Incorporating BDI Agents into Human-Agent Decision Making Research

Kamphorst, Bart A.; Wissen, Arlette van; Dignum, Virginia

doi:10.1007/978-3-642-10203-5_8

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…BDI is a popular model to explore the representations of human values and ethical decision-making and to control simulations and associated data analysis by autonomous agents (Raja et al, 2012). The BDI agent model has been successfully used in a wide range of applications that require a mix of reactive behaviour and goal-directed reasoning, such as modelling internal dynamic behaviour (Brazier et al, 1999), human-agent decision-making (Kamphorst et al, 2009), flexible conversation management (Wong et al, 2012), ethical decision-making (Raja et al, 2012) and human behaviour reasoning in complex systems (Joo, 2013), among others.…”

Section: Bdi Agent-based Norm Compliance Decision-makingmentioning

confidence: 99%

A belief–desire–intention model for blog users’ negative emotional norm compliance

et al. 2017

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Purpose The purpose of this paper is to understand blog users’ negative emotional norm compliance decision-making in crises (blog users’-NNDC). Design/methodology/approach A belief–desire–intention (BDI) model to evaluate the blog users’-NNDC (the BDI-NNDC model) was developed. This model was based on three social characteristics: self-interests, expectations and emotions. An experimental study was conducted to evaluate the efficiency of the BDI-NNDC model by using data retrieved from a popular Chinese social network called “Sina Weibo” about three major crises. Findings The BDI-NNDC model strongly predicted the Blog users’-NNDC. The predictions were as follows: a self-interested blog user posted content that was targeting his own interests; a blogger with high expectations wrote and commented emotionally negative blogs on the condition that the numbers of negative posts increased, while he ignored the norm when there was relatively less negative emotional news; and an emotional blog user obeyed the norm based on the emotional intentions of the blogosphere in most of the cases. Research limitations/implications The BDI-NNDC model can explain the diffusion of negative emotions by blog users during crises, and this paper shows a way to bridge the social norm modelling and the research of blog users’ activity and behaviour characteristics in the context of “real life” crises. However, the criterion for differentiating blog users according to social characteristics needs to be further revised, as the generalizability of the results is limited by the number of cases selected in this study. Practical implications The current method could be applied to predict emotional trends of blog users who have different social characteristics and it could support government agencies to build strategic responses to crises. Originality/value This paper supports the creation of normative models and engineering methods to predict blog users’-NNDC and mitigate their effect in real-world crises.

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Section: Bdi Agent-based Norm Compliance Decision-makingmentioning

confidence: 99%

A belief–desire–intention model for blog users’ negative emotional norm compliance

et al. 2017

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“…Les simulations peuvent aussi être utilisées pour tenter de reproduire et donc de comprendre le comportement humain. Par exemple (Kamphorst et al, 2009), dans le but de comprendre les processus cognitifs impliqués dans la prise de décision humaine, ont utilisé une plateforme de modélisation de décision existante, Colored Trails ; ils ont ensuite dû l'étendre avec des agents BDI programmés en 2APL afin de gagner le pouvoir explicatif nécessaire.…”

Section: Découverteunclassified

Agents BDI et simulations sociales. Unis pour le meilleur et pour le pire

Adam¹,

Gaudou²,

Hickmott³

et al. 2011

Revue d'intelligence artificielle

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La modélisation et la simulation ont longtemps été dominées par les approches basées sur les équations, jusqu'à l'avènement récent des approches orientées agents. Pour freiner l'augmentation de complexité des modèles que peut entraîner l'utilisation de cette nouvelle approche, la tendance est à la sursimplification des modèles. Des modèles plus descriptifs ont cependant été développés pour une variété de phénomènes, mais la cognition des agents est encore trop souvent négligée alors qu'elle a une grande importance dans certains domaines, en particulier en sciences humaines et sociales. La solution que nous proposons dans cet article est d'utiliser des agents BDI. Nous montrons qu'il s'agit d'un paradigme expressif, réaliste et simple qui apporte de nombreux bénéfices à la simulation à base d'agents. ABSTRACT. Modeling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the increasing complexity of models resulting from this new approach, the trend has been to oversimplify the models. Some more descriptive models have still been developed for various phenomenons, but the cognition of agents is too often neglected, despite its great importance in some fields, such as Social and Human Sciences. The solution that we put forward in this paper is to use BDI agents. We will show that this is an expressive, realistic yet simple paradigm that thus offers numerous benefits to agent-based simulation.

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“…We have to look at the choice of the BDI agent because it is close to human behavior. [8] [9]. So the connexion of the latter with the REST Web Service allows him to publish his capacity as a Web Service.…”

Section: Introductionmentioning

confidence: 99%

Jason-RS, A Collaboration Between Agents and an IoT Platform

Rafalimanana

Razafindramintsa

Cherrier

et al. 2020

Machine Learning for Networking

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In this article we start from the observation that REST services are the most used as tools of interoperability and orchestration in the Internet of Things (IoT). But REST does not make it possible to inject artificial intelligence into connected objects, ie it cannot allow autonomy and decision-making by the objects themselves. To define an intelligence to a connected object, one can use a Beleive Desire Intention agent (BDI an intelligent agent that adopts human behavior) such as Jason Agentspeak. But Jason AgentSpeak does not guarantee orchestration or choreography between connected objects. There are platforms for service orchestration and choreography in IoT, still the interconnection with artificial intelligence needs to be built. In this article, we propose a new approach called Jason-RS. It is a result of pairing Jason BDI agent with the web service technologies to exploit the agent capacity as a service, Jason-RS turn in Java SE and it does not need any middleware. The architecture that we propose allows to create the link between Artificial Intelligence and Services choreography to reduce human intervention in the service choreography. In order to validate the proposed approach, we have interconnected the Iot BeC 3 platform and the REST agent (Jason-RS). The decision-making faculty offered by Jason-RS is derived from the information sent by the objects according to the different methods of REST (GET, POST, PUT, and DELETE) that Jason-RS offers. As a result, the objects feed the inter-agent collaborations and decision-making inside the agent. Finally, we show that Jason-RS allows the Web of Objects to power complex systems such as an artificial intelligence responsible for processing data. This performance is promising.

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Incorporating BDI Agents into Human-Agent Decision Making Research

Cited by 7 publications

References 23 publications

A belief–desire–intention model for blog users’ negative emotional norm compliance

A belief–desire–intention model for blog users’ negative emotional norm compliance

Agents BDI et simulations sociales. Unis pour le meilleur et pour le pire

Jason-RS, A Collaboration Between Agents and an IoT Platform

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