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.
While DUP focuses on learning models from plan corpora, some recent work looked at using crowdsourcing to acquire domain models. For example, @cite_0 introduce Legion:AR, which combines the benefits of automatic and human activity labeling for robust and deployable activity recognition. The system exploits an active learning approach @cite_12 in which automatic activity recognition is augmented with on-demand activity labels from the crowd when an observed activity cannot be confidently classified. By engaging a group of people, Legion:AR is able to label activities as they occur more reliably than a single person can, especially in complex domains with multiple actors performing activities quickly. @cite_26 built a crowdsourcing based system called ARchitect, using the crowd to capture the dependency structure of the actions that make up activities. Such crowd-sourcing methods can complement the plan-corpus based approach proposed in DUP.
@cite_0 @cite_26 @cite_12
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Systems that automatically recognize human activities offer the potential of timely, task-relevant information and support. For example, prompting systems can help keep people with cognitive disabilities on track and surveillance systems can warn of activities of concern. Current automatic systems are difficult to deploy because they cannot identify novel activities, and, instead, must be trained in advance to recognize important activities. Identifying and labeling these events is time consuming and thus not suitable for real-time support of already-deployed activity recognition systems. In this paper, we introduce Legion:AR, a system that provides robust, deployable activity recognition by supplementing existing recognition systems with on-demand, real-time activity identification using input from the crowd. Legion:AR uses activity labels collected from crowd workers to train an automatic activity recognition system online to automatically recognize future occurrences. To enable the crowd to keep up with real-time activities, Legion:AR intelligently merges input from multiple workers into a single ordered label set. We validate Legion:AR across multiple domains and crowds and discuss features that allow appropriate privacy and accuracy tradeoffs. Activity recognition can provide computers with the context underlying user inputs, enabling more relevant responses and more fluid interaction. However, training these systems is difficult because it requires observing every possible sequence of actions that comprise a given activity. Prior work has enabled the crowd to provide labels in real-time to train automated systems on-the-fly, but numerous examples are still needed before the system can recognize an activity on its own. To reduce the need to collect this data by observing users, we introduce ARchitect, a system that uses the crowd to capture the dependency structure of the actions that make up activities. Our tests show that over seven times as many examples can be collected using our approach versus relying on direct observation alone, demonstrating that by leveraging the understanding of the crowd, it is possible to more easily train automated systems. Recognizing human activities from wearable sensor data is an important problem, particularly for health and eldercare applications. However, collecting sufficient labeled training data is challenging, especially since interpreting IMU traces is difficult for human annotators. Recently, crowdsourcing through services such as Amazon's Mechanical Turk has emerged as a promising alternative for annotating such data, with active learning (Cohn, Ghahramani, and Jordan 1996) serving as a natural method for affordably selecting an appropriate subset of instances to label. Unfortunately, since most active learning strategies are greedy methods that select the most uncertain sample, they are very sensitive to annotation errors (which corrupt a significant fraction of crowdsourced labels). This paper proposes methods for robust active learning under these conditions. Specifically, we make three contributions: 1) we obtain better initial labels by asking labelers to solve a related task; 2) we propose a new principled method for selecting instances in active learning that is more robust to annotation noise; 3) we estimate confidence scores for labels acquired from MTurk and ask workers to relabel samples that receive low scores under this metric. The proposed method is shown to significantly outperform existing techniques both under controlled noise conditions and in real active learning scenarios. The resulting method trains classifiers that are close in accuracy to those trained using ground-truth data.