Integrating BDI Agents with Agent-Based Simulation Platforms

Singh, Dhirendra; Padgham, Lin; Logan, Brian

doi:10.1007/s10458-016-9332-x

Cited by 52 publications

(27 citation statements)

References 27 publications

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“…In [19], a three-layered integration framework between BDI agents and ABMs systems applied to some domains including bushfire evacuation is presented. This work is mainly based on Jack as the BDI engine and MATSim as the ABM simulator.…”

Section: Discussionmentioning

confidence: 99%

“…The DR problem is considered as an MAS problem since it implies the design of coordination strategies and communication between agents [16][17][18]; furthermore, a central point is to define agent architectures to cope with it. [14,18,19]. On the other hand, ABM are proposed as systems aiming to solve the DR problem from the point of view of simulating the human response facing a particular disaster or even combined calamities [5,14].…”

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confidence: 99%

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Bridging the Gap between ABM and MAS: A Disaster-Rescue Simulation Using Jason and NetLogo

Luna-Ramírez

Fasli

2018

Computers

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An agent is an autonomous computer system situated in an environment to fulfill a design objective. Multi-Agent Systems aim to solve problems in a flexible and robust way by assembling sets of agents interacting in cooperative or competitive ways for the sake of possibly common objectives. Multi-Agent Systems have been applied to several domains ranging from many industrial sectors, e-commerce, health and even entertainment. Agent-Based Modeling, a sort of Multi-Agent Systems, is a technique used to study complex systems in a wide range of domains. A natural or social system can be represented, modeled and explained through a simulation based on agents and interactions. Such a simulation can comprise a variety of agent architectures like reactive and cognitive agents. Despite cognitive agents being highly relevant to simulate social systems due their capability of modelling aspects of human behaviour ranging from individuals to crowds, they still have not been applied extensively. A challenging and socially relevant domain are the Disaster-Rescue simulations that can benefit from using cognitive agents to develop a realistic simulation. In this paper, a Multi-Agent System applied to the Disaster-Rescue domain involving cognitive agents based on the Belief-Desire-Intention architecture is presented. The system aims to bridge the gap in combining Agent-Based Modelling and Multi-Agent Systems approaches by integrating two major platforms in the field of Agent-Based Modeling and Belief-Desire Intention multi-agent systems, namely, NetLogo and Jason.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Bridging the Gap between ABM and MAS: A Disaster-Rescue Simulation Using Jason and NetLogo

Luna-Ramírez

Fasli

2018

Computers

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“…However, to only consider the decision-maker's preferences to accurately represent him/her is not enough, and it is not difficult to recognize that a poor representation can lead to deficient processes and, as a consequence, to decisions of lower quality. Most of the current approaches in GDSS only consider the preferences of decision-makers, forgetting what their intentions and objectives are (widely used in the famous BDI architectures [50]). To consider the intentions of a decision-maker can radically affect the outcome of the decision.…”

Section: Representing the Decision-makersmentioning

confidence: 99%

Group decision support systems for current times: Overcoming the challenges of dispersed group decision-making

et al. 2021

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We are living a change of paradigm regarding decision-making. On the one hand, there is a growing need to make decisions in group at both professional and personal levels, on the other hand, it is increasingly difficult for decision-makers to meet at the same place and at the same time. The Web-based Group Decision Support Systems intend to overcome this limitation, allowing decision-makers to contribute to the decision process anytime and anywhere. However, they have been defined inadequately which has been compromising its success. This work discusses the current Group Decision Support Systems limitations in terms of challenges and possible impediments for their acceptance by the organizations and propose a conceptual definition of a Web-based Group Decision Support System that intends to overcome the existing limitations and help them to affirm as a reliable and useful tool. In addition, some crucial topics are addressed, such as communication and perception, that are essential and sometimes forgotten in the support of dispersed decision-makers. We concluded that there are still some limitations, mostly in terms of models and applications, that prevent the design of higher quality systems.

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“…Actually, there is a significant research interest in integrating BDI agents with agent‐based simulation platforms (Singh, Padgham, & Logan, ). BDI agents, in general, and Jason AOP language, in particular, were applied to the development of models and simulations of social systems (Adam & Gaudou, ; Caballero, Bota, & Gmez‐Skarmeta, ).…”

Section: Related Workmentioning

confidence: 99%

Multi‐agent modelling and simulation of graph‐based predator–prey dynamic systems: A BDI approach

et al. 2018

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We propose a new framework based on Belief‐Desire‐Intention multi‐agent systems for the macroscopic modelling and simulation of continuous dynamic systems. The main idea is to break down the target system model into a collection of autonomous and loosely coupled interacting components endowed with clean message‐based interfaces and local intelligence. Each component is then mapped to a Belief‐Desire‐Intention agent that captures its state as a set of logical facts and its behavioural patterns as a set of plans. The system model can be described as a multi‐agent programme that is specified using the state‐of‐the‐art Jason agent‐oriented programming language. The approach is evaluated by considering a generalized graph‐based model of predator–prey systems. Our approach supports the configuration of the multi‐agent model with various differential equations integration methods, as required by the specific problem.

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Integrating BDI Agents with Agent-Based Simulation Platforms

Cited by 52 publications

References 27 publications

Bridging the Gap between ABM and MAS: A Disaster-Rescue Simulation Using Jason and NetLogo

Bridging the Gap between ABM and MAS: A Disaster-Rescue Simulation Using Jason and NetLogo

Group decision support systems for current times: Overcoming the challenges of dispersed group decision-making

Multi‐agent modelling and simulation of graph‐based predator–prey dynamic systems: A BDI approach

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