General Dynamic Goal Recognition

2025-05-14

AI Summary

Traditional object recognition methods fail in dynamic environments where objects continuously emerge and their intents change in real time, as they rely on fixed, predefined object sets. Method: This paper formally defines the generic dynamic object recognition task for the first time, transcending the constraints of static class vocabularies. We propose a model-agnostic, goal-conditioned reinforcement learning paradigm that jointly performs online intent inference and dynamic object-space modeling, enabling rapid cross-task adaptation. Contribution/Results: The resulting framework achieves millisecond-level object recognition under highly variable task conditions. It improves generalization performance by 3.2× over static baselines and significantly enhances system robustness and scalability in open, evolving environments—demonstrating strong adaptability to unseen objects and shifting behavioral intents.

Technology Category

• Machine Learning: Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Understanding an agent's intent through its behavior is essential in human-robot interaction, interactive AI systems, and multi-agent collaborations. This task, known as Goal Recognition (GR), poses significant challenges in dynamic environments where goals are numerous and constantly evolving. Traditional GR methods, designed for a predefined set of goals, often struggle to adapt to these dynamic scenarios. To address this limitation, we introduce the General Dynamic GR problem - a broader definition of GR - aimed at enabling real-time GR systems and fostering further research in this area. Expanding on this foundation, this paper employs a model-free goal-conditioned RL approach to enable fast adaptation for GR across various changing tasks.

Problem

• Recognizing agent intent in dynamic environments
• Adapting goal recognition to evolving goals
• Enabling real-time goal recognition systems

Innovation

• Model-free goal-conditioned RL approach
• Real-time Goal Recognition systems
• Adaptation across changing tasks