Discovering Underlying Plans Based on Distributed Representations of Actions
This addresses the challenge of plan recognition in real-world applications where building complete plan libraries or domain models is difficult, offering a more flexible approach.
The paper tackles the problem of plan recognition when target plans are not in existing libraries and complete domain models are unavailable, by learning vector representations of actions from corpora and discovering plans based on these representations, achieving effectiveness demonstrated through empirical comparisons in three planning domains.
Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or domain models in hand. Previous approaches either discover plans by maximally "matching" observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing domain models to best explain the observed actions, assuming complete domain models are available. In real world applications, however, target plans are often not from plan libraries and complete domain models are often not available, since building complete sets of plans and complete domain models are often difficult or expensive. In this paper we view plan libraries as corpora and learn vector representations of actions using the corpora; we then discover target plans based on the vector representations. Our approach is capable of discovering underlying plans that are not from plan libraries, without requiring domain models provided. We empirically demonstrate the effectiveness of our approach by comparing its performance to traditional plan recognition approaches in three planning domains.