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How to design agent-based simulation models using agent learning
Örebro University, School of Science and Technology. (MoS)
Örebro University, School of Science and Technology. (MoS)ORCID iD: 0000-0002-1470-6288
2012 (English)In: Winter Simulation Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2012, 1-10 p.Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

The question of what is the best way to develop an agent-based simulation model becomes more important as this paradigm is more and more used. Clearly, general model development processes can be used, but these do not solve the major problems of actually deciding about the agents' structure and behavior. In this contribution we introduce the MABLe methodology for analyzing and designing agent simulation models that relies on adaptive agents, where the agent helps the modeler by proposing a suitable behavior program. We test our methodology in a pedestrian evacuation scenario. Results demonstrate the agents can learn and report back to the modeler a behavior that is interestingly better than a hand-made model.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2012. 1-10 p.
Series
Winter Simulation Conference Proceedings, ISSN 0891-7736
National Category
Computer Systems
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-24164DOI: 10.1109/WSC.2012.6465017ISI: 000319225500037Scopus ID: 2-s2.0-84874704749ISBN: 978-1-4673-4779-2 (print)OAI: oai:DiVA.org:oru-24164DiVA: diva2:541879
Conference
Winter Simulation Conference (WSC 2012), Berlin, Germany, December 9-12, 2012
Available from: 2012-07-25 Created: 2012-07-25 Last updated: 2017-10-27Bibliographically approved
In thesis
1. A Learning-driven Approach for Behavior Modeling in Agent-based Simulation
Open this publication in new window or tab >>A Learning-driven Approach for Behavior Modeling in Agent-based Simulation
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Agent-based simulation is a prominent application of the agent-based system metaphor. One of the main characteristics of this simulation paradigm is the generative nature of the outcome: the macro-level system behavior is generated from the micro-level agent behavior. Designing this agent behavior becomes challenging, as it is not clear how much each individual agent will contribute to the macro-level phenomenon in the simulation.

Agent learning has proven to be successful for behavior configuration and calibration in many domains. It can also be used to mitigate the design challenge here. Agents learn their behaviors, adapted towards their micro and some macro level goals in the simulation. However, machine learning techniques that in principle could be used in this context usually constitute black-boxes, to which the modeler has no access to understand what was learned.

This thesis proposes an engineering method for developing agent behavior using agent learning. The focus of learning hereby is not on improving performance, but in supporting a modeling endeavor: the results must be readable and explainable to and by the modeler. Instead of pre-equipping the agents with a behavior program, a model of the behavior is learned from scratch within a given environmental model.

The following are the contributions of the research conducted: a) a study of the general applicability of machine learning as means to support agent behavior modeling: different techniques for learning and abstracting the behavior learned were reviewed; b) the formulation of a novel engineering method encapsulating the general approach for learning behavior models: MABLe (Modeling Agent Behavior by Learning); c) the construction of a general framework for applying the devised method inside an easy-accessible agent-based simulation tool; d) evaluating the proposed method and framework.

This thesis contributes to advancing the state-of-the-art in agent-based simulation engineering: the individual agent behavior design is supported by a novel engineering method, which may be more adapted to the general way modelers proceed than others inspired by software engineering.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2017. 58 p.
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 75
Keyword
agent-based simulation, agent modeling, agent learning
National Category
Information Systems
Identifiers
urn:nbn:se:oru:diva-61117 (URN)978-91-7529-208-3 (ISBN)
Public defence
2017-11-13, Örebro universitet, Teknikhuset, Hörsal T, Fakultetsgatan 1, Örebro, 09:00 (English)
Opponent
Supervisors
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2017-10-31Bibliographically approved

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WCS2012 Design by Learning(180 kB)221 downloads
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Junges, RobertKlügl, Franziska

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