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• W2128426625 abstract "This thesis desires to enable a new kind of interaction between humans and computational agents, such as robots or computers, by allowing the agent to anticipate and adapt to human intent. In the future, more robots may be deployed in situations that require collaboration with humans, such as scientific exploration, search and rescue, hospital assistance, and even domestic care. These situations require robots to work together with humans, as part of a team, rather than as a stand-alone tool. The intent recognition capability is necessary for computational agents to play a more collaborative role in human-robot interactions, moving beyond the standard master-slave relationship of humans and computers today. We provide an innovative capability for recognizing human intent, through statistical plan learning and online recognition. We approach the plan learning problem by employing unsupervised learning to automatically determine the activities in a plan based on training data. The plan activities are described by a mixture of multivariate probability densities. The number of distributions in the mixture used to describe the data is assumed to be given. The training data trajectories are fed again through the activities’ density distributions to determine each possible sequence of activities that make up a plan. These activity sequences are then summarized with temporal information in a temporal plan network, which consists of a network of all possible plans. Our approach to plan recognition begins with formulating the temporal plan network as a hidden Markov model. Next, we determine the most likely path using the Viterbi algorithm. Finally, we refer back to the temporal plan network to obtain predicted future activities. Our research presents several innovations: First, we introduce a modified representation of temporal plan networks that incorporates probabilistic information into the state space and temporal representations. Second, we learn plans from actual data, such that the notion of an activity is not arbitrarily or manually defined, but is determined by the characteristics of the data. Third, we develop a recognition algorithm that can perform recognition continuously by making probabilistic updates. Finally, our recognizer not only identifies previously executed activities, but also pre-" @default.
• W2128426625 created "2016-06-24" @default.
• W2128426625 creator A5089250955 @default.
• W2128426625 date "2007-08-23" @default.
• W2128426625 modified "2023-09-24" @default.
• W2128426625 title "Unsupervised learning and recognition of physical activity plans" @default.
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• W2128426625 hasPublicationYear "2007" @default.
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