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Programming agent behavior by learning in simulation models
Örebro University, School of Science and Technology.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-1470-6288
2012 (English)In: Applied Artificial Intelligence, ISSN 0883-9514, E-ISSN 1087-6545, Vol. 26, no 4, p. 349-375Article in journal (Refereed) Published
Abstract [en]

Designing the proper agent behavior for a multiagent system is a complex task. Often it is not obvious which of the agents' actions, and the interactions among them and with their environment, can produce the intended macro-phenomenon. We assume that the modeler can benefit from using agent-learning techniques. There are several issues with which learning can help modeling; for example, by using self-adaptive agents for calibration. In this contribution we are dealing with another example: the usage of learning for supporting system analysis and model design. A candidate-learning architecture is the combination of reinforcement learning and decision tree learning. The former generates a policy for agent behavior and the latter is used for abstraction and interpretation purposes. Here, we focus on the relation between policy-learning convergence and the quality of the abstracted model produced from that.

Place, publisher, year, edition, pages
Taylor & Francis, 2012. Vol. 26, no 4, p. 349-375
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Information technology
Identifiers
URN: urn:nbn:se:oru:diva-23067DOI: 10.1080/08839514.2012.652906ISI: 000303822500004Scopus ID: 2-s2.0-84861056993OAI: oai:DiVA.org:oru-23067DiVA, id: diva2:529656
Available from: 2012-05-31 Created: 2012-05-31 Last updated: 2018-02-02Bibliographically 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. p. 58
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 75
Keywords
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: 2018-01-13Bibliographically approved

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Junges, RobertKlügl, Franziska

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