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Generating inspiration for multi-agent simulation design by Q-Learning
Örebro universitet, Akademin för naturvetenskap och teknik.
Örebro universitet, Akademin för naturvetenskap och teknik.ORCID-id: 0000-0002-1470-6288
2010 (engelsk)Inngår i: MALLOW-2010: proceedings of  the multi-agent logics, languages, and organisations federated workshops 2010, 2010Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

One major challenge in developing multiagent simulations is to find the appropriate agent design that is able to generate the intended overall phenomenon dynamics, but does not contain unnecessary details. In this paper we suggest to use agent learning for supporting the development of an agent model: the modeler defines the environmental model and the agent interfaces. Using rewards capturing the intended agent behavior, reinforcement learning techniques can be used for learning the rules that are optimally governing the agent behavior. However, for really being useful in a modeling and simulation context, a human modeler must be able to review and understand the outcome of the learning. We propose to use additional forms of learning as post-processing step for supporting the analysis of the learned model. We test our ideas using a simple evacuation simulation scenario.

sted, utgiver, år, opplag, sider
2010.
Emneord [en]
Multiagent Systems, Multiagent Simulation
HSV kategori
Forskningsprogram
Datalogi
Identifikatorer
URN: urn:nbn:se:oru:diva-14663OAI: oai:DiVA.org:oru-14663DiVA, id: diva2:398339
Konferanse
MAS&S at MALLOW 2010, Lyon, France, August 30 - September 2
Tilgjengelig fra: 2011-02-17 Laget: 2011-02-17 Sist oppdatert: 2018-01-12bibliografisk kontrollert
Inngår i avhandling
1. A Learning-driven Approach for Behavior Modeling in Agent-based Simulation
Åpne denne publikasjonen i ny fane eller vindu >>A Learning-driven Approach for Behavior Modeling in Agent-based Simulation
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Örebro: Örebro University, 2017. s. 58
Serie
Örebro Studies in Technology, ISSN 1650-8580 ; 75
Emneord
agent-based simulation, agent modeling, agent learning
HSV kategori
Identifikatorer
urn:nbn:se:oru:diva-61117 (URN)978-91-7529-208-3 (ISBN)
Disputas
2017-11-13, Örebro universitet, Teknikhuset, Hörsal T, Fakultetsgatan 1, Örebro, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2017-09-25 Laget: 2017-09-25 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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