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  • 1.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology. Örebro University, School of Law, Psychology and Social Work.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Exploiting Context and Semantics for UAV Path-finding in an Urban Setting2017In: Proceedings of the 1st International Workshop on Application of Semantic Web technologies in Robotics (AnSWeR 2017), Portoroz, Slovenia, May 29th, 2017 / [ed] Emanuele Bastianelli, Mathieu d'Aquin, Daniele Nardi, Technical University Aachen , 2017, p. 11-20Conference paper (Refereed)
    Abstract [en]

    In this paper we propose an ontology pattern that represents paths in a geo-representation model to be used in an aerial path planning processes. This pattern provides semantics related to constraints (i.e., ight forbidden zones) in a path planning problem in order to generate collision free paths. Our proposed approach has been applied on an ontology containing geo-regions extracted from satellite imagery data from a large urban city as an illustrative example.

  • 2.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 11, article id 2545Article in journal (Refereed)
    Abstract [en]

    This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

  • 3.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Knowing without telling: integrating sensing and mapping for creating an artificial companion2016In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA: Association for Computing Machinery (ACM), 2016, p. 11:1-11:4Conference paper (Refereed)
    Abstract [en]

    This paper depicts a sensor-based map navigation approach which targets users, who due to disabilities or lack of technical knowledge are currently not in the focus of map system developments for personalized information. What differentiates our approach from the state-of-art mostly integrating localized social media data, is that our vision is to integrate real time sensor generated data that indicates the situation of dfferent phenomena (such as the physiological functions of the body) related to the user. The challenge hereby is mainly related to knowledge representation and integration. The tentative impact of our vision for future navigation systems is re ected within a scenario.

  • 4.
    Bazzan, Ana L. C.
    et al.
    UFRGS, Porto Alegre, Brazil.
    de Oliveira, Denise
    UFRGS, Porto Alegre, Brazil.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Nagel, Kai
    TU Berlin.
    To adapt or not to adapt: consequences of adapting driver and traffic light agents2008In: Adaptive agents and multi-agent systems III: adaptation and multi-agent learning : 5th, 6th, and 7th European Symposium,ALAMAS 2005-2007on Adaptive and Learning Agents and Multi-Agent Systems : revised selected papers / [ed] Karl Tuyls, Ann Nowe, Zahia Guessoum, New York: Springer , 2008, p. 1-14Conference paper (Refereed)
    Abstract [en]

    One way to cope with the increasing traffic demand is to integrate standard solutions with more intelligent control measures. However, the result of possible interferences between intelligent control or information provision tools and other components of the overall traffic system is not easily predictable. This paper discusses the effects of integrating co-adaptive decision-making regarding route choices (by drivers) and control measures (by traffic lights). The motivation behind this is that optimization of traffic light control is starting to be integrated with navigation support for drivers. We use microscopic, agent-based modelling and simulation, in opposition to the classical network analysis, as this work focuses on the effect of local adaptation. In a scenario that exhibits features comparable to real-world networks, we evaluate different types of adaptation by drivers and by traffic lights, based on local perceptions. In order to compare the performance, we have also used a global level optimization method based on genetic algorithms.

  • 5.
    Bazzan, Ana L. C.
    et al.
    Instituto de Informática/PPGC, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre RS, Brazil.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    A review on agent-based technology for traffic and transportation2014In: Knowledge engineering review (Print), ISSN 0269-8889, E-ISSN 1469-8005, Vol. 29, no 3, p. 375-403Article, review/survey (Refereed)
    Abstract [en]

    In the last few years, the number of papers devoted to applications of agent-based technologies to traffic and transportation engineering has grown enormously. Thus, it seems to be the appropriate time to shed light over the achievements of the last decade, on the questions that have been successfully addressed, as well as on remaining challenging issues. In the present paper, we review the literature related to the areas of agent-based traffic modelling and simulation, and agent-based traffic control and management. Later we discuss and summarize the main achievements and the challenges.

  • 6.
    Bazzan, Ana L. C.
    et al.
    UFRGS, Porto Alegre, Brazil.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Re-routing agents in an abstract traffic scenario2008In: Advances in artificial intelligence: SBIA 2008 / [ed] Gerson Zaverucha, Augusto Loureiro da Costa, Berlin: Springer , 2008, p. 63-72Conference paper (Refereed)
    Abstract [en]

    Human drivers may perform replanning when facing traffic jams or when informed that there are expected delays on their planned routes. In this paper, we address the effects of drivers re-routing, an issue that has been ignored so far. We tackle re-routing scenarios, also considering traffic lights that are adaptive, in order to test whether such a form of co-adaptation may result in interferences or positive cumulative effects. An abstract route choice scenario is used which resembles many features of real world networks. Results of our experiments show that re-routing indeed pays off from a global perspective as the overall load of the network is balanced. Besides, re-routing is useful to compensate an eventual lack of adaptivity regarding traffic management.

