A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features
This addresses the challenge of goal recognition for intelligent agents in physical environments, though it appears incremental as it builds on existing few-shot and transfer learning methods.
The paper tackles the problem of inferring an agent's intent from navigation sequences by proposing a novel approach that combines few-shot and transfer learning with cross-domain spatial features, resulting in improved performance in learning from few samples and generalizing to unseen configurations compared to a deep-learning baseline.
The ability to infer the intentions of others, predict their goals, and deduce their plans are critical features for intelligent agents. For a long time, several approaches investigated the use of symbolic representations and inferences with limited success, principally because it is difficult to capture the cognitive knowledge behind human decisions explicitly. The trend, nowadays, is increasingly focusing on learning to infer intentions directly from data, using deep learning in particular. We are now observing interesting applications of intent classification in natural language processing, visual activity recognition, and emerging approaches in other domains. This paper discusses a novel approach combining few-shot and transfer learning with cross-domain features, to learn to infer the intent of an agent navigating in physical environments, executing arbitrary long sequences of actions to achieve their goals. Experiments in synthetic environments demonstrate improved performance in terms of learning from few samples and generalizing to unseen configurations, compared to a deep-learning baseline approach.