Inferring Implicit Goals Across Differing Task Models

2025-01-29

AI Summary

In human-agent interaction, misalignment between the agent’s task model and the user’s implicit goals—due to unexpressed intentions—leads to persistent misunderstandings. Method: We propose an implicit subgoal inference framework grounded in bottleneck state identification. For the first time, we formulate implicit goal inference as a subgoal discovery problem under model discrepancy, leveraging differences in bottleneck states between the user’s and agent’s MDP models to generate high-potential implicit subgoal candidates. We further design a minimal active querying strategy to efficiently infer the true intent, integrating bottleneck analysis, active learning, and policy consistency verification. Results: Experiments across diverse tasks show our method converges to a policy guaranteeing underlying goal achievement with ≤5 queries on average—significantly improving implicit goal recognition accuracy while ensuring robust task completion.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search
• Machine Learning: Active Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

One of the significant challenges to generating value-aligned behavior is to not only account for the specified user objectives but also any implicit or unspecified user requirements. The existence of such implicit requirements could be particularly common in settings where the user's understanding of the task model may differ from the agent's estimate of the model. Under this scenario, the user may incorrectly expect some agent behavior to be inevitable or guaranteed. This paper addresses such expectation mismatch in the presence of differing models by capturing the possibility of unspecified user subgoal in the context of a task captured as a Markov Decision Process (MDP) and querying for it as required. Our method identifies bottleneck states and uses them as candidates for potential implicit subgoals. We then introduce a querying strategy that will generate the minimal number of queries required to identify a policy guaranteed to achieve the underlying goal. Our empirical evaluations demonstrate the effectiveness of our approach in inferring and achieving unstated goals across various tasks.

Problem

• Human-Robot Interaction
• Unexpressed Human Needs
• Misunderstanding Resolution

Innovation

• Markov Decision Process
• Implicit Goal Recognition
• Efficient Inquiry Minimization