  • 7.
    Bazzan, Ana L.
    et al.
    UFRGS, Porto Alegre, Brazil.
    Klügl, FranziskaÖrebro University, School of Science and Technology.
    Multi-agent systems for traffic and transportation engineering2009Collection (editor) (Other academic)
  • 8.
    Bazzan, Ana Lucia
    et al.
    Universidade Federal Rio Grande do Sul (UFRGS), Rio Grande do Sul, Brazil .
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Introduction to Intelligent Systems in Traffic and Transportation2013Book (Refereed)
    Abstract [en]

    Urban mobility is not only one of the pillars of modern economic systems, but also a key issue in the quest for equality of opportunity, once it can improve access to other services. Currently, however, there are a number of negative issues related to traffic, especially in mega-cities, such as economical issues (cost of opportunity caused by delays), environmental (externalities related to emissions of pollutants), and social (traffic accidents). Solutions to these issues are more and more closely tied to information and communication technology. Indeed, a search in the technical literature (using the keyword ``urban traffic" to filter out articles on data network traffic) retrieved the following number of articles (as of December 3, 2013): 9,443  (ACM Digital Library), 26,054 (Scopus), and 1,730,000 (Google Scholar). Moreover, articles listed in the ACM query relate to conferences as diverse as MobiCom, CHI, PADS, and AAMAS. This means that  there is a big and diverse community of computer scientists and computer engineers who tackle research that is connected to the development of intelligent traffic and transportation systems. It is also possible to see that this community is growing, and that research projects are getting more and more interdisciplinary. To foster the cooperation among the involved communities, this book aims at  giving a broad introduction into the basic but relevant concepts related to transportation systems, targeting researchers and practitioners from computer science and information technology. In addition, the second part of the book gives a panorama of some of the most exciting and newest technologies, originating in computer science and computer engineering, that are now being employed in projects related to car-to-car communication, interconnected vehicles, car navigation, platooning, crowd sensing and sensor networks, among others. This material will also be of interest to engineers and researchers from the traffic and transportation community.

  • 9.
    Carrascosa, Carlos
    et al.
    Universitat Politécnica de Valencia, Valencia, Spain.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Ricci, Alessandro
    Universita di Bologna, Cesena, Italy.
    Virtual Environments 4 MAS2014In: E4MAS - 10 Years Later. Workshop at AAMAS 2014 / [ed] D. Weyns et al., 2014Conference paper (Refereed)
    Abstract [en]

    The environment is a key point when talking about MASapplications, being a key concept when developing a platform or appli-cation in the past ten years: what is important in it and how to access it.At the same time, technology has evolved so that Virtual Environment-kinds of applications have grown out of science ction novels till researchpapers and even real applications. Current technology makes possible toMAS to interact also in this environments.In this paper, we have looked for the common ground that have all thedierent domains relating Virtual Environments as E4MAS, and we havecharacterized those domains according to three dimensions: connectionto the physical world of the environment, agents nature, and sociability.Moreover, we comment one of these domains, Mirror Worlds, as it is oneof the most complex domains commented, that we believe that is one ofthe topics to take into account in the near future both as a researh anddeveloping domain.

  • 10.
    Carrascosa, Carlos
    et al.
    Departamento de Sistemas Informáticos y Computación (DSIC), Universitat Politècnica de València, Valencia, Spain.
    Klügl, Franziska
    Örebro University, School of Law, Psychology and Social Work.
    Ricci, Alessandro
    Dipartimento di Informatica - Scienza e Ingegneria (DISI), Alma Mater Studiorum Università di Bologna, Cesena, Italy.
    Boissier, Olivier
    Mines and Laboratoire Hubert Curien CNRS:UMR 5516, Institut Henri Fayol - Ecole Normale Supérieure (FAYOL - ENS), Saint-Etienne, France.
    From Physical to Virtual: Widening the Perspective on Multi-Agent Environments2015In: Agent Environments for Multi-Agent Systems IV, Springer, 2015, 1, p. 133-146Conference paper (Refereed)
    Abstract [en]

    Since more than a decade, the environment is seen as a key element when analyzing, developing or deploying Multi-Agent Systems (MAS) applications. Especially, for the development of multi-agent platforms, it has become a key concept, similarly to many application in the area of location-based, distributed systems. An emerging, prominent application area for MAS is related to Virtual Environments. The underlying technology has evolved in a way, that these applications have grown out of science fiction novels till research papers and even real applications. Even more, current technologies enable MAS to be key components of such virtual environments.

    In this paper, we widen the concept of the environment of a MAS to encompass new and mixed physical, virtual, simulated, etc. forms of environments. We analyze currently most interesting application domains based on three dimensions: the way different "realities" are mixed via the environment, the underlying natures of agents, the possible forms and sophistication of interactions. In addition to this characterization, we discuss how this widened concept of possible environments influences the support it can give for developing applications in the respective domains.

