Towards Intentional Agents to Manipulate Belief, Desire and Commitment Degrees

Saadi, Adel; Sahnoun, Zaïdi

doi:10.1109/aiccsa.2006.205139

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In the past decade, several researchers have integrated psychological studies into robotics research. Such works have estimated the mental states of other people by observing their behaviors and aimed to design a robot with human-like attention capabilities [30,31,32,33,34]. Psycholinguistic studies revealed that speaking status plays an important role in attention, in that a listener’s visual attention is driven by what they hear [35,36].…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory

Tiawongsombat

Jeong

Pirayawaraporn

et al. 2019

Sensors

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Attention capability is an essential component of human–robot interaction. Several robot attention models have been proposed which aim to enable a robot to identify the attentiveness of the humans with which it communicates and gives them its attention accordingly. However, previous proposed models are often susceptible to noisy observations and result in the robot’s frequent and undesired shifts in attention. Furthermore, most approaches have difficulty adapting to change in the number of participants. To address these limitations, a novel attentiveness determination algorithm is proposed for determining the most attentive person, as well as prioritizing people based on attentiveness. The proposed algorithm, which is based on relevance theory, is named the Scalable Hidden Markov Model (Scalable HMM). The Scalable HMM allows effective computation and contributes an adaptation approach for human attentiveness; unlike conventional HMMs, Scalable HMM has a scalable number of states and observations and online adaptability for state transition probabilities, in terms of changes in the current number of states, i.e., the number of participants in a robot’s view. The proposed approach was successfully tested on image sequences (7567 frames) of individuals exhibiting a variety of actions (speaking, walking, turning head, and entering or leaving a robot’s view). From these experimental results, Scalable HMM showed a detection rate of 76% in determining the most attentive person and over 75% in prioritizing people’s attention with variation in the number of participants. Compared to recent attention approaches, Scalable HMM’s performance in people attention prioritization presents an approximately 20% improvement.

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Section: Related Workmentioning

confidence: 99%

Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory

Tiawongsombat

Jeong

Pirayawaraporn

et al. 2019

Sensors

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“…Different BDI architectures [9,10] have evolved from this theory which bestows three mental attitudes for determining agent's responses to perceived changes in the environment: beliefs, desires and intentions.…”

Section: Introductionmentioning

confidence: 99%

Revisiting Character-Based Affective Storytelling under a Narrative BDI Framework

Peinado

Cavazza

Pizzi

2006

Interactive Storytelling

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Abstract. Belief-Desire-Intention (BDI) is a well-known cognitive theory, especially in the field of Software Agents. Modelling characters using software agents has been proven to be a suitable approach for obtaining emergent and autonomous behaviours in Interactive Storytelling. In this paper it is claimed that an effective extension of previous models to the BDI framework is useful for designing intelligent characters. An example shows how internal thoughts and motivations of Madame Bovary's main characters can be more naturally formalised as a cognitive side of the story. A narrative reformulation of BDI theory is needed to avoid the implicit complexity of other proposals.

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2011

Improving Natural Resource Management

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Towards Intentional Agents to Manipulate Belief, Desire and Commitment Degrees

Cited by 3 publications

References 16 publications

Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory

Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory

Revisiting Character-Based Affective Storytelling under a Narrative BDI Framework

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