Designing Multiagent Decision Support Systems for Traffic Management

Ossowskí, Sascha; Fernández, Alberto; Serrano, João; Pérez-de-la-Cruz, José-Luís; Belmonte, María‐Victoria; Hernández, Javier Sánchez; Garcı́a-Serrano, Ana; Maseda, José-Manuel

doi:10.1007/3-7643-7363-6_4

Cited by 17 publications

(13 citation statements)

References 10 publications

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“…The first of these levels is discussed in several papers, e.g. the platform called Multi-Agent Environment for Constructing Cooperative Applications-MECCA/UTS- [36], as well as in [57,67,84]. The other two levels are discussed next.…”

Section: Conceptual and Organizational Aspects In Traffic Engineeringmentioning

confidence: 99%

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

Bazzan

2008

Auton Agent Multi-Agent Syst

196

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The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence and multiagent systems in particular. As it is often the case, it is not possible to provide additional capacity, so that a more efficient use of the available transportation infrastructure is necessary. This relates closely to multiagent systems as many problems in traffic management and control are inherently distributed. Also, many actors in a transportation system fit very well the concept of autonomous agents: the driver, the pedestrian, the traffic expert; in some cases, also the intersection and the traffic signal controller can be regarded as an autonomous agent. However, the "agentification" of a transportation system is associated with some challenging issues: the number of agents is high, typically agents are highly adaptive, they react to changes in the environment at individual level but cause an unpredictable collective pattern, and act in a highly coupled environment. Therefore, this domain poses many challenges for standard techniques from multiagent systems such as coordination and learning. This paper has two main objectives: (i) to present problems, methods, approaches and practices in traffic engineering (especially regarding traffic signal control); and (ii) to highlight open problems and challenges so that future research in multiagent systems can address them.

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Section: Conceptual and Organizational Aspects In Traffic Engineeringmentioning

confidence: 99%

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

Bazzan

2008

Auton Agent Multi-Agent Syst

196

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“…At present, a lot of studies deal with the possibility of using fuzzy logic in systems [41]. The model will also take into consideration aspects connected with the reliability of the technical system [22][23][24], the effectiveness of its operation [2], [13], [18], [25], [26], [38], [43] and susceptibility aspects [39], [40], [42].…”

Section: Functioning Of Air Transport Systems -Literature Reviewmentioning

confidence: 99%

Theory and Engineering of Complex Systems and Dependability

Zamojski¹,

Mazurkiewicz²,

Sugier³

et al. 2015

Advances in Intelligent Systems and Computing

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The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing.The publications within "Advances in Intelligent Systems and Computing" are primarily textbooks and proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. Advisory BoardChairman Nikhil R. Pal,

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“…Current applications of multi-agent technology in DSS have been mainly focusing on decentralized frameworks. For instance, Ossowski et al (2004) propose and implement the multi-agent decision support system in transportation management; Vahidov (2006) suggests the use of intelligent agents in situated systems, and Padgham and Winikoff (2002) introduce the Prometheus methodology for building Belief-Desire-Intention (BDI) based distributed agent systems. However, these systems mainly rely on the efficient concurrent control and synchronous communication technologies.…”

Section: Multi-agents Applications In Dssmentioning

confidence: 99%

Application of Multi-agent Technology to Information Systems: An Agent-based Design Architecture for Decision Support Systems

Zhang

2009

AJIS

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One of the most difficult issues in building efficient Information Systems (IS) is the integration of these systems with the organization's other systems. This issue is particularly acute for Decision Support Systems (DSSs). To become more effective and efficient, a DSS must have an open structure to adapt to the dynamic environment. However, current IS, especially DSSs, tend to rely excessively on traditional System Development Life Cycle (SDLC) and this places limitations on current systems' infrastructures. The emergence of multi-agent technology addresses this issue and its applications to IS are becoming highly efficient. In this paper, we introduce a Matrix-Agent connection design, called Agent based Open Connectivity for Decision Support Systems (AOCD), which balances the manageability and flexibility in a system and maximizes system performance.

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Designing Multiagent Decision Support Systems for Traffic Management

Cited by 17 publications

References 10 publications

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

Theory and Engineering of Complex Systems and Dependability

Application of Multi-agent Technology to Information Systems: An Agent-based Design Architecture for Decision Support Systems

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