  • 11.
    Davidsson, Paul
    et al.
    Malmö University, Malmö, Sweden.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Verhagen, Harko
    Stockholm University, Stockholm, Sweden.
    Simulation of Complex Systems2017In: Springer Handbook of Model-Based Science / [ed] Lorenzo Magnani and Tommaso Bertolotti, Cham: Springer, 2017, 1, p. 783-797Chapter in book (Refereed)
    Abstract [en]

    Understanding and managing complex systems has become one of the biggest challenges for research, policy and industry. Modeling and simulationof complex systems promises to enable us to understand how a human nervous systemand brain not just maintain the activities of a metabolism, but enable the production of intelligent behavior, how huge ecosystems adapt to changes, or what actually influences climatic changes. Also man-made systems are getting more complex and difficult, or even impossible, to grasp. Therefore we need methods and tools that can help us in, for example, estimating how different infrastructure investments will affect the transport system and understanding the behavior of large Internet-based systems in different situations. This type of system is becoming the focus of research and sustainable management as there are now techniques, tools and the computational resources available. This chapter discusses modeling and simulation of such complex systems. We will start by discussing what characterizes complex systems.

  • 12.
    Hatko, Reinhard
    et al.
    Unversity of Würzburg.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    A distributed simulation engine for a time-driven multi-agent simulation2008In: Summer computer simulation conference (SCSC), ADS track, San Diego: SCS Publishing House , 2008, p. 38-45Conference paper (Refereed)
  • 13.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Behavior abstraction robustness in agent modeling2012In: Web Intelligence and Intelligent Agent Technology (WIIAT), IEEE Computer Society Digital Library, 2012, p. 228-235Conference paper (Refereed)
    Abstract [en]

    Due to the "generative" nature of the macro phenomena, agent-based systems require experience from the modeler to determine the proper low-level agent behavior. Adaptive and learning agents can facilitate this task: Partial or preliminary learnt versions of the behavior can serve as inspiration for the human modeler. Using a simulation process we develop agents that explore sensors and actuators inside a given environment. The exploration is guided by the attribution of rewards to their actions, expressed in an objective function. These rewards are used to develop a situation-action mapping, later abstracted to a human-readable format. In this contribution we test the robustness of a decision-tree-representation of the agent's decision-making process with regards to changes in the objective function. The importance of this study lies on understanding how sensitive the definition of the objective function is to the final abstraction of the model, not merely to a performance evaluation.

  • 14.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Behavior modeling from learning agents: sensitivity to objective function details2012In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012): volume 3 / [ed] Conitzer,Winikoff, Padgham, and van der Hoek, Richland SC: The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2012, p. 1335-1336Conference paper (Refereed)
    Abstract [en]

    The process of finding the appropriate agent behavior is a cumbersome task – no matter whether it is for agent-based software or simulation models. Machine Learning can help by generating partial or preliminary versions of the agent low-level behavior. However, for actually being useful for the human modeler the results should be interpretable, which may require some post-processing step after the actual behavior learning. In this contribution we test the sensitivity of the resulting, interpretable behavior program with respect to parameters and components of the function that describes the intended behavior.

  • 15.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University.
    Evaluation of techniques for a learning-driven modeling methodology in multiagent simulation2010In: Multiagent system technologies / [ed] Jürgen Dix, Cees Witteveen, Berlin, Germany: Springer, 2010, p. 185-196Conference paper (Refereed)
    Abstract [en]

    There have been a number of suggestions for methodologies supporting the development of multiagent simulation models. In this contribution we are introducing a learning-driven methodology that exploits learning techniques for generating suggestions for agent behavior models based on a given environmental model. The output must be human-interpretable. We compare different candidates for learning techniques - classier systems, neural networks and reinforcement learning - concerning their appropriateness for such a modeling methodology.

  • 16.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Evolution for modeling: a genetic programming framework for SeSAm2011In: GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, ACM Digital Library, 2011, p. 551-558Conference paper (Refereed)
    Abstract [en]

    Developing a valid agent-based simulation model is not always straight forward, but involves a lot of prototyping, testing and analyzing until the right low-level behavior is fully specified and calibrated. Our aim is to replace the try and error search of a modeler by adaptive agents which learn a behavior that then can serve as a source of inspiration for the modeler. In this contribution, we suggest to use genetic programming as the learning mechanism. For this aim we developed a genetic programming framework integrated into the visual agent-based modeling and simulation tool SeSAm, providing similar easy-to-use functionality.

  • 17.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Generating inspiration for agent design by reinforcement learning2012In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 54, no 6, p. 639-649Article in journal (Refereed)
    Abstract [en]

    One major challenge in developing multiagent systems is to find the appropriate agent design that is able to generate the intended overall dynamics, but does not contain unnecessary features. In this article we suggest to use agent learning for supporting the development of an agent model during an analysis phase in agent-based software engineering. Hereby, the designer defines the environmental model and the agent interfaces. A reward function captures a description of the overall agent performance with respect to the intended outcome of the agent behavior. Based on this setup, reinforcement learning techniques can be used for learning rules that are optimally governing the agent behavior. However, for really being useful for analysis, the human developer must be able to review and fully understand the learnt behavior program. We propose to use additional learning mechanisms for a post-processing step supporting the usage of the learnt model.

