A Knowledge-Graph Translation Layer for Mission-Aware Multi-Agent Path Planning in Spatiotemporal Dynamics

2025-10-24

AI Summary

A semantic gap exists between high-level task objectives and low-level path planners in dynamic environments. Method: This paper proposes a knowledge-graph-based dual-plane translation framework, wherein a knowledge graph serves as a stateful coordination hub integrating declarative rule engines and spatiotemporal reasoning to automatically map task semantics into physically constrained, task-aware individual “worldviews,” thereby decoupling high-level decision-making from low-level execution. Contribution/Results: The method enables online, adaptive multi-agent behavior adjustment via fact updates, significantly enhancing system interpretability and flexibility. Empirical evaluation in Gulf of Mexico AUV cooperative control demonstrates that diverse declarative strategies generate high-performance, differentiated end-to-end collaborative trajectories, validating the framework’s effectiveness and generalizability.

Technology Category

• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The coordination of autonomous agents in dynamic environments is hampered by the semantic gap between high-level mission objectives and low-level planner inputs. To address this, we introduce a framework centered on a Knowledge Graph (KG) that functions as an intelligent translation layer. The KG's two-plane architecture compiles declarative facts into per-agent, mission-aware ``worldviews"and physics-aware traversal rules, decoupling mission semantics from a domain-agnostic planner. This allows complex, coordinated paths to be modified simply by changing facts in the KG. A case study involving Autonomous Underwater Vehicles (AUVs) in the Gulf of Mexico visually demonstrates the end-to-end process and quantitatively proves that different declarative policies produce distinct, high-performing outcomes. This work establishes the KG not merely as a data repository, but as a powerful, stateful orchestrator for creating adaptive and explainable autonomous systems.

Problem

• Bridging semantic gap between mission objectives and planner inputs
• Translating declarative facts into agent-specific worldviews and rules
• Enabling adaptive path planning through knowledge graph modifications

Innovation

• Knowledge Graph translates mission objectives into agent paths
• Two-plane architecture decouples mission semantics from planning
• Dynamic path adaptation by modifying Knowledge Graph facts