A Multi-Agent Simulation of Collaborative Air Traffic Flow Management

Wolfe, Shawn R.; Jarvis, Peter; Enomoto, Francis Y.; Sierhuis, Maarten; Putten, Bart-Jan van

doi:10.4018/9781605662268.ch018

Cited by 6 publications

(14 citation statements)

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“…To the best of our knowledge, limited attention has been payed to the scalability of BDI agents. For example Wolfe et al (2008) investigate the use of BDI agents in air-traffic flow management and conclude that an overuse of the BDI paradigm results in scalability limitations and draw guidelines for the use of the BDI agents.…”

Section: Related Workmentioning

confidence: 99%

“…Bartish and Thevathayan (2002) Figure 8: Scalability of Framework using BDI agents for controlling game characters and arrive at comparable linear scalability results. Wolfe et al (2008) report on a framework for air-traffic control using BDI agents. The authors identify the execution properties of the underlying BDI agent implementation as one of the important sources for the inefficiency of their framework.…”

Section: Scalabilitymentioning

confidence: 99%

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Towards Simulating Heterogeneous Drivers with Cognitive Agents

Noroozian

Hindriks

Jonker

2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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Every driver behaves differently in traffic. However, when it comes to micro-simulation of drivers with a high level of detail no framework manages to model the complexities of various driving styles as well as scale up to larger simulations. We propose a framework of micro-simulation combined with cognitive agents to facilitate such simulation tasks. Our goal is to (i) model individual drivers, and (ii) use this framework for the purpose of simulating realistic highway traffic with heterogeneous driving styles. The challenge is therefore to create a framework that facilitates such complex modeling and supports large scale simulations. We evaluate the framework from two perspectives. First, the ability to represent, model and simulate dissimilar drivers in addition to study and compare emerging behavior. Second, the scalability of the framework. We report on our experiences with the framework, outline several challenges and identify future areas for development.

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Section: Related Workmentioning

confidence: 99%

Section: Scalabilitymentioning

confidence: 99%

Towards Simulating Heterogeneous Drivers with Cognitive Agents

Noroozian

Hindriks

Jonker

2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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“…Conversely, if an aircraft is airborne or airborne delay is preferred then an explicit constraint to prevent ground delay is added in Eq. (14). Since the preference is either for ground delay or airborne delay only one of Eq.…”

Section: Appendixmentioning

confidence: 99%

“…(14) shows up in the formulation. 0 = G D (14) Ground delay is also used to specify the entry time to the first sector in Eq. (15).…”

Section: Appendixmentioning

confidence: 99%

“…Previous work proposes increasing the involvement of users in the traffic flow management decision-making process above the level of collaborative decision-making and to additionally include local situations to realize further benefit. 7,8,12,14 One concept allows users to send a prioritized list of alternative routing options, which the traffic managers incorporate in reroutes assigned to flights. 10 An agent-based model 16 was used to conclude that the service provider cannot make the best traffic flow management decision without collaboration from airlines.…”

Section: Introductionmentioning

confidence: 99%

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Airline and Service Provider Collaborative Algorithms for Flight Route and Delay Decisions

Henderson

Idris

Kicinger

et al. 2009

AIAA Guidance, Navigation, and Control Conference

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BDI agents in social simulations: a survey

Adam¹,

Gaudou²

2016

The Knowledge Engineering Review

113

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International audienceModeling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the resulting complexity of models, Axelrod promoted the KISS principle: “Keep It Simple, Stupid”. But the community is divided and a new principle appeared: KIDS, “Keep It Descriptive Simple”. Richer models were thus developed for a variety of phenomena, while agent cognition still tends to be modelled with simple reactive particle-like agents. This is not always appropriate, in particular in the social sciences trying to account for the complexity of human behaviour. One solution is to model humans as BDI agents, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation. This paper provides a methodological guide to the use of BDI agents in social simulations, and an overview of existing methodologies and tools for using them

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A Multi-Agent Simulation of Collaborative Air Traffic Flow Management

Cited by 6 publications

References 0 publications

Towards Simulating Heterogeneous Drivers with Cognitive Agents

Towards Simulating Heterogeneous Drivers with Cognitive Agents

Airline and Service Provider Collaborative Algorithms for Flight Route and Delay Decisions

BDI agents in social simulations: a survey

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