  • 18.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Generating inspiration for multi-agent simulation design by Q-Learning2010In: MALLOW-2010: proceedings of  the multi-agent logics, languages, and organisations federated workshops 2010, 2010Conference paper (Refereed)
    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.

  • 19.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    How to design agent-based simulation models using agent learning2012In: Winter Simulation Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2012, p. 1-10Conference paper (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.

  • 20.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Learning Agent Models in SeSAm: (Demonstration)2013In: / [ed] Ito, Jonker, Gini and Shehory, The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2013Conference paper (Refereed)
    Abstract [en]

    Designing the agent model in a multiagent simulation is a challenging task due to the generative nature of such systems. In this contribution we present an extension to the multiagent simulation platform SeSAm, introducing a learning-based design strategy for building agent behavior models.

  • 21.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Learning convergence and agent behavior interpretation for designing agent-based simulations2010Conference paper (Refereed)
    Abstract [en]

    Designing a proper agent behavior for a multiagent simulation is a complex task as it is not obvious how the agents actions, and interactions among them and with their environment, result in an intended macro-phenomenon. To cope with the complexity involved in this challenge, and to achieve the intended overall result, the modeler may benefit from using agent learning techniques. In this contribution we focus on testing different configurations of the interface between the learning algorithm and the simulation scenario. The learned result is post-processed by a decision tree learner, to derive a comprehensible model for the agent behavior.

  • 22.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Learning Tools for Agent-based Modeling and Simulation2013In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 27, no 3, p. 273-280Article in journal (Refereed)
    Abstract [en]

    In this project report, we describe ongoing research on supporting the development of agent-based simulation models. The vision is that the agents themselves should learn their (individual) behavior model, instead of letting a human modeler test which of the many possible agent-level behaviors leads to the correct macro-level observations. To that aim, we integrate a suite of agent learning tools into SeSAm, a fully visual platform for agent-based simulation models. This integration is the focus of this contribution.

  • 23.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Modeling agent behavior through online evolutionary and reinforcement learning2011In: Federated Conference on Computer Science and Information Systems (FedCSIS), 2011, IEEE, 2011, p. 643-650Conference paper (Refereed)
    Abstract [en]

    The process of creation and validation of an agentbased simulation model requires the modeler to undergo a number of prototyping, testing, analyzing and re-designing rounds. The aim is to specify and calibrate the proper low level agent behavior that truly produces the intended macro level phenomena. We assume that this development can be supported by agent learning techniques, specially by generating inspiration about behaviors as starting points for the modeler. In this contribution we address this learning-driven modeling task and compare two methods that are producing decision trees: reinforcement learning with a post-processing step for generalization and Genetic Programming.

  • 24.
    Junges, Robert
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Programming agent behavior by learning in simulation models2012In: Applied Artificial Intelligence, ISSN 0883-9514, E-ISSN 1087-6545, Vol. 26, no 4, p. 349-375Article in journal (Refereed)
    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.

  • 25.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    A validation methodology for agent-based simulations2008In: SAC '08: Proceedings of the 2008 ACM symposium on applied computing (SAC) / [ed] Roger L. Wainwright, Hisham M. Haddad, New York, NY: ACM Press , 2008, p. 39-43Conference paper (Refereed)
    Abstract [en]

    Validity forms the basic prerequisite for every simulation model, therefore also for reasonable usage of the agent-based simulation paradigm. However, models based on the multi-agent system metaphor tend to need some particular approaches. In this paper, I propose a process for validating agent-based simulation models that combines face validation, sensitivity analysis, calibration and statistical validation.

  • 26.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Affordance-Based Interaction Design for Agent-Based Simulation Models2015In: Multi-Agent Systems (EUMAS 2014), Springer, 2015, Vol. 8953, p. 51-66Conference paper (Refereed)
    Abstract [en]

    When designing and implementing an Agent-Based Simulation model a major challenge is to formulate the interactions between agents and between agents and their environment. In this contribution we present an approach for capturing agent-environment interactions based on the “affordance” concept. Originated in ecological psychology, affordances represent relations between environmental objects and potential actions that an agent may perform with those objects and thus offer a higher abstraction level for dealing with potential interaction. Our approach has two elements: a methodology for using the affordance concept to identify interactions and secondly, a suggestion for integrating affordances into agents’ decision making. We illustrate our approach indicating an agent-based model of after-earthquake behavior.

  • 27.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Agent-Based Simulation EngineeringManuscript (preprint) (Other academic)
    Abstract [en]

    The history of agent-based models started in the 1970ies with singular yet path-breaking examples such as the Segregation model by T. Schelling [Schelling, 1971]. From end of the 80ies on more and more agent-based models were developed and implemented. However, almost no simulation engineering happened. Due to the relation to social sciences, mostly sociologists and psychologists used the paradigm of simulated humans based on rather complex models of human decision making to model hypotheses and theories about societal dynamics. The resulting models were complex but abstract. The role of empirical embeddedness is still discussed in the area of social simulation. Practioneers from more engineering-oriented domains like traffic simulation or researchers from domains with long simulation background like theoretical biology or engineering found the techniques associated with agent-based simulation interesting, yet not mature enoughto actually apply them.

