Goal-Oriented Semantic Communication for Logical Decision Making

2026-04-21

AI Summary

This work addresses the challenge of efficiently transmitting task-relevant semantic information under bandwidth constraints in multi-agent collaborative perception, where decisions must be both explainable and verifiable for safety-critical applications. The authors propose an interpretable semantic communication framework grounded in a hierarchical world representation based on first-order logic (FOL). For the first time, they integrate FOL structures with semantic information measures, defining semantic entropy and mutual information under logical probability, and introduce a goal-oriented state layer to abstract decision-critical information. By leveraging semantic rate-distortion theory and the semantic information bottleneck principle, the method selects the most informative FOL clauses for transmission, achieving semantic compression while preserving logical verifiability. Evaluated in a dynamic urban multi-agent simulation environment, the approach significantly reduces communication overhead while maintaining high decision accuracy and logical interpretability.

Technology Category

• Multiagent Systems: Agent Communication
• Knowledge Representation and Reasoning: Logic Programming
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Security and Privacy: Data transparency and provenance

Abstract

This paper develops a principled foundation for goal-oriented semantic communication for logical decision-making. Consider a setting where autonomous agents engage in collaborative perception. In such settings, the volume of sensory data and limited bandwidth often make transmission of raw observations infeasible, requiring intelligent selection of task-relevant information. Because these scenarios are safety-critical, the selection and decision processes must also be transparent and verifiable. To address this, we propose an explainable semantic communication framework grounded in a First-Order Logic (FOL) hierarchical representation of the world. We define semantic information, entropy, conditional entropy, and mutual information by assigning an inductive logical probability measure over semantic structures in the language. Based on these definitions, we formulate a goal-oriented semantic communication objective through semantic rate-distortion theory and, equivalently, through the semantic information bottleneck principle. In this framework, task rules are represented as goal-oriented states, defined as a layer over the world states to capture decision-relevant abstractions. The resulting principle selects evidence that is most informative about these states, aiming to transmit only those FOL clauses most critical for decision-making while preserving logical verifiability. We demonstrate the effectiveness of the approach in a deduction-based safe path-following task within an FOL-based urban environment simulator with multiple dynamic agents.

Problem

• semantic communication
• logical decision making
• goal-oriented
• explainability
• information selection

Innovation

• goal-oriented semantic communication
• first-order logic
• semantic information bottleneck
• inductive logical probability
• explainable AI