, there have been various approaches to extend those with DL reasoning, which is then interpreted under the usual open-world semantics. Current approaches for planning with DL ontologies integrate the DL directly into the planning language, and practical approaches have been developed based on first-order rewritings or rewritings into datalog. We present here a new approach in which the planning specification and ontology are kept separate, and are linked together using an interface. This allows planning experts to work in a familiar formalism, while existing ontologies can be easily integrated and extended by ontology experts. Our approach for planning with those ontology-mediated planning problems is optimized for cases with comparatively small domains, and supports the whole OWL DL fragment. The idea is to rewrite the ontology-mediated planning problem into a classical planning problem to be processed by existing planning tools. Different to other approaches, our rewriting is data-dependent. A first experimental evaluation of our approach shows the potential and limitations of this approach.

Related papers

• Planning with OWL-DL Ontologies (Extended Version) [6.767885381740952]
  We present a black-box that supports the full power expressive DL. Our main algorithm relies on rewritings of the OWL-mediated planning specifications into PDDL. We evaluate our implementation on benchmark sets from several domains.
  arXiv  Detail & Related papers  (2024-08-14T13:27:02Z)
• Parallel Strategies for Best-First Generalized Planning [51.713634067802104]
  Generalized planning (GP) is a research area of AI that studies the automated synthesis of algorithmic-like solutions capable of solving multiple classical planning instances. One of the current advancements has been the introduction of Best-First Generalized Planning (BFGP), a GP algorithm based on a novel solution space that can be explored with search. This paper evaluates the application of parallel search techniques to BFGP, another critical component in closing the performance gap.
  arXiv  Detail & Related papers  (2024-07-31T09:50:22Z)
• PROC2PDDL: Open-Domain Planning Representations from Texts [56.627183903841164]
  Proc2PDDL is the first dataset containing open-domain procedural texts paired with expert-annotated PDDL representations. We show that Proc2PDDL is highly challenging, with GPT-3.5's success rate close to 0% and GPT-4's around 35%.
  arXiv  Detail & Related papers  (2024-02-29T19:40:25Z)
• Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty [56.30846158280031]
  Task planning for embodied AI has been one of the most challenging problems. We propose a task-agnostic method named 'planning as in-painting' The proposed framework achieves promising performances in various embodied AI tasks.
  arXiv  Detail & Related papers  (2023-12-02T10:07:17Z)
• A Categorical Representation Language and Computational System for Knowledge-Based Planning [5.004278968175897]
  We propose an alternative approach to representing and managing updates to world states during planning. Based on the category-theoretic concepts of $mathsfC$-sets and double-pushout rewriting (DPO), our proposed representation can effectively handle structured knowledge about world states.
  arXiv  Detail & Related papers  (2023-05-26T19:01:57Z)
• Turning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data Generation [50.06143883455979]
  Flowchart-grounded troubleshooting dialogue (FTD) systems follow the instructions of a flowchart to diagnose users' problems in specific domains. We propose a plan-based synthetic data generation approach that generates diverse synthetic dialog data at scale.
  arXiv  Detail & Related papers  (2023-05-02T11:08:27Z)
• Hierarchical Decomposition and Analysis for Generalized Planning [26.288236123430117]
  This paper presents new methods for analyzing and evaluating generalized plans. We develop a new conceptual framework along with proof techniques and algorithmic processes. We show that this approach significantly extends the class of generalized plans that can be assessed automatically.
  arXiv  Detail & Related papers  (2022-12-06T08:37:21Z)
• Expressivity of Planning with Horn Description Logic Ontologies (Technical Report) [12.448670165713652]
  We address open-world state constraints formalized by planning over a description logic (DL) ontology. We propose a novel compilation scheme into standard PDDL with derived predicates. We show that our approach can outperform previous work on existing benchmarks for planning with DL.
  arXiv  Detail & Related papers  (2022-03-17T14:50:06Z)
• Differentiable Spatial Planning using Transformers [87.90709874369192]
  We propose Spatial Planning Transformers (SPT), which given an obstacle map learns to generate actions by planning over long-range spatial dependencies. In the setting where the ground truth map is not known to the agent, we leverage pre-trained SPTs in an end-to-end framework. SPTs outperform prior state-of-the-art differentiable planners across all the setups for both manipulation and navigation tasks.
  arXiv  Detail & Related papers  (2021-12-02T06:48:16Z)
• STRIPS Action Discovery [67.73368413278631]
  Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. We propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown.
  arXiv  Detail & Related papers  (2020-01-30T17:08:39Z)

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.