Richard G. Freedman

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We examine new ways to perform plan recognition (PR) using natural language processing (NLP) techniques. PR often focuses on the structural relationships between consecutive observations and ordered activities that comprise plans. However , NLP commonly treats text as a bag-of-words, omitting such structural relationships and using topic models to break(More)
The use of robots in stroke rehabilitation has become a popular trend in rehabilitation robotics. However, despite the acknowledged value of customized service for individual patients, research on programming adaptive therapy for individual patients has received little attention. The goal of the current study is to model teletherapy sessions in the form of(More)
We consider ways to improve the performance of unsuper-vised plan and activity recognition techniques by considering temporal and object relations in addition to postural data. Temporal relationships can help recognize activities with cyclic structure and are often implicit because plans have degrees of ordering actions. Relations with objects can help(More)
The ability to identify what humans are doing in the environment is a crucial element of responsive behavior in human-robot interaction. We examine new ways to perform plan recognition (PR) using natural language processing (NLP) techniques. PR often focuses on the structural relationships between consecutive observations and ordered activities that(More)
I. INTRODUCTION For robots to properly interact with humans, it is important that they are able to recognize users' plans and activities so that they may respond accordingly. Many activity recognition (AR) algorithms involve signal processing [1] or supervised learning [2], [3] to label raw sensor data with a human-defined action, but these methods restrict(More)
Unsupervised machine learning methods are useful for identifying clusters of similar inputs with respect to some criteria and giving the inputs within each cluster the same label. However, the results of many such methods rely on parameter choices that can alter the derived classification labels for each input. Verification methods for determining the(More)
Interaction between multiple agents requires some form of coordination and a level of mutual awareness. When computers and robots interact with people, they need to recognize human plans and react appropriately. Plan and goal recognition techniques have focused on identifying an agent's task given a sufficiently long action sequence. However, by the time(More)
The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the(More)