Multi Agent Based Simulation: Beyond Social Simulation

Davidsson, Paul

doi:10.1007/3-540-44561-7_7

Cited by 140 publications

(87 citation statements)

References 6 publications

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“…Davidsson's [5] findings -combined with Ferrari's [8] -have proved that a mathematical simulation model has to be discarded and restated from the beginning when new variables are added to the system -bring another opportunity niche for the micro-LCS-based simulation presented here.…”

Section: Maxcs: a Multi-agent System That Learns Using Xcsmentioning

confidence: 96%

“…Davidsson [5] has shown how multi-agent based simulation, and other micro simulation techniques, explicitly attempts to model specific behaviors of specific individuals. He compared them favorably to macro simulation techniques that are typically based on mathematical models, where the characteristics of a population are averaged together and the model attempts to simulate changes in these averaged characteristics for the whole population.…”

Section: Maxcs: a Multi-agent System That Learns Using Xcsmentioning

confidence: 99%

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Co-evolutionary Agent Self-Organization for City Traffic Congestion Modeling

Hercog¹

2004

Genetic and Evolutionary Computation – GECCO 2004

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Abstract. This paper introduces a model based on a multi-agent system that learns using the extended classifier system (MAXCS). The agents are informed in the news, after the weather forecast, the congestion levels of the main city avenues and they decide which avenue to use the following day. The results are encouraging, as the agents, both homogeneous and heterogeneous adapt to the congestion thresholds set by the authorities. The main factors for this adaptation are the reward received by the agents and their perception.

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Section: Maxcs: a Multi-agent System That Learns Using Xcsmentioning

confidence: 96%

Section: Maxcs: a Multi-agent System That Learns Using Xcsmentioning

confidence: 99%

Co-evolutionary Agent Self-Organization for City Traffic Congestion Modeling

Hercog¹

2004

Genetic and Evolutionary Computation – GECCO 2004

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“…Arguably, different types of owners for various types of buildings will behave differently in terms of the aforementioned aspects. Thus, the simulation system is realized as a Multi-Agent based Simulation (MABS) [15], where the various owner types are mapped to different agents. The current version of the simulation system maps five particular types of homeowners of either detached or terraced houses.…”

Section: Simulation Approachmentioning

confidence: 99%

Towards an Agent-based Simulation of Building Stock Development for the City of Hamburg

Preisler

Dethlefs

Renz

et al. 2017

Annals of Computer Science and Information Systems

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Abstract-In the context of European climate goals municipalities have an increasing interest in an accurate estimation of current and future energy demand in buildings, as the domestic energy consumption is one of the major adjusting screws for the reduction of electrical and thermal energy consumption, whereas the demand for space heating has the highest impact. As part of the ongoing GEWISS project it is planned to create a geographical information system (GIS) to visualize domestic and industrial heat consumption in the city of Hamburg (Germany) to support political decision making by linking the development of urban areas and the district heating grid. Additionally, it is planned to provide simulation capabilities to offer planning assistance for future development. This paper will present the underlying agent-based simulation system that is used to simulate the development of the building stock. Thereby, the simulation approach and first results regarding the development of the renovation state of the building stock based on a study about the renovation behavior of different types of home-owners of detached and terraced houses will be presented.

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“…Therefore all squares within three units of a wall or obstruction were encoded as no-go squares (0), squares exactly four units away from a wall or obstruction were labelled as wall squares (1), and squares more than four units away from walls as space squares (2). Choice points, at their simplest, are then wall squares that coincide with obtuse corners; where the mouse might wish to change direction (or stop); or squares where current movement may proceed in more than one wall direction.…”

Section: The Environment Agentmentioning

confidence: 99%

“…buildings [2], transport chains [3], malaria re-emergence in the south of France [6], and urban population growth [7], to give just a few examples. To the best knowledge of the authors there is no work on MABS frameworks to study rodent behaviour.…”

mentioning

confidence: 99%

Towards Large-Scale Multi-Agent Based Rodent Simulation: The “Mice In A Box” Scenario

Agiriga

Coenen

Hurst

et al. 2011

Research and Development in Intelligent Systems XXVIII

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Some initial research concerning the provision of a Multi-Agent Based Simulation (MABS) frameworks, to support rodent simulation, is presented. The issues discussed include the representation of: (i) the environment and the characters that interact with the environment, (ii) the nature of the "intelligence" that these characters might posses and (iii) the mechanisms whereby characters interact with environments and each other. Two categories of character are identified: "dumb characters" and "smart characters", the obvious distinction being that the first posses no intelligence while the second have at least some sort of reasoning capability. The focus of the discussion is the provision of a simple "mice in a box" scenario simulation.

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Multi Agent Based Simulation: Beyond Social Simulation

Cited by 140 publications

References 6 publications

Co-evolutionary Agent Self-Organization for City Traffic Congestion Modeling

Co-evolutionary Agent Self-Organization for City Traffic Congestion Modeling

Towards an Agent-based Simulation of Building Stock Development for the City of Hamburg

Towards Large-Scale Multi-Agent Based Rodent Simulation: The “Mice In A Box” Scenario

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