    Agent-based simulation definitely is a highly valuable tool, especially when studying complex self-organizing systems in many domains. Thus, the question arises, what shows the maturity of a simulation paradigm and how the achievement of a high level of applicability can be brought forward? The answer is basically that engineering-like development and some form of good practice have to be established. In particular, this leads to the following issues that have to be addressed for fostering the development of agent-based models.

    • Deep understanding of the “object”, that means understanding of agent-based models themselvesand what particular feature is useful in what particular context.
    • Development of best practice: Establishing knowledge about how to build an agent-basedmodel efficiently and in a way that costs can be a priori estimated.

    Until now, none of these items is solved in a satisfying way. However, they are necessarily achieved at least partially for improving the broad applicability of agent-based modeling and simulation. Steps leading to the general aim of this book – fostering the applicability of agent-based simulation – can be derived from these considerations.

    A basic prerequisite and therefore first step is collecting specific knowledge about agent-based simulation and the context of its appropriate application. This refers to properties of simulation questions and modeling targets as well as to theoretical and empirical requirements for model design, implementation and usage.

    The second step concerns the development of an agent-based simulation. Although the general process model for developing simulation models, presented in every simulation textbook, can also be applied for agent-based simulation, the problem goes deeper than just using an appropriate specification or implementation language. Agent-based simulations are generative. It is not jus tdescribing what was observed, but finding agent behavior and interactions that produce a particular phenomenon. This idea has several consequences ranging from missing micro-macro links over non-linear models and tendencies to full detail to several levels of validation. Thus, developing methods for bridging the gap between macro-level objectives and appropriate micro-level programs in a systematic and reproducible way is the challenge for agent-based simulation engineering.

    A third step must consider practical application of the theoretical foundations. Basically,learning how to model for simulation possesses the same characteristics as learning how to program software. One might read about language constructs, but how its actually working is only experience-able by doing it. Therefore, a detailed presentation of simulation models and theirconstruction has to be part of a book about simulation engineering.

    Thus, this book sums up experiences in methodological research and application of agent-based simulation, especially in modeling complex and self-organizing systems. This book is a further step towards systematic engineering of agent-based models involving appropriate meta-models, procedures for development, conceptual and technical design and validation of models. It bridges the gap between established techniques related to modeling and simulation and the approaches and requirements for complex agent-based simulation modeling.

  • 28.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    "Engineering" agent-based simulation models?2013In: Agent-oriented software engineering XIII: revised selected papers / [ed] Jörg P. Müller, Massimo Cossentino, Springer Berlin/Heidelberg, 2013, p. 179-196Chapter in book (Refereed)
    Abstract [en]

    Multiagent simulation emerges to be one of the "killer applications" of multiagent system technology. For several reasons, there is a serious lack of engineering approaches in developing simulation models, so connecting AOSE with Multiagent Simulation seems to end in a win-win situation. A basic prerequisite is hereby to understand the current state and challenges of developing multiagent simulations. This is the objective of this contribution.

  • 29.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Measuring complexity of agent-based simulation: an attempt using metrics2008In: Languages, methodologies and development tools for multi-agent systems: Revised selected and invited papers / [ed] Mehdi Dastani, Amal El Fallah Seghrouchni, João Leite, Paolo Torroni, Berlin: Springer , 2008, p. 123-138Conference paper (Refereed)
  • 30.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Multi-Agenten-Systeme2013In: Handbuch der Künstlichen Intelligenz / [ed] Günther Görz, Josef Schneeberger and Ute Schmid, Oldenbourg Wissenschaftsverlag GmbH, 2013, 5., p. 999-1007Chapter in book (Refereed)
  • 31.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    SeSAm: visual programming and participatory simulation for agent-based models2009In: Multi-agent systems: simulation and applications / [ed] Adelinde Uhrmacher, Danny Weyns, Boca Raton, Fla: CRC Press, 2009, p. 477-508Chapter in book (Other academic)
  • 32.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Using the affordance concept for model design in agent-based simulation2016In: Annals of Mathematics and Artificial Intelligence, ISSN 1012-2443, E-ISSN 1573-7470, Vol. 78, no 1, p. 21-44Article in journal (Refereed)
    Abstract [en]

    When designing an Agent-Based Simulation Model a central challenge is to formulate the appropriate interactions between agents as well as between agents and their environment. In this contribution we present the idea of capturing agent-environment interactions based on the “affordance” concept. Originating in ecological psychology, affordances represent relations between environmental objects and potential actions that agents may perform using those objects. We assume that explicitly handling affordances based on semantic annotation of entities in simulated space may offer a higher abstraction level for dealing with potential interaction. Our approach has two elements: firstly a methodology for using the affordance concept to identify interactions and secondly a suggestion for integrating affordances into agents’ decision making. We illustrate our approach indicating an agent-based model of after-earthquake behavior.

