Computational methods for Dynamic Answer Set Programming
- URL: http://arxiv.org/abs/2502.09228v1
- Date: Thu, 13 Feb 2025 11:52:25 GMT
- Title: Computational methods for Dynamic Answer Set Programming
- Authors: Susana Hahn,
- Abstract summary: This research aims to extend Answer Set Programming (ASP) to handle dynamic domains effectively.
By integrating concepts from dynamic, temporal, and metric logics into ASP, we seek to develop robust systems capable of modeling complex dynamic problems.
- Score: 0.0
- License:
- Abstract: In our daily lives and industrial settings, we often encounter dynamic problems that require reasoning over time and metric constraints. These include tasks such as scheduling, routing, and production sequencing. Dynamic logics have traditionally addressed these needs but often lack the flexibility and integration required for comprehensive problem modeling. This research aims to extend Answer Set Programming (ASP), a powerful declarative problem-solving approach, to handle dynamic domains effectively. By integrating concepts from dynamic, temporal, and metric logics into ASP, we seek to develop robust systems capable of modeling complex dynamic problems and performing efficient reasoning tasks, thereby enhancing ASPs applicability in industrial contexts.
Related papers
- Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG [0.8463972278020965]
Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human like text generation and natural language understanding.
Retrieval Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real time data retrieval to provide contextually relevant responses.
Agentic Retrieval-Augmented Generation (RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline.
arXiv Detail & Related papers (2025-01-15T20:40:25Z) - BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom's-Taxonomy-Inspired Prompts [59.83547898874152]
We introduce BloomWise, a new prompting technique, inspired by Bloom's taxonomy, to improve the performance of Large Language Models (LLMs)
The decision regarding the need to employ more sophisticated cognitive skills is based on self-evaluation performed by the LLM.
In extensive experiments across 4 popular math reasoning datasets, we have demonstrated the effectiveness of our proposed approach.
arXiv Detail & Related papers (2024-10-05T09:27:52Z) - HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic Workflows [33.035088506211096]
We propose a novel framework HDFlow for complex reasoning with large language models (LLMs)
Our approach consists of two key components: 1) a new approach for slow, deliberate reasoning called Dynamic, which automatically decomposes complex problems into more manageable sub-tasks; and 2) Hybrid Thinking, a general framework that dynamically combines fast and slow thinking based on problem complexity.
Experiments on four reasoning benchmark demonstrate that our slow thinking with dynamic datasets significantly outperforms Chain-of-Thought, and hybrid thinking achieves the highest accuracy while providing an effective balance between computational efficiency and performance.
arXiv Detail & Related papers (2024-09-25T23:52:17Z) - Efficient Orchestrated AI Workflows Execution on Scale-out Spatial Architecture [17.516934379812994]
We present "Orchestrated AIs," an approach that integrates various tasks with logic-driven decisions into dynamic, sophisticated AIs.
We find that the intrinsic Dual Dynamicity of Orchestrated AIs can be effectively represented using the Orchestrated spatial Graph.
Our evaluations demonstrate that significantly outperforms traditional architectures in handling the dynamic demands of Orchestrated AIs.
arXiv Detail & Related papers (2024-05-21T14:09:31Z) - HAZARD Challenge: Embodied Decision Making in Dynamically Changing
Environments [93.94020724735199]
HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind.
This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
arXiv Detail & Related papers (2024-01-23T18:59:43Z) - Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers [54.83459025465947]
Even the largest models struggle with compositional reasoning, generalization, fine-grained spatial and temporal reasoning, and counting.
Visual reasoning with large language models (LLMs) as controllers can, in principle, address these limitations by decomposing the task and solving subtasks by orchestrating a set of (visual) tools.
We present a framework that mitigates these issues by introducing spatially and temporally abstract routines and by leveraging a small number of labeled examples to automatically generate in-context examples.
arXiv Detail & Related papers (2024-01-03T20:48:47Z) - Controllable Dynamic Multi-Task Architectures [92.74372912009127]
We propose a controllable multi-task network that dynamically adjusts its architecture and weights to match the desired task preference as well as the resource constraints.
We propose a disentangled training of two hypernetworks, by exploiting task affinity and a novel branching regularized loss, to take input preferences and accordingly predict tree-structured models with adapted weights.
arXiv Detail & Related papers (2022-03-28T17:56:40Z) - Multi-Objective Constrained Optimization for Energy Applications via
Tree Ensembles [55.23285485923913]
Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives.
In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions.
This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems.
arXiv Detail & Related papers (2021-11-04T20:18:55Z) - Constructing Neural Network-Based Models for Simulating Dynamical
Systems [59.0861954179401]
Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system.
This paper provides a survey of the different ways to construct models of dynamical systems using neural networks.
In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
arXiv Detail & Related papers (2021-11-02T10:51:42Z) - Automata Techniques for Temporal Answer Set Programming [0.0]
Temporal and dynamic extensions of Answer Set Programming (ASP) have played an important role in addressing dynamic problems.
I intend to exploit the relationship between automata theory and dynamic logic to add automata-based techniques to the ASP solver CLINGO.
arXiv Detail & Related papers (2021-09-17T01:43:31Z) - Implementing Dynamic Answer Set Programming [0.0]
We develop a translation of dynamic formulas into temporal logic programs.
The reduction of dynamic formulas to temporal logic programs allows us to extend ASP with both approaches in a uniform way.
arXiv Detail & Related papers (2020-02-17T12:34:14Z)
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.