Attentional Model for Perceiving Social Context in Intelligent Environments

Maisonnasse, Jérôme; Gourier, Nicolas; Brdiczka, Oliver; Reignier, Patrick

doi:10.1007/0-387-34224-9_20

Cited by 5 publications

(8 citation statements)

References 7 publications

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“…We have built upon the computational attention model initially proposed in [11] that computes the attention matrix, i.e. a model of how the attention of each person is distributed among the different elements, persons and objects, of his/her environment.…”

Section: Discussionmentioning

confidence: 99%

“…To that end, we build upon a computational model of attention that was earlier proposed in [11]. This attention model was initially developed in the context of ambient applications and pervasive systems.…”

Section: B Related Work and Contributionsmentioning

confidence: 99%

“…Then, section III outlines the attention model originally proposed in [11] and that is used throughout the paper. Section IV describes the implementation details of the model while section V illustrates via three case studies how the attention matrix and the attention field can be used in HRM.…”

Section: Outline Of the Papermentioning

confidence: 99%

“…In 2006, [11] proposed a computational model of attention that was later detailed in [17]. As mentioned earlier, this attentional model, henceforth called AM, was initially developed in the context of ambient applications and pervasive systems.…”

Section: Attentional Modelmentioning

confidence: 99%

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Human-Robot Motion: An attention-based navigation approach

Fraichard

Paulin

Reignier

2014

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

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Abstract-Mobile robot companions are service robots that are mobile and designed to share our living space. For such robots, mobility is essential and their coexistence with humans adds new aspects to the mobility issue: the first one is to obtain appropriate motion and the second one is interaction through motion. We encapsulate these two aspects in the term Human-Robot Motion (HRM) with reference to Human-Robot Interaction. The long-term issue is to design robot companions whose motions, while remaining safe, are deemed appropriate from a human point of view. This is the key to the acceptance of such systems in our daily lives. The primary purpose of this paper is to explore how the psychological concept of attention can be taken into account in HRM. To that end, we build upon an existing model of attention that computes an attention matrix that describes how the attention of each person is distributed among the different elements, persons and objects, of his/her environment. Using the attention matrix, we propose the novel concept of attention field that can be viewed as an attention predictor. Using different case studies, we show how the attention matrix and the attention field can be used in HRM.

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Section: Discussionmentioning

confidence: 99%

Section: B Related Work and Contributionsmentioning

confidence: 99%

Section: Outline Of the Papermentioning

confidence: 99%

Section: Attentional Modelmentioning

confidence: 99%

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Human-Robot Motion: An attention-based navigation approach

Fraichard

Paulin

Reignier

2014

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

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“…We used a network of cameras and microphones to maintain a situation model describing the activity of one or more humans. In particular, situations were defined using a model of shared attention proposed by Maisonnasse [18]. The sensors on the AIBO robot were used to drive learning.…”

Section: Methodsmentioning

confidence: 99%

Learning polite behavior with situation models

Barraquand

Crowley

2008

Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction

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In this paper, we describe experiments with methods for learning the appropriateness of behaviors based on a model of the current social situation. We first review different approaches for social robotics, and present a new approach based on situation modeling. We then review algorithms for social learning and propose three modifications to the classical Q-Learning algorithm. We describe five experiments with progressively complex algorithms for learning the appropriateness of behaviors. The first three experiments illustrate how social factors can be used to improve learning by controlling learning rate. In the fourth experiment we demonstrate that proper credit assignment improves the effectiveness of reinforcement learning for social interaction. In our fifth experiment we show that analogy can be used to accelerate learning rates in contexts composed of many situations. Categories and Subject Descriptors Situated Social Common SenseWith current technology, systems and services are unable to discriminate between appropriate and inappropriate behaviors. As a result, most attempts at proactive user services produce systems that are highly disruptive of human activity. In short, computing systems lack social common sense.Common sense is the collection of shared concepts and ideas that are accepted as correct by a community of people. Social common sense refers to the shared rules for polite, social interaction that implicitly rule behavior within a social group. To a large extent, such common sense is developed using implicit feedback during interaction between individuals. Our goal in this research is to develop methods to endow an artificial agent with the ability to acquire social common sense using the implicit feedback obtained from interaction with people. We believe that such methods can provide a foundation for socially polite man-machine interaction, and ultimately for other forms of cognitive abilities.In this paper, we propose to focus on a key aspect of social common sense: the ability to act appropriately in social situations. In this work, we have sought to train an association between behavior and social situation. Our approach for modeling social situations is inspired by the cognitive models for situation proposed by Johnson-Laird [14] in which situations are modeled as relations between entities. In previous work, we have generalized situation models with the introduction of the concept of "role" [8] and experimented with the use of machine learning techniques for automatically acquiring situation models. In this paper we extend this approach to the problem of learning social common sense. It is our intention that these methods may be used with any system that can spontaneously act to propose information or services.Rules for polite social interaction tend to be highly dependent on context, as well as specific to individuals or groups. Thus we have sought methods that would allow systems to learn the appropriateness of actions using the natural social feedback that people provide in mo...

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Modelling Shared Knowledge and Shared Knowledge Awareness in CSCL Scenarios Through Automated Argumentation Systems

González

Chesñevar

Collazos

et al. 2007

Groupware: Design, Implementation, and Use

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Abstract.Over the last few years, argumentation systems have been gaining increasing importance in several areas of Artificial Intelligence, mainly as a vehicle for facilitating rationally justifiable decision making when handling incomplete and potentially inconsistent information. Argumentation provides a sound model for dialectical reasoning, which underlies discussions among students when solving tasks collaboratively in a CSCL environment. In this setting, we identify the problem of constructing Shared Knowledge and its related Shared Knowledge Awareness. While Shared Knowledge refers to the common knowledge students acquire when they work in a collaborative activity, Shared Knowledge Awareness is associated with the consciousness on the Shared Knowledge that a particular student has. This paper presents a novel approach to model Shared Knowledge construction and the associated Shared Knowledge Awareness through an automated argumentation system.

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Attentional Model for Perceiving Social Context in Intelligent Environments

Cited by 5 publications

References 7 publications

Human-Robot Motion: An attention-based navigation approach

Human-Robot Motion: An attention-based navigation approach

Learning polite behavior with situation models

Modelling Shared Knowledge and Shared Knowledge Awareness in CSCL Scenarios Through Automated Argumentation Systems

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