  • 33.
    Klügl, Franziska
    et al.
    Örebro University.
    Bazzan, Ana L. C.
    Univ Fed Rio Grande do Sul, Porto Alegre RS, Brazil.
    Ossowski, Sascha
    Univ Rey Juan Carlos, Madrid, Spain.
    Agents in traffic and transportation Preface2010In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 18, no 1, p. 69-70Article in journal (Refereed)
  • 34.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Bazzan, Ana Lucia
    University of Rio Grande Do sul (UFRGS), Porto Alegre, Brazil; Multiagent Syst Lab, University of Massachusetts, Amherst MA, USA.
    Agent-based modeling and simulation2012In: The AI Magazine, ISSN 0738-4602, Vol. 33, no 3, p. 29-40Article in journal (Refereed)
    Abstract [en]

    This article gives an introduction to agent-based modeling and simulation (ABMS). After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom-up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application-oriented aspects, including selected tools and well-known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well-known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challenges is given.

  • 35.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Bernon, Carole
    Paul Sabatier University, Toulouse, France.
    Self-Adaptive Agents for Debugging Multi-Agent Simulations2011In: Proc. of the 3rd ADAPTIVE 2011: The Third International Conference on Adaptive and Self-Adaptive Systems and Applications / [ed] J. Fox and A. Rausch, Xpert Publishing Services, 2011, p. 79-84Conference paper (Refereed)
    Abstract [en]

    In this contribution, we propose an adaptation-drivenmethodology for the technical design and implementation of multi-agent simulations that is inspired by the concept of "living design". The simulated agents are capable of evaluatingtheir behavior and self-adapt for improving the overall model.For this aim, the modeler describes critical, non valid situations in the life of an agent, or the complete agent system, and explicitly specifies repair knowledge for these situations.

  • 36.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Davidsson, Paul
    Malmö University, Malmö, Sweden.
    AMASON: Abstract Meta-model for Agent-based SimulatiON2013In: Multiagent System Technologies / [ed] Matthias Klusch, Matthias Thimm and Marcin Paprzycki, Springer Berlin/Heidelberg, 2013, p. 101-114Conference paper (Refereed)
    Abstract [en]

    The basic prerequisite for methodological advance in Multi-Agent Based Modelling and Simulation is a clear, ideally formally-grounded, concept of our subject. A commonly accepted, implementation-independent meta-model may improve the status of MABS as a scientific field providing a solid foundation that can be used for describing, comparing, analysing, and understanding MABS models. In this contribution, we present an attempt formalizing a general view of MABS models by defining the AMASON meta-model that captures the basic structure and dynamics of a MABS model.

  • 37.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Hatko, Reinhard
    University of Würzburg.
    Butz, Martin V.
    University of Würzburg.
    Agent learning instead of behavior implementation for simulations: a case study using classifier systems2008In: Multiagent System Technologies / [ed] Ralph Bergmann, Gabriela Lindemann, Stefan Kirn, Michal Pechoucek, Berlin: Springer , 2008, p. 111-122Conference paper (Refereed)
    Abstract [en]

    Although multi-agent simulations are an intuitive way of conceptualizing systems that consist of autonomous actors, a major problem is the actual design of the agent behavior. In this contribution, we examine the potential of using agent-based learning for implementing the agent behavior. We enhanced SeSAm, a platform for agent-based simulation, by replacing the usual rule-based agent architecture by XCS, a well-known learning classifier system (LCS). The resulting model is tested using a simple evacuation scenario. The results show that on the one hand side plausible agent behavior could be learned. On the other hand side, though, the results are quite brittle concerning the frame of environmental feedback, perception and action modeling.

  • 38.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Towards Pattern-Oriented Design of Agent-Based Simulation Models2009In: MULTI-AGENT SYSTEM TECHNOLOGIES, PROCEEDINGS / [ed] Braubach, L; VanderHoek, W; Petta, P; Pokahr, A, Berlin, Germany: Springer, 2009, p. 41-53Conference paper (Refereed)
    Abstract [en]

    The formalization and use of experiences in good model design would make an important contribution to increasing the efficiency of modeling as well as to supporting the knowledge transfer from experienced modelers to modeling novices. We propose to address this problem by providing a set of model design patterns inspired by patterns in Software Engineering for capturing the reusable essence of a solution to specific partial modeling problem. This contribution provides a First step formulating the vision and indicating how patterns and which types of patterns can play a role in agent-based model design.

  • 39.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Klubertanz, Georg
    Rindsfüser, Guido
    Agent-based pedestrian simulation of train evacuation integrating environmental data2009In: KI 2009: advances in artificial intelligence / [ed] Bärbel Mertsching, Marcus Hund, Zaheer Aziz, Berlin: Springer, 2009, p. 631-638Conference paper (Other academic)
    Abstract [en]

    Simulating evacuation processes forms an established way of layout evaluation or testing routing strategies or sign location. A variety of simulation projects using different microscopic modeling and simulation paradigms has been performed. In this contribution, we are presenting a particular simulation project that evaluates different emergency system layout for a planned train tunnel. The particular interesting aspect of this project is the integration of realistic dynamic environmental data about temperature and smoke propagation and its effect on the agents equipped with high-level abilities.

