Enhancing Multi-Agent Based Simulation with Human-Like Decision Making Strategies

Norling, Emma; Sonenberg, Liz; Rönnquist, Ralph

doi:10.1007/3-540-44561-7_16

Cited by 36 publications

(24 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, the RPD theory has been applied in the area of multi-agent systems as well, see e.g. [15,16,28], although these applications have not addressed virtual storytelling as yet.…”

Section: Discussionmentioning

confidence: 99%

Combining rational and biological factors in virtual agent decision making

2009

View full text Add to dashboard Cite

To enhance believability of virtual agents, this paper presents an agent-based modelling approach for decision making, which integrates rational reasoning based on means-end analysis with personal psychological and biological aspects. The agent model developed is a combination of a BDI-model and a utility-based decision model in the context of specific desires and beliefs. The approach is illustrated by addressing the behaviour of violent criminals, thereby creating a model for virtual criminals. Within a number of simulation experiments, the model has been tested in the context of a street robbery scenario. In addition, a user study has been performed, which confirms the fact that the model enhances believability of virtual agents. Keywords Virtual agents · Decision making · Rational vs. nonrationalA shorter, preliminary version of this paper appeared as: Bosse, T., Gerritsen, C., and Treur, J., Integrating rational choice and subjective biological and psychological factors in criminal behaviour models.

show abstract

“…Recently, the RPD theory has been applied in the area of multi-agent systems as well, see e.g. [15,16,28], although these applications have not addressed virtual storytelling as yet.…”

Section: Discussionmentioning

confidence: 99%

Combining rational and biological factors in virtual agent decision making

2009

View full text Add to dashboard Cite

show abstract

“…Once the situation is recognized, properly selecting a course of action almost always automatically follows. The course of action is usually the one that was successful in the previous, similar situation [18,25].…”

Section: Action Evaluationmentioning

confidence: 99%

“…Other computational RPD models use different ways (e.g. crisp matching [25], neural network [22,31]) to achieve the matching between the current situation and past experience, but they either cannot capture partial matching or assume that all cues are crisp values. Our approach can overcome these limitations.…”

Section: Similarity Measuresmentioning

confidence: 99%

“…There are also several studies in which the computational RPD model was developed and integrated with agent technologies. Norling et al employed the computational RPD in the Belief-Desire-Intention agent framework as a more realistic way for simulating human societies [25]. More recently, Yen et al developed an agent architecture (R-CAST) that includes a computational RPD model that extends the RPD model in order to support human agent collaboration and the sharing of relevant information within a team [10,42].…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

A fuzzy logic-based computational recognition-primed decision model

Massanari

Ager

et al. 2007

Information Sciences

View full text Add to dashboard Cite

“…There have been several attempts at integrating RPD with agent technologies. 24,25 For instance, Norling et al employed RPD in a BDI agent framework as a more realistic way of simulating human societies. 24 More recently, Yen et al implemented an RPD-enabled team-oriented agent architecture~R-CAST!…”

Section: Brief Introduction To the Rpd Modelmentioning

confidence: 99%

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition-primed decision model

Ji¹,

Ying²,

Yen³

et al. 2007

Int. J. Intell. Syst.

View full text Add to dashboard Cite

Discovering unknown adverse drug reactions~ADRs! in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well-known human decision-making model called Recognition-Primed Decision~RPD!. We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs.

show abstract

Enhancing Multi-Agent Based Simulation with Human-Like Decision Making Strategies

Cited by 36 publications

References 10 publications

Combining rational and biological factors in virtual agent decision making

Combining rational and biological factors in virtual agent decision making

A fuzzy logic-based computational recognition-primed decision model

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition-primed decision model

Contact Info

Product

Resources

About