Neuro-Symbolic Hierarchical Intention Anticipation in Human Behavior

📅 2026-09-15
📈 Citations: 0
Influential: 0
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🤖 AI Summary
该研究通过结合神经符号识别编码器和分层规划解码器,解决了辅助自主系统在人类行为未完成前预测其目标的问题。
📝 Abstract
Assistive autonomous systems must anticipate human goals before an observed behavior is complete. This article formulates anticipation as goal inference from a partially observed multimodal episode together with structured prediction of the remaining behavior, rather than exact motor forecasting. A compact Hierarchical Planning Decoder (HPD) is attached to a frozen neuro-symbolic recognition encoder and predicts, at four ontological levels, the next actions, the remaining activities and low-level intentions, and the episode high-level intention(HLI). The decoder is trained with soft neuro-symbolic regularization combining transition-coherence and hierarchical continuity losses, and is decoded with hard reachability masks that enforce ontological validity at inference. On a compositional four-level benchmark of 15,002 multimodal episodes built over NTU RGB+D 120 features, three headline properties are observed together. The advantage over the strongest sequential baseline grows with the anticipation horizon, from +1.7 points at step 1 to +7.3 points at step 3 (top-5). Under compositional generalization, where one parent association per multi-parent low level intention is held out, this advantage widens to +4.9 points at step 1. At the episode level, 96.8% of anticipated trajectories satisfy the joint logic constraints, above the 88.1% strongest-baseline value and the 73.9% ground-truth floor; soft logic terms alone account for a 59.8 to 71.1% relative reduction of HLI-reachability violations, and the hard masks then eliminate them entirely. Neural generation supplies predictive ranking, symbolic constraints supply onto logical validity, and their combination yields coherent hierarchical anticipation while exposing remaining challenges in compositional goal generalization and unordered set prediction.
Problem

Research questions and friction points this paper is trying to address.

anticipation
goal inference
hierarchical intention
Innovation

Methods, ideas, or system contributions that make the work stand out.

Neuro-Symbolic Hierarchical Intention Anticipation
Hierarchical Planning Decoder (HPD)
soft neuro-symbolic regularization
hard reachability masks
ontological validity
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