  • 40.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Ossowski, SaschaUniversity Rey Juan Carlos, Madrid, Spain.
    Multiagent system technologies: 9th German Conference, MATES 2011, Berlin, Germany, October 6-7, 2011. Proceedings2011Conference proceedings (editor) (Refereed)
    Abstract [en]

    This book constitutes the proceedings of the 9th German Conference on Multiagent System Technologies held in Berlin, Germany, in October 2011. The 12 revised full papers presented together with 6 short parers were carefully reviewed and selected from 50 submissions. Providing an interdisciplinary forum for researchers, users, and developers to present and discuss latest advances in research work as well as prototyped or fielded systems of intelligent agents and multi-agent systems, the papers cover the whole range of this sector and promote its theory and applications.

  • 41.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Rindsfüser, Guido
    Emch&Berger AG, Bern, Switzerland.
    Agent-based route (and mode) choice simulation in real-world networks2011In: 2011 IEEE/WIC/ACM International Conference on  Web Intelligence and Intelligent Agent Technology (WI-IAT) / [ed] Jomi F. Hübner, Jean-Marc Petit, Einoshin Suzuki, IEEE, 2011, p. 22-29Conference paper (Refereed)
    Abstract [en]

    Mode and route choice are central elements of traffic simulations. Traditionally they form two subsequent steps in the four-step process where first, the simulated population distributes among available transportation modes and then their movement is assigned to the roads respectively other networks. However, these two phases are better dealt with simultaneously as choices are highly depending on each other. In this paper, we are suggesting an agent-based combined route and mode choice model that is not only able to resemble traditional simulations, but provides the means for new applications. As the simulated agents are active and situated while moving through the network, they are able to react to unforeseen events such as the closing of a link. Thus we can reproduce the self-organized re-distribution of travelers to new routes depending on when/where they are notified about the problem. We illustrate the feasibility and usefulness of our agent-based mode and route choice simulation using a real-world network of a small-size Swiss town.

  • 42.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Timpf, Sabine
    Institute for Geography, Augsburg University, Augsburg, Germany.
    Approaching Interactions in Agent-Based Modelling with an Affordance Perspective2017In: Autonomous Agents and Multiagent Systems: AAMAS 2017 Workshops, Best Papers, São Paulo, Brazil, May 8-12, 2017, Revised Selected Papers / [ed] Sukthankar. G.; Rodriguez-Aguilar J. A., Cham: Springer, 2017, p. 222-238Conference paper (Refereed)
    Abstract [en]

    Over the last years, the affordance concept has attracted more and more attention in agent-based simulation. Due to its grounding in cognitive science, we assume that it may help a modeller to capture possible interactions in the modelling phase as it can be used to clearly state under which circumstances an agent might execute a particular action with a particular environmental entity.

    In this discussion paper we clarify the concept of affordance and introduce a light-weight formalization of the notions in a way appropriate for agent-based simulation modelling. We debate its suitability for capturing interaction compared to other approaches.

  • 43.
    Klügl, Franziska
    et al.
    Örebro University, School of Science and Technology.
    Vizzari, GiuseppeUniversity of Milano-Bicocca, Milano, Italy.Vokrinek, JiriCzech Technical University, Prague, Czech Republic.
    Proceedings of the 8th International Workshop on Agents in Traffic and Transportation: Workshop at AAMAS 2014, Paris, May, 5/6th 20142014Conference proceedings (editor) (Refereed)
  • 44.
    Lopez-Paredes, Adolfo
    et al.
    INSISOC, Universidad de Valladolid, Escuela de Ingenierias Industriales Paseo del Cauce, Valladolid, Spain.
    Edmonds, Bruce
    Centre for Policy Modelling, Manchester Metropolitan University, Manchester, United Kingdom.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Special issue: Agent based simulation of complex social systems2012In: Simulation (San Diego, Calif.), ISSN 0037-5497, E-ISSN 1741-3133, Vol. 88, no 1, p. 4-6Article in journal (Refereed)
  • 45.
    Louloudi, Athanasia
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    A new framework for coupling agent-based simulation and immersive visualisation2012In: Proceedings of the 26th European Conference on Modelling and Simulation (ECMS) / [ed] Klaus G. Troitzsch, Michael Möhring, Ulf Lotzmann, ECMS European Council for Modelling and Simulation, 2012, p. 51-57Conference paper (Refereed)
    Abstract [en]

    In this contribution, we are dealing with the problem of on-demand interfacing agent-based simulations with Virtual Reality (VR) systems. We focus on how to handle and relate information between the two systems and investigate a generic way for mapping the information whichdescribes the state and context of a simulated agent toits 3D counterpart in the VR system. We provide details about the issues involved as well as propose generic representations and procedures that cope with the granularity discrepancies between the two systems. Finally, we present a first prototype and discuss aspects of its performance.

