Planning with OWL-DL Ontologies (Extended Version)
- URL: http://arxiv.org/abs/2408.07544v1
- Date: Wed, 14 Aug 2024 13:27:02 GMT
- Title: Planning with OWL-DL Ontologies (Extended Version)
- Authors: Tobias John, Patrick Koopmann,
- Abstract summary: 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.
- Score: 6.767885381740952
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce ontology-mediated planning, in which planning problems are combined with an ontology. Our formalism differs from existing ones in that we focus on a strong separation of the formalisms for describing planning problems and ontologies, which are only losely coupled by an interface. Moreover, we present a black-box algorithm that supports the full expressive power of OWL DL. This goes beyond what existing approaches combining automated planning with ontologies can do, which only support limited description logics such as DL-Lite and description logics that are Horn. Our main algorithm relies on rewritings of the ontology-mediated planning specifications into PDDL, so that existing planning systems can be used to solve them. The algorithm relies on justifications, which allows for a generic approach that is independent of the expressivity of the ontology language. However, dedicated optimizations for computing justifications need to be implemented to enable an efficient rewriting procedure. We evaluated our implementation on benchmark sets from several domains. The evaluation shows that our procedure works in practice and that tailoring the reasoning procedure has significant impact on the performance.
Related papers
- 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) - Temporal Planning via Interval Logic Satisfiability for Autonomous Systems [0.0]
We consider formulations of temporal planning where intervals are associated with both action and fluent atoms and relations between these are given as sentences in Allen's Interval Logic.
We propose a notion of planning graphs that can account for complex relations between actions and fluents as a Constraint Programming (CP) model.
We demonstrate our algorithm outperforms existing PDDL 2.1 planners in the case studies.
arXiv Detail & Related papers (2024-06-14T02:21:53Z) - Learning Logic Specifications for Policy Guidance in POMDPs: an
Inductive Logic Programming Approach [57.788675205519986]
We learn high-quality traces from POMDP executions generated by any solver.
We exploit data- and time-efficient Indu Logic Programming (ILP) to generate interpretable belief-based policy specifications.
We show that learneds expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specifics within lower computational time.
arXiv Detail & Related papers (2024-02-29T15:36:01Z) - Unified Task and Motion Planning using Object-centric Abstractions of
Motion Constraints [56.283944756315066]
We propose an alternative TAMP approach that unifies task and motion planning into a single search.
Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI search to yield physically feasible plans.
arXiv Detail & Related papers (2023-12-29T14:00:20Z) - 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) - Guiding Language Model Reasoning with Planning Tokens [122.43639723387516]
Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks.
We propose a hierarchical generation scheme to encourage a more structural generation of chain-of-thought steps.
Our approach requires a negligible increase in trainable parameters (0.001%) and can be applied through either full fine-tuning or a more parameter-efficient scheme.
arXiv Detail & Related papers (2023-10-09T13:29:37Z) - Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop
Visual Reasoning [16.495754104540605]
Large language models (LLMs) can generate code-like plans for complex inference tasks such as visual reasoning.
We propose a hierarchical plan-searching algorithm that integrates the one-stop reasoning (fast) and the Tree-of-thought (slow)
arXiv Detail & Related papers (2023-08-18T16:21:40Z) - Towards Ontology-Mediated Planning with OWL DL Ontologies (Extended
Version) [7.995360025953931]
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.
arXiv Detail & Related papers (2023-08-16T08:05:53Z) - An Efficient HTN to STRIPS Encoding for Concurrent Plans [0.0]
We present a new HTN to STRIPS encoding allowing to generate concurrent plans.
We show experimentally that this encoding outperforms previous approaches on hierarchical IPC benchmarks.
arXiv Detail & Related papers (2022-06-14T18:18:22Z) - 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) - 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.