AGENTIC-SYSTEMSJuly 20, 20261 min read

Logic, Optimization, and Artificial Intelligence

The combination of logic and optimization offers a powerful synergy for rule-based AI, addressing the critical need for transparency, explainability, and trustworthiness in modern artificial intelligence systems.

Computer Science > Artificial Intelligence

Title:Logic, Optimization, and Artificial Intelligence

View PDF HTML (experimental)Abstract:Logic and optimization can, in combination, make valuable contributions to rule-based AI. Logic is the obvious medium for encoding a rule base and drawing inferences from it, while optimization provides a powerful technology for computing inferences. Their combination has taken on new relevance amid a growing concern for transparency in AI. which is important for reproducibility, explainability, trustworthiness, and fairness. Rule-based AI provides a natural solution to transparency that is becoming increasingly practical due to today's highly advanced optimization methods. This article surveys several areas of logic-optimization partnership, including probabilistic logic, Bayesian logic, belief logics and Dempster-Shafer theory, nonmonotonic (default) logic, many-valued logics, and inference of logical formulas from noisy data based on Boolean regression. It shows how to compute projections, the fundamental problem of both logic and optimization, using decision diagrams and logic-based Benders decomposition. It describes the use of postoptimality analysis to explain how conclusions are reached, further enhancing transparency, as well as the role of optimization in answer set programming modulo theories. The paper concludes by suggesting possible future research directions.

Current browse context:

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation

Researchers have introduced S1-Omni, a unified multimodal reasoning model designed for AI for Science (AI4S). Outperforming advanced models like GPT-5.5 and Gemini-3.1-Pro on various benchmarks, S1-Omni addresses the fragmentation in modeling complex scientific data.

agentic-systems

NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning

Researchers have introduced NeurOWL, a novel LLM-based neuro-symbolic framework designed to address reasoning challenges in incomplete OWL ontologies. By unifying subsumption verification and ontology abduction, NeurOWL demonstrates robust performance across multiple domains.

agentic-systems

ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning

Researchers introduce ToolVerse, a comprehensive framework designed to scale up agentic reinforcement learning environments and enable LLM agents to perform complex, long-horizon reasoning tasks using thousands of real-world tools.

NOW LET US Related – Neuro-Symbolic AI for LEED compliance: Document-Centric Benchmarking, Deterministic Numeric Checking, and When Multimodal Hurts

agentic-systems

Neuro-Symbolic AI for LEED compliance: Document-Centric Benchmarking, Deterministic Numeric Checking, and When Multimodal Hurts

A new study introduces a local neuro-symbolic AI pipeline to automate LEED v4.1 green building certification screening. The findings reveal that the small 4-billion-parameter Gemma 3 model outperforms larger models, while incorporating multimodal drawing images consistently degrades performance.

agentic-systems

SeerGuard: A Safety Framework for Mobile GUI Agents via World Model Prediction

Researchers have introduced SeerGuard, a proactive safety framework that prevents mobile GUI agents from executing risky actions by predicting consequences beforehand using a Safety-Augmented World Model (SAWM).

NOW LET US Related – MGDT: MLLM-Guided Diffusion Transformer with Relation-Adaptive Mixture-of-Experts for Multimodal Knowledge Graph Completion

agentic-systems

MGDT: MLLM-Guided Diffusion Transformer with Relation-Adaptive Mixture-of-Experts for Multimodal Knowledge Graph Completion

Researchers have proposed MGDT, a novel framework for Multimodal Knowledge Graph Completion (MKGC) that utilizes an align-then-diffuse paradigm. By integrating a frozen MLLM and a Relation-Adaptive Mixture-of-Experts module, MGDT significantly outperforms existing baselines.