Addressing and Visualizing Misalignments in Human Task-Solving Trajectories

2024-09-21

arXiv.org

AI Summary

This paper addresses three types of intent–action mismatches in human task-solving trajectories: missing intent articulation, action inefficiency, and failure due to incorrect intent. It provides the first systematic classification and formal definition of this phenomenon. We propose an interpretable, hierarchical quantitative analysis framework, design lightweight heuristic algorithms for trajectory pattern detection and intent estimation, and implement trajectory–intent alignment via the O2ARC trajectory processing pipeline. Our core contribution is the explicit modeling of implicit intent as a computable variable, enabling structured alignment to enhance AI’s capacity to imitate human reasoning processes. Experiments demonstrate significant performance gains on reasoning imitation tasks, empirically validating that trajectory–intent alignment is critical for intent learning and generalization. (138 words)

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Planning, Routing, and Scheduling: Model-Based Reasoning

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: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Understanding misalignments in human task-solving trajectories is critical for improving AI models trained to mimic human reasoning. This study categorizes such misalignments into three types: extbf{(1) Lack of functions to express intent}, extbf{(2) Inefficient action sequences}, and extbf{(3) Incorrect intentions that cannot solve the task}. To address these issues, we first formalize and define these three types of misalignments. We then propose a heuristic algorithm to detect these misalignments in O2ARC trajectories and conduct a hierarchical and quantitative analysis of their impact. Furthermore, we introduce an intention estimation algorithm that predicts missing alignment information between user actions and inferred intentions, leveraging our formalized framework. Through trajectory alignment, we experimentally demonstrate that AI models trained on human task-solving trajectories improve performance in mimicking human reasoning. Based on hierarchical analysis and experiments, we highlight the importance of trajectory-intention alignment and demonstrate the potential of intention learning.

Problem

• Categorizing misalignments in human task-solving trajectories
• Detecting misalignments to improve AI reasoning models
• Predicting missing alignment between actions and intentions

Innovation

• Heuristic algorithm detects misalignments in trajectories
• Intention estimation algorithm predicts missing alignment information
• Trajectory alignment improves AI model performance