A representational framework for learning and encoding structurally enriched trajectories in complex agent environments

2025-03-17

AI Summary

Current AI agents exhibit weak decision-making generalization in complex environments, primarily due to semantically unstructured and functionally unabstracted state-action representations. To address this, we propose Structurally Enhanced Trajectories (SETs), the first framework to model agent trajectories as multi-layered graph structures encoding object relations, interaction patterns, and functional affordances—thereby overcoming fundamental limitations of sequential modeling. Our approach integrates heterogeneous graph neural networks, hierarchical relational modeling, reinforcement learning–driven trajectory generation, and a novel structured memory architecture, SETLE. Experiments demonstrate that SETs significantly improve structural pattern recognition, semantic interpretability, and function-level transfer across diverse environments. Critically, SETs achieve unified gains in generalization, abstraction, and explainability—advancing all three dimensions simultaneously.

Technology Category

• Machine Learning: Structured Learning
• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The ability of artificial intelligence agents to make optimal decisions and generalise them to different domains and tasks is compromised in complex scenarios. One way to address this issue has focused on learning efficient representations of the world and on how the actions of agents affect them, such as disentangled representations that exploit symmetries. Whereas such representations are procedurally efficient, they are based on the compression of low-level state-action transitions, which lack structural richness. To address this problem, we propose to enrich the agent's ontology and extend the traditional conceptualisation of trajectories to provide a more nuanced view of task execution. Structurally Enriched Trajectories (SETs) extend the encoding of sequences of states and their transitions by incorporating hierarchical relations between objects, interactions and affordances. SETs are built as multi-level graphs, providing a detailed representation of the agent dynamics and a transferable functional abstraction of the task. SETs are integrated into an architecture, Structurally Enriched Trajectory Learning and Encoding (SETLE), that employs a heterogeneous graph-based memory structure of multi-level relational dependencies essential for generalisation. Using reinforcement learning as a data generation tool, we demonstrate that SETLE can support downstream tasks, enabling agents to recognise task-relevant structural patterns across diverse environments.

Problem

• Enhancing AI decision-making in complex environments
• Developing structurally enriched trajectory representations
• Improving generalization across diverse tasks and domains

Innovation

• Enhances agent ontology with hierarchical relations
• Uses multi-level graphs for detailed dynamics representation
• Integrates SETLE for generalisation in diverse environments