  • 46.
    Louloudi, Athanasia
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Immersive face validation: a new validation technique for agent-based simulation2012Conference paper (Refereed)
    Abstract [en]

    Agent-based simulation is very popular for its ability to approximate real life problems in an efficient way. To assure that the developed model is sufficienlty correct, validation needs to be performed. This contribution proposes a new approach to validate agent-based simulation models. To this end, a novel face validation technique is presented that enables systematic plausibility checks by a human expert immersed in a fine grain virtual reality environment that is the exact representation of the simulated multiagent model. It turns out that Immersive Face Validation, is a technically feasible process which offers great insight into the behaviour context of individual agents.

  • 47.
    Louloudi, Athanasia
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Towards a generic connection between agent behaviour and visualisation2011Conference paper (Refereed)
    Abstract [en]

    When interfacing standard agent-based simulations with Virtual Reality (VR) systems online during simulation, an important aspect to be addressed is what information needs to be tackled on both sides and how to relate this information. This is not a trivial task as it involves the association of abstract domain-specific agent properties in the simulation to low level information in the virtual world. During runtime, sufficient and appropriate information from the simulation has to be sent in a way that can be processed by the VR system. We want to identify a generic way for mapping the information which describes the state and context a simulated agent is in, to its detailed 3D representation inside a VR system. Hereby, the different time granularities of the two system form a core problem for maintaining consistency between the two platforms. In this contribution we discuss in detail the issues involved, propose generic representations as well as procedures to fill them. Finally we present a first prototype and its performance evaluation. 

  • 48.
    Louloudi, Athanasia
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Visualisation on demand for agent-based simulation2013In: Cognitive agents for virtual environments: First International Workshop, CAVE 2012, Held at AAMAS 2012, Valencia, Spain, June 4, 2012, Revised Selected Papers / [ed] Frank Dignum, Cyril Brom, Koen Hindriks, Martin Beer, Deborah Richards, Berling, Heidelberg: Springer, 2013, , p. 106-119Chapter in book (Refereed)
    Abstract [en]

    Agent-based simulation can be a useful tool when analysing complex systems such as real world scenarios. In multi-agent models when no quantitative data are available we have to search for new solutions in order to verify that the simulated model is appropriate with respect to the real one. Typical solutions approach the problem of missing data by considering contributions of human experts. In our work, we enhance the transparency of the simulation via a detailed visualisation in order to enable an immersive evaluation of the simulated model. We initially identify several conceptual and technical problems regarding the combination of a multi-agent simulation system with a game engine. We then propose a framework which implements a solution for generic processing and communication of information while maintaining consistency between the two systems. The end result automates several processes in a context independent manner and minimises the overall modeller’s effort.

  • 49.
    Louloudi, Athanasia
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Visualizing agent-based simulation dynamics in a CAVE: issues and architectures2011In: 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, 2011, p. 651-658Conference paper (Refereed)
    Abstract [en]

    Displaying an agent-based simulation on an immersivevirtual environment called CAVE (Cave Automatic Virtual Environment), a human expert is enabled to evaluate the simulation’s dynamics from the same point of view as in real life- from a within perspective instead of a birds eye view. As thisform of face validation is useful for many multiagent simulations,it should be possible to setup such a system with as little effortas possible. In this context, we systematically analyse the critical issues that a realization of such a system raises. Addressing these problems, we finally discuss design aspects of basic frameworkarchitectures.

  • 50.
    Marmaras, Charalampos
    et al.
    School of Engineering, Cardiff University, Cardiff, UK.
    Xydas, Erotokritos
    School of Engineering, Cardiff University, Cardiff, UK.
    Cipcigan, Liana M.
    School of Engineering, Cardiff University, Cardiff, UK.
    Rana, Omer
    School of Computer Science and Informatics, Cardiff University, Cardiff, UK .
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Electric Vehicles in road transport and electric power networks2016In: Autonomic Road Transport Support Systems / [ed] Thomas Leo McCluskey et al., Springer, 2016, p. 233-252Chapter in book (Refereed)
    Abstract [en]

    Electric Vehicle (EV) market penetration is expected to increase in the next few years. Transport electrification will affect both the road transport and the electric power network, as EV charging will be influenced by events that take place on the road network (such as congestion, weather, etc.) which subsequently have an impact on the potential load imposed on an electricity grid (based on where EV charging takes place). An EV is therefore seen as a link between transport and ener-gy systems, and their interdependencies are important. In this chapter an EV is mod-eled as an autonomous agent with a set of predefined high-level goals (such as trav-eling from origin to destination). Algorithms for the routing and charging proce-dures of EVs are presented. A multi-agent simulation is carried out, based on a number of scenarios, which demonstrates interactions between transport and energy systems, showing how an EV agent is able to adapt its behavior based on changes within each of these systems.

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