PEXIS: Probabilistic experience representation based adaptive interaction system for personal robots

Inamura, Tetsunari; Inaba, Masayuki; Inoue, H.

doi:10.1002/scj.10034

Cited by 18 publications

(7 citation statements)

References 9 publications

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“…Ref. [29] introduced a type of human-robot interactive systems, and in this system the behavioral knowledge and the environment knowledge needed by the robot were collected by statistical models. Evertsz et al [30] introduced a type of human behavior modeling tool-rule of engagement (ROE), which supported the representation of meta-knowledge.…”

Section: Data-based Knowledge Representation and Self-learning Abilitymentioning

confidence: 99%

Theory and techniques of data mining in CGF behavior modeling

Yin

Gong

Han

2011

Sci. China Inf. Sci.

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For the large quantity of data, rules and models generated in the course of computer generated forces (CGFs) behavior modeling, the common analytical methods are statistical methods based on the tactical rules, tactical doctrine and empirical knowledge. However, from the viewpoint of data mining, we can find many of these analytical methods are also each-and-every different data mining methods. In this paper, we survey the data mining theory and techniques that have appeared in the course of CGF behavior modeling from the viewpoint of data mining. Further, we redefine and reclassify these theories and techniques according to the research requirements of data mining (the knowledge structure of data mining). We aim to help the CGF investigators to learn the key points and the research methods of the data intelligent processing in the CGF behavior modeling, and thus we can provide the references for the colleagues who are engaged in the research and practice of the CGF behavior modeling by this article. We also introduce our ongoing work on the CGF behavior modeling based on data mining.

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Section: Data-based Knowledge Representation and Self-learning Abilitymentioning

confidence: 99%

Theory and techniques of data mining in CGF behavior modeling

Yin

Gong

Han

2011

Sci. China Inf. Sci.

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“…Dynamic Bayesian networks are used in expanded cases to handle time series data [12]. Figure 5 shows a flowchart used for generating behaviors of SELF that consists of four phases, referring to a report by Inamura: Observation, Learning, Reasoning, and Introspection [13][14][15].…”

Section: Behavior Generator For Selfmentioning

confidence: 99%

Autonomous Behavior Generator for a Companion Robot: Self

Mori

Kubota

Inoue

2009

KANSEI Engineering International

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“…These studies often focus on the recognition process: They proposed methods to recognize the object by using a pointing gesture, an utterance, and stored object information [7][8] [9]. Moreover, some studies integrated the recognition process with the indication process [10] [11], but they could not dynamically handle environments where the locations of objects and people change; that is, they only performed pre-implemented gestures and utterances.…”

Section: Related Workmentioning

confidence: 99%

Natural deictic communication with humanoid robots

Sugiyama

Kanda²,

Imai

et al. 2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-A simple view of deictic communication only includes the indication process and recognition process: a person points at an object and says something about it such as "look at this," and then the other person recognizes the pointing gesture and pays attention to the indicated object. However, this simple view lacks three important processes: attention synchronization, context focus, and believability establishment. We refer to these three processes as "facilitation processes" and implement them in a humanoid robot with a motion capturing system. An experiment with 30 subjects revealed that the facilitation processes make deictic communication natural.I. INTRODUCTION The aim of our research is to develop "communication robots" that naturally interact with humans and support daily human activities based on advanced interaction capabilities with their human-like bodies. Since the target audience of a communication robot is ordinary people who do not have specialized computing and engineering knowledge, a conversational interface using both verbal and non-verbal expressions is becoming more important. For example, the precedent research works have shown the effectiveness of facial expressions [1], eye-gaze [2], and gestures [3].This study focuses on deictic communication as shown in Fig. 1. In casual communication, people often use reference terms in combination with pointing, such as saying "look at this" while pointing at an object in order to draw others' attention to the object. A simple view of deictic communication is the use of attention-drawing behavior based on pointing gestures with reference terms. People use pointing gestures as one of the mechanisms of joint attention [4]. Reference terms are important for quickly and naturally informing the listener of an indicated object's location. It is important for a communication robot to be capable of both understanding people's attention-drawing behavior and performing attention-drawing behavior for people [5]. This mutual attention-drawing activity is classified into an indication process and a recognition process in Fig. 1.However, this simple view only encompasses the processes related to the interpretation of information, and it lacks a process for facilitating interaction. We believe that three major processes facilitate deictic communication. The first one is attention synchronization. When we point at anAll the author is with ATR Intelligent Robotics and Communication Laboratory, 2-2-2 Hikaridai, Keihanna Science City, Kyoto, Japan (phone:+81-774-95-1492, e-mail: kanda@atr.jp). Osamu Sugiyama and Michita Imai are also with graduate school, Keio University, Kouhokuku, Yokohama City, Kanagawa, Japan. Hiroshi Ishiguro is with Faculty of Engineering, Osaka University, Suita City, Osaka, Japan. object, we pay attention to whether the listener's gaze is following our pointing gesture. Thus, if a communication robot is listening to a person using attention-drawing behavior, it needs to indicate that it is paying attention to the pointing gesture in or...

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PEXIS: Probabilistic experience representation based adaptive interaction system for personal robots

Cited by 18 publications

References 9 publications

Theory and techniques of data mining in CGF behavior modeling

Theory and techniques of data mining in CGF behavior modeling

Autonomous Behavior Generator for a Companion Robot: Self

Natural deictic communication with humanoid robots

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