Natural Language, Mixed-Initiative Personal Assistant Agents

The increasing popularity and use of personal voice assistant technologies, such as Siri and Google Now, is driving and expanding progress toward the long-term and lofty goal of using artificial intelligence to build human-computer dialog systems capable of understanding natural language. While dialog-based systems such as Siri support utterances communicated through natural language, they are limited in the flexibility they afford to the user in interacting with the system and, thus, support primarily action-requesting and information-seeking tasks. Mixed-initiative interaction, on the other hand, is a flexible interaction technique where the user and the system act as equal participants in an activity, and is often exhibited in human-human conversations. In this paper, we study user support for mixed-initiative interaction with dialog-based systems through natural language using a bag-of-words model and k-nearest-neighbor classifier. We study this problem in the context of a toolkit we developed for automated, mixed-initiative dialog system construction, involving a dialog authoring notation and management engine based on lambda calculus, for specifying and implementing task-based, mixed-initiative dialogs. We use ordering at Subway through natural language, human-computer dialogs as a case study. Our results demonstrate that the dialogs authored with our toolkit support the end user\u27s completion of a natural language, human-computer dialog in a mixed-initiative fashion. The use of natural language in the resulting mixed-initiative dialogs afford the user the ability to experience multiple self-directed paths through the dialog and makes the flexibility in communicating user utterances commensurate with that in dialog completion paths---an aspect missing from commercial assistants like Siri

Human gesture classification by brute-force machine learning for exergaming in physiotherapy

In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods

Data association and occlusion handling for vision-based people tracking by mobile robots

This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets