Using Semantic Technology to ModelPersona for Adaptable Agents

Johannes Nguyen, Thomas Farrenkopf,Michael Guckert

Kompetenzzentrum fur InformationstechnologieTechnische Hochschule Mittelhessen

61169, Friedberg, Germany{johannes.nguyen,thomas.farrenkopf,

michael.guckert}@mnd.thm.de

Simon T. Powers, Neil UrquhartSchool of Computing

Edinburgh Napier UniversityEH10 5DT, Edinburgh, United Kingdom{s.powers,n.urquhart}@napier.ac.uk

KEYWORDS

Adaptable Agents, Persona, Agent Modelling, Se-mantic Technologies

ABSTRACT

In state of the art research a growing interest inthe application of agent models for the simulation ofroad traffic can be observed. Software agents are par-ticularly suitable for the representation of travellersand their goal-oriented behaviour. Although numer-ous applications based on these types of models arealready available, the options for modelling and cali-bration of the agents as goal-oriented individuals areeither simplified to aggregated parameters or associ-ated with overly complex and opaque implementationdetails. This makes it difficult to reuse available simu-lation models. In this paper, we demonstrate how thecombination of persona models together with seman-tic methods can be applied to achieve a well-structuredagent model that allows for improved reusability.

INTRODUCTION

Computer-based simulation is an accepted means forresearching transportation questions, which has beenused as early as the 1970s [1], [2]. The number of exist-ing simulators is significant, with each of the tools fo-cusing on different aspects of the transport system anddiffering in the underlying methods. There is a vari-ety of simulators that range from more general purposeapplications (e.g. [3], [4], [5], [6], [7]) to systems de-signed for specific research questions (e.g. [8], [9], [10]).In practical research on transportation, researchers arefaced with the issue of finding appropriate simulators.[7] have described that even though general purpose ap-plications such as MATSim [5] and SUMO [4] offer a lotof potential for reusability and sharing of common traf-fic concepts (e.g. modelling of road network, vehicles,traffic flow), in many cases researchers have instead im-plemented their own simulation models from scratch.A reason for this may be that customisation options inavailable simulators are either too limited or too com-plex to be implemented. This is the case when cus-tomisation requires advanced programming or a deepunderstanding of the underlying system. A structured

design with a clear separation of concerns (see [11]) formodelling software agents using persona models andsemantic methods can help to improve reusability ofsimulation models and reduce complexity for customi-sation.

This paper is organised as follows: The following sec-tions provides a short introduction into the theoreti-cal background of persona models, which are usuallyapplied to areas in which focus lies on user-centricitysuch as Human–Computer interaction (HCI) or mar-keting. Furthermore, an overview of the semantic in-struments used in this work is given, namely ontologiesimplemented in OWL (Web Ontology Language) [12]and SWRL (Semantic Web Rule Language)[13]. Fol-lowing this, we discuss related work. We then presenta modelling method that allows for less complex cus-tomisation using the concepts of persona models andontologies. As proof of concept, we perform simula-tion of two example scenarios using the AGADE Traf-fic simulator [14]. The scenarios fundamentally differin types of mobility, which is often the case when spe-cific research questions at hand deviate from the mainfocus of available simulators. Thus, we demonstratehow customisation or extensions to the model can beimplemented with the proposed method. Finally, sum-mary and conclusions are given as well as indicationsfor future work.

PRELIMINARIES

The following section briefly introduces backgroundknowledge on the concept of persona models and se-mantic methods.

Persona Models

Persona models are an instrument for analysing andmodelling groups of individuals sharing similar be-haviour. They are often applied in the field of Hu-man–Computer interaction (HCI) and for marketingpurposes. In practical applications persona are usuallycreated with segmentation or clustering methods basedon collected customer or user data. [15] has discussedthe origins of persona models as an approach to goal-oriented software design. Reference is given to Cooper’sdefinition of persona models as “a precise description

Communications of the ECMS, Volume 35, Issue 1, Proceedings, ©ECMS Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela (Editors) ISBN: 978-3-937436-72-2 / 78-3-937436-73-9(CD) ISSN 2522-2414

of [a] user and what he wishes to accomplish” (see [16],p.123). A more detailed description is given by [17]who describe persona as “fictional, detailed archetyp-ical characters that represent distinct groupings of be-haviours, goals and motivations observed and identifiedduring the research phase”. It can be summarised thatpersona are fictional characters representing groups ofindividuals. They are identified by a unique name andcarry additional descriptive information of relevance forthe perspective that is to be modelled, e.g. appearance,private background, preferences, habits and goals in or-der to make a group of individuals more comprehensi-ble and manageable and to convey their personality andmotivations.

Ontologies and Rules

Ontologies are an expressive tool to model a do-main in machine readable form and provide an explicit,shared specification of a conceptualisation [18]. Ontolo-gies typically consist of a taxonomy of concepts eachwith properties and relations. OWL (Web OntologyLanguage) is a standardised implementation of a de-scription logic based ontology language [12]. As de-scription logic is object centered, formulation of simpleif-then rules is limited. These rules can be expressedusing Semantic Web Rule Language (SWRL). SWRLis also standardised by W3C. Inference engines deriveknowledge by evaluating OWL and SWRL expressions.

RELATED WORK

We have reviewed a wide range of available trafficsimulators in detail (inter alia [3], [5], [19], [8], [20], [6],[7], [21], [22], [23]). In particular, the AgentPolis ap-proach stands out as it also reflects on the shortcomingsof reusability in available simulation models. AgentPo-lis is a fully agent based traffic simulator that focuseson the simulation of interaction-rich transport scenar-ios [24], [7]. For example, simulation of on-demandmobility services (e.g ridesharing) requires interactionbetween service providers and customers but numerousother forms of interaction between travellers are possi-ble. Despite the fact that general purpose traffic simu-lators such as MATSim and SUMO provide a variety ofmodelling concepts (e.g. road network, vehicles, traf-fic flow), the authors of AgentPolis identified the gap oftransport scenarios with significant interaction betweentravellers and their immediate surrounding. The au-thors of AgentPolis concluded that similarities betweensimulation models have not been exploited sufficientlydue to existing tools not taking into account the multi-agent nature of interaction-rich transport systems. Ref-erence is given to work in which model-specific simula-tion tools have been developed from scratch (see [10],[25], [9]). AgentPolis addresses these deficiencies andprovides a set of abstractions, code libraries and soft-ware tools for building simulation models [7]. While fo-cus of the project was on the modelling of interaction-rich transport systems, a technical solution has alsobeen implemented to facilitate the reuse of commontransportation concepts. For this purpose, AgentPolis

integrates a modelling abstraction ontology. The the-oretical concept of this component is to separate de-fined modelling abstractions from implementations ofspecific modelling elements. It uses an ontology in or-der to define more general concepts of multi-agent sys-tems. This approach results in a tailored structure forobject-oriented programming that simplifies extendingthe simulation models for research-specific scenarios.In this paper, we revisit this idea of reusable modellingconcepts using ontologies in traffic simulations and fur-ther expand on the modelling capabilities of seman-tic methods. Furthermore, we will go one step furtherand place the individual and its decision-making at thecenter of attention in our modelling rather than solelydefining general modelling abstractions in the ontol-ogy for common transportation concepts such as trafficlights, etc.

MODELLING

The application of agent-based models for simulatingroad traffic is an established method. Traffic is an emer-gent phenomenon in which global system behaviour isdetermined by a large set of individuals, each with theirown goals and preferences. As [26] describe, softwareagents are closed computer systems that are situated insome environment, and that are capable of autonomousaction in this environment in order to meet their de-signed objectives. This autonomous and goal-orientedbehaviour also applies to travellers in the real worldwhich is why software agents are particularly suitablefor representing travellers in computer-based simula-tion models. The modelling of these individuals andtheir decision-making behaviour is often complex, andclosely depends to the research question at hand. As aresult, agent behaviour needs to be adjusted. For exam-ple, choice of transport mode in sightseeing scenariosdiffers from the choice in everyday commuting to workas travellers value time differently. It is precisely theseadjustments in agent modelling that transportation re-searchers have to implement in order to be able to prop-erly simulate their research scenarios in the first place.In this context, various researchers are repeatedly con-fronted with difficulties, as options for modelling andcalibration of the agents are either simplified to ag-gregated parameters, or are associated with complexprogramming that often requires a deep understandingof the underlying software architecture. The problemdoes not only relate to researchers with a background incomputing science, but also to those who would ratherdeal with traffic engineering issues exclusively. Con-sequently, it can be anticipated that these researcherswill be overburdened when customising existing mod-els, which is why new ontological concepts are neededto simplify this process. Otherwise, these researcherswill start to develop their own simulation models fromscratch as illustrated by [7].

As agent modelling essentially depends on the scenariobeing investigated, agents are usually modelled specif-ically for one particular scenario. For flexible reuse ofagents in different scenarios, we need methods that en-

able generalisation of agent behaviour. A similar prob-lem can be observed in general problem solving whichis a subcategory of artificial intelligence [27]. [28] anal-ysed implementations of domain-specific problem solv-ing, in order to identify abstraction patterns that candefine different methods of general problem solving.These patterns have served as the basis for numeroussubsequent research. Particularly, the CommonKADSproject is one of the outcomes [29]. The project cre-ated its own abstraction patterns for general problemsolving and also expanded on concepts of knowledge en-gineering. Based on this, [27] describe the expertise of asystem as the combination of knowledge about the con-texts of the observation subject at hand and the abilityto draw conclusions. An example is given of the knowl-edge acquisition process for building an domain-specificproblem solver that performs fault detection on bicy-cles: (1) First, a mechanic that specialises in bicyclesis interviewed about his working methods. (2) In ad-dition, the same mechanic is observed while at work inorder to also capture implicit knowledge that cannot beexpressed and described with words and that is neededfor such a diagnosis. (3) Furthermore, documents suchas repair manuals or measurement tables can be in-cluded. Collected knowledge can be merged into a uni-tary model of expertise. [27] point out that differenttypes of knowledge are involved. More particularly,knowledge on the assembly of bicycles, about the me-chanics, as well as knowledge about possible faults andtheir causes, and knowledge about the procedure forrecognising and repairing faults. The CommonKADSproject has defined a layered model for distinguishingthe different types of knowledge (see figure 1). The low-est layer describes Domain Knowledge. In this layer,domain-specific concepts and simple relations are de-fined. Considering the example of fault detection forbicycles, information on this layer may include whata bicycle is, which parts it consists of, which possiblefaults may occur, as well as possible causes of faults andcorrective measures. Inference Knowledge is located inthe layer above. This layer contains information aboutthe logical contexts of the concepts defined in the do-main knowledge. Based on this, conclusions can bedrawn using various methods and algorithms. Finally,at the top layer there is Task Knowledge in which infor-mation from the lower levels is brought together in or-der to perform decision-making and determine actions.

We now propose an architecture analogous to theCommonKADS knowledge structure as knowledge basefor adaptable agents in traffic simulations (see Fig-ure 2). This knowledge base is implemented by meansof OWL ontologies extended with SWRL rules. Wedistinguish between two types of domain knowledge:travel and activity information. Concepts of the firsttype of domain knowledge are relevant for traffic relatedaspects such as mode options. To facilitate reusabil-ity, they are encoded in a separate ontology which wecall the travel ontology. The travel ontology exclusivelycontains knowledge on common traffic concepts for ex-ample transportation modes, road signs, etc. The sec-

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ond type of domain knowledge can be referred to asactivity information and extends the knowledge of theagents by concepts that are relevant to model research-question-specific activity. For example, when simulat-ing a sightseeing scenario agents require completely dif-ferent activity information compared to a grocery shop-ping scenario. This method allows for a flexible exten-sion of agent knowledge. Agents are not bound to onetype of activity information, but may also integrate sev-eral activity ontologies for broader decision-making andsimulation of more complex scenarios. Regarding thelayer of inference knowledge, all ontologies containingtravel or activity related information are consolidated(imported) into a central person ontology. This on-tology contains information about person-specific con-cepts such as census properties. This enables the imple-mentation of decision-making in various domains usingonly one software agent. The idea matches the indi-vidual in the real world, that is constantly requiredto make multi-criteria decisions based on preferencesfrom various aspects in life. The defined concepts inthe domain knowledge can be used to formulate a setof logic based inference rules that enables the applica-tion of computer based reasoners. By employing theseestablished reasoning mechanisms, we use census infor-mation as input data to infer domain-specific prefer-ences that can be used as criteria for agent decisionmaking. For example, in a grocery shopping scenariotravellers have to make a decision as to which super-market they want to approach. This decision not onlydepends on traffic-related preferences (transport mode,shortest distance, etc.) but also on personal food pref-erences. This reflects different domains of knowledge.Travellers who particularly value organic and sustain-able products would possibly be willing to travel to aspecialist store for organic food even if the distance isa bit longer. For determining these preferences, rulescan be defined according to the following scheme:

Person(?p) ∧ hasCensusProperty(?p, ?cprop) ∧swrlb : equal(?cprop, specificProperty) ∧Preference(?pr) ∧ hasPreference(?p, ?pr)⇒ Person(?p) ∧ hasV alue(?pr, assigned value)

The rule states that if a person p has a specific censusproperty cprop, then it can be inferred that this per-son holds the value assigned value for a preference pr.An example may look as follows: If a person p has anage of 18-25 years (cprop), then it can be concludedthat the person p has a preference for organic food prof 5. Assuming that pr is for example measured on aLikert scale from 1 to 5 [30]. For reasons of compre-hensibility, a simple example rule has been formulated.In practical modelling, preferences should be inferredusing probability distributions as even within the agegroup of 18-25, there are various types of travellers withvarying preferences. Moreover, the same preference prcan be inferred from different census properties. Themultiple inference of values for the same preference prresults in probabilities for all attributes of the Likertscale that can be considered in final decision making.With our approach, researchers that are looking to cus-tomise the simulation model for research question spe-cific purposes no longer have to deal with complex pro-gramming, but instead can make use of the benefitsof semantic modelling. Using tangible persona models,settings for different agent types can be captured in acomprehensible form. Agents are assigned to personatypes and are mirrored as individuals into the ontol-ogy. This means that individual conclusions can bedrawn for the particular agent, and the inferred pref-erences can be incorporated into the decision-makingbehaviour of the agent.

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Finally for implementing task knowledge, the BDImodel as a well established paradigm for implement-ing intelligent agents is particularly suitable. It enablessoftware agents to perform action decisions (intentions)on the basis of defined goals (desires) and their mod-elled knowledge of their external world (beliefs) [31].The BDI model is well suited to model actors in traf-fic scenarios: not only destinations of journeys but alsooptimisation goals e.g. minimal travel time, minimalemissions can be formulated as desires. Travel prefer-ences and other parameters are potential beliefs thatcan be used to determine e.g. the selection of meansof transportation. In our own previous work, we havegiven proof that a separation of general agent activitylogic from aspects of modelling agent knowledge is anefficient and effective approach [32].

PROOF OF CONCEPT

To demonstrate the benefits of our proposed mod-elling method we have selected two example scenar-ios that fundamentally differ in types of mobility. Inpractical application this will be the case when spe-cific research questions at hand deviate from the mainfocus of available simulators. We use the proposedmodelling method and perform simulation as proof ofconcept. For both simulations we have exported geo-graphical map data from OpenStreetMap [33] for thearea around the city of Wetzlar which is located inHesse, Germany, in addition we use data provided bythe German census of 2011 [34]. Regarding the differ-ent types of travellers, for both scenarios, we created 12persona based on a classification provided in [35] (seefigure 3). The classification is based on various stagesin life (age/occupation status) as well as family statusand social strata/income (as illustrated in [36]) in or-der to represent the most significant groups of peoplein the German demographic. AGADE Traffic providesan option to create this type of persona using the webfrontend.

For the first simulation, a commuter scenario hasbeen modelled in which individuals start from variousresidential areas with all having the same target loca-tion. In real world scenarios, this is the case for exam-ple, when large gatherings take place or a large numberof persons is commuting to the same workplace. Wehave marked the event arena in Wetzlar as the venueand thus, the desired target location for all agents. Fur-thermore, markers for each of the residential areas inthe surrounding area of Wetzlar have been defined. Thedistribution of traveller agents starting from each res-idential area is based on data provided by the Ger-man census of 2011. A commuter scenario of this typeprimarily deals with knowledge about the traffic do-main. In this context, route choice problems are com-monly studied to determine current effects on the in-frastructure or immediate surroundings. For example,research on transportation usually attempts to relieveparticularly crowded road sections by improving traf-fic management, which is supposed to evenly distributetravel volume across alternative routes. For simulat-ing this type of route choice problems, AGADE Traf-fic provides a default simulation model. The defaultsimulation model generally assumes that all agents aretravelling by car and performs routing based on the A*algorithm (see [37]) that uses a cost functions based onshortest distance and additional geographical informa-tion. However in this context, the question of modechoice, e.g. travelling by car, bicycle, or walking, isjust as relevant. Therefore, we perform customisationto the supplied default simulation model, just as re-searchers would like to do with research specific prob-lems. The authors created an example ontology us-ing OWL for modelling domain knowledge on trafficconcepts. Using the ontology, agents obtain knowledgeabout different transportation modes available to them.For ease of exposition, in this paper we limit this ontol-

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ogy to the concepts of various travel mode options. Inparticular, information on cars, bicycles, walking andpublic transport has been modelled. For more com-plex scenarios that require an expanded knowledge ofthe traffic domain, such as the simulation of IntelligentTransportation Systems (ITS) or testing of traffic lightalgorithms, this ontology can be extended. Accordingto the modelling structure illustrated in figure 2, thisontology is equivalent to the travel ontology. Withinthis simulation, no activity ontology has been imple-mented as domain knowledge about traffic concepts issufficient. In addition, we have extended the person on-tology to define rules that reflect the decision-makingbehaviour regarding travel mode choice. The rules arecreated using semantic methods and do not requirecomplex programming capabilities. The person ontol-ogy describes the traveller agent as a person conceptwhich is itself described by various census properties.Furthermore, preferences are modelled in the personontology which can be included as criteria for decisionmaking. Using survey data, rules can be formulatedthat infer real values for the preferences of the agentsbased on the census properties defined in the respec-tive personas. For this simulation, we have used datafrom [38]. For the integration of the inferred knowl-edge into the layer of Task Knowledge, it is not possibleto completely avoid programming. Using our proposedmodelling structure we have reduced the amount of pro-gramming required to the minimum. AGADE Trafficis written in Java and implements BDI agents usingthe JADEX framework [39]. For customisation pur-poses, AGADE Traffic makes use of the advantages of

object oriented programming, and provides a central in-terface within the agent to implement decision-makingalgorithms or cost functions based on the inferred de-cision criteria from the ontology. For selection of travelmode, we have implemented a simple utility functionthat determines a personal utility score for each agentand mode based on utility values of the mode for vari-ous dimensions (monetary costs, eco-friendliness, etc.)and the inferred personal preference: Assuming I be-ing the set of modelled preferences in the ontology withi ∈ I, n being the number of preferences in I, Ui(m)being the utility value of a transportation mode m forpreference dimension i, and pi the inferred value of thepersonal preference of dimension i for an agent. Based

on this, we define UtilityScore(m) =

n∑i=0

Ui(m) ∗ pi.

Furthermore, we implemented mode selection based onMax(UtilityScore). This concludes the customisationperformed for the first simulation scenario.

We have created a second scenario in which we sim-ulate mobility related to grocery shopping. The char-acteristics of this scenario differ significantly from thefirst simulation. While all agents in the first simula-tion had a common target location, the grocery shop-ping scenario features different shopping locations thatagents can travel to. Agents are assigned a generatedlist of food items to purchase and are then requiredto make decisions about the selection of supermarketsas well as mode of travel. It should be noted that su-permarkets not only differ in product supply, but also

available stock may vary in product quality and sus-tainability. Consequently, in some cases agents will notbe able to purchase all items on the assigned grocerylist at a selected grocery store, which requires themto visit subsequent target locations. In comparison tothe first simulation, the decision-making process andthe number of decision criteria involved are much morediverse. Using our proposed modelling structure, wedemonstrate necessary customisation.With regard to the difference in agent decision-making,it can be noted that agents have to decide on two ma-jor aspects; firstly, the selection of target locations (su-permarkets) and secondly the selection of the travelmode. Decision criteria includes preferences not onlyregarding travel related aspects but also food relatedproperties. Therefore, domain knowledge has to beextended by a separate ontology that provides infor-mation on various types of food and grocery stores,as well as information on available product inventoryand further product related properties such as qual-ity, sustainability, price tendency, etc. Considering theproposed modelling structure illustrated in Figure 2,this food ontology matches an activity ontology that re-searchers have to append when customising the pro-vided default model for research specific scenarios. Forthis simulation, we thus make use of the same travelontology from the first scenario, but append a new foodontology to the domain knowledge. We then extendedthe person ontology by rules that conclude informationon food preferences. For this, we make use of pollingdata provided by [40]. With this, it is possible to in-fer all necessary preference information regarding bothtravel and food related aspects. Finally, we can usethe provided programming interface within the agentto implement algorithms regarding decision-making ofagents. The selection of supermarkets can for examplebe implemented in a similar manner using utility func-tions as demonstrated for travel mode selection. Giventhat the focus of this example is the description of thecustomisation process, at this point we will not fur-ther elaborate on the precise algorithm that we haveimplemented for this scenario. However, we will makesource code and simulation data available.1 The algo-rithms for the implementation of the decision behaviourcan be kept arbitrarily complex or simple dependingon the research question at hand. With our proposedmodelling structure, we create the basis for capturingall necessary decision preferences without complex pro-gramming and at the same time allow for flexible andadaptive scaling of the domain knowledge.

CONCLUSION AND FUTURE WORK

As customisation options in available traffic simula-tors are either simplified to aggregated parameters orassociated with complex programming, existing simula-tion models have not been reused to their full potential.As a result, researchers dealing with specific researchquestions have rarely made used of available simulators,but instead created their own simulation environment

1see https://github.com/kite-cloud/agade-traffic

from scratch. Based on the ideas of the CommonKADSproject and application of persona models and seman-tic methods, we have created a modelling structurethat facilitates easy reuse by reducing required pro-gramming to the necessary minimum. Moreover, ourmodelling structure allows for adaptable modelling ofagent knowledge as well as decision behaviour. For fu-ture work, modelling of both travel and activity re-lated knowledge can be expanded. The creation andcombination of further activity models for various do-mains may result in an open source library of activityknowledge that can be flexibly integrated, reused andcustomised for modelling complex research specific sim-ulations.

ACKNOWLEDGEMENT

This research has been supported by a grantfrom the Karl-Vossloh-Stiftung (Project NumberS0047/10053/2019).

REFERENCES

[1] M. Poeck and D. Zumkeller, “Die anwendung einer mass-nahmenempfindlichen prognosemethode am beispiel desgrossraums nurnberg,” in DVWG-Workshop Policy Sensi-tive Models, Giessen, 1976.

[2] K. Axhausen and R. Herz, “Simulating activity chains:German approach,” Journal of Transportation Engineering,vol. 115, pp. 316–325, may 1989.

[3] Texas Transportation Institute, “Early deployment of tran-sims: Issue paper,” 1999.

[4] D. Krajzewicz, G. Hertkorn, C. Rossel, and P. Wagner,“Sumo (simulation of urban mobility)-an open-source trafficsimulation,” in Proceedings of the 4th middle East Sympo-sium on Simulation and Modelling (MESM20002), pp. 183–187, 2002.

[5] A. Horni, K. Nagel, and K. Axhausen, The Multi-AgentTransport Simulation MATSim. Ubiquity Press, aug 2016.

[6] J. Auld, M. Hope, H. Ley, V. Sokolov, B. Xu, and K. Zhang,“POLARIS: Agent-based modeling framework developmentand implementation for integrated travel demand and net-work and operations simulations,” Transportation ResearchPart C: Emerging Technologies, vol. 64, pp. 101–116, mar2016.

[7] M. Jakob and Z. Moler, “Modular framework for simulationmodelling of interaction-rich transport systems,” in 16thInternational IEEE Conference on Intelligent Transporta-tion Systems (ITSC 2013), pp. 2152–2159, IEEE, IEEE, oct2013.

[8] M. Treiber and A. Kesting, “An open-source microscopictraffic simulator,” IEEE Intelligent Transportation SystemsMagazine, vol. 2, no. 3, pp. 6–13, 2010.

[9] S. Cheng and T. Nguyen, “TaxiSim: A multiagent sim-ulation platform for evaluating taxi fleet operations,” inAdvanced Agent Technology, vol. 2, pp. 359–360, SpringerBerlin Heidelberg, 2012.

[10] M. Horn, “Multi-modal and demand-responsive passengertransport systems: a modelling framework with embeddedcontrol systems,” Transportation Research Part A: Policyand Practice, vol. 36, pp. 167–188, feb 2002.

[11] B. De Win, F. Piessens, W. Joosen, and T. Verhanneman,“On the importance of the separation-of-concerns principlein secure software engineering,” in Workshop on the Appli-cation of Engineering Principles to System Security Design,pp. 1–10, 2002.

[12] D. McGuinness, F. Van Harmelen, et al., “Owl web ontologylanguage overview,” W3C recommendation, vol. 10, no. 10,p. 2004, 2004.

[13] I. Horrocks, P. Patel-Schneider, H. Boley, S. Tabet,B. Grosof, M. Dean, et al., “Swrl: A semantic web rulelanguage combining owl and ruleml,” W3C Member sub-mission, vol. 21, no. 79, pp. 1–31, 2004.

[14] J. Geyer, J. Nguyen, T. Farrenkopf, and M. Guckert,“AGADE traffic 2.0 - a knowledge-based approach for multi-agent traffic simulations,” in Advances in Practical Appli-

cations of Agents, Multi-Agent Systems, and Trustworthi-ness. The PAAMS Collection, pp. 417–420, Springer Inter-national Publishing, 2020.

[15] S. Blomkvist, “The user as a personality: A reflection onthe theoritical and practical use of personas in hci design,”Proceedings of the Technical report, pp. 1–13, 2006.

[16] A. Cooper, “The inmates are running the asylum. indi-anapolis, ia: Sams,” Macmillan, 1999.

[17] S. Calde, K. Goodwin, and R. Reimann, “SHS orcas,” inCase studies of the CHI2002|AIGA Experience Design FO-RUM on - CHI '02, pp. 2–16, ACM Press, 2002.

[18] N. Guarino, D. Oberle, and S. Staab, “What is an ontol-ogy?,” in Handbook on Ontologies, pp. 1–17, Springer BerlinHeidelberg, 2009.

[19] A. Bazzan, M. do Amarante, T. Sommer, and A. Benavides,“Itsumo: an agent-based simulator for its applications,” inProc. of the 4th Workshop on Artificial Transportation Sys-tems and Simulation. IEEE, p. 8, 2010.

[20] B. Torabi, M. Al-Zinati, and R. Wenkstern, “MATISSE3.0: A large-scale multi-agent simulation system for intelli-gent transportation systems,” in Advances in Practical Ap-plications of Agents, Multi-Agent Systems, and Complex-ity: The PAAMS Collection, pp. 357–360, Springer Inter-national Publishing, 2018.

[21] M. Adnan, F. Pereira, C. Azevedo, K. Basak, M. Lovric,S. Raveau, Y. Zhu, J. Ferreira, C. Zegras, and M. Ben-Akiva, “Simmobility: A multi-scale integrated agent-basedsimulation platform,” in 95th Annual Meeting of the Trans-portation Research Board Forthcoming in TransportationResearch Record, 2016.

[22] V. Chu, J. Gormer, and J. Muller, “Atsim: Combining aim-sum and jade for agent-based traffic simulation,” in Proceed-ings of the 14th Conference of the Spanish Association forArtificial Intelligence (CAEPIA), vol. 1, 2011.

[23] S. Thulasidasan, S. Kasiviswanathan, S. Eidenbenz,E. Galli, S. Mniszewski, and P. Romero, “Designing sys-tems for large-scale, discrete-event simulations: Experienceswith the fasttrans parallel microsimulator,” in 2009 In-ternational Conference on High Performance Computing(HiPC), pp. 428–437, IEEE, IEEE, dec 2009.

[24] M. Jakob, Z. Moler, A. Komenda, Z. Yin, A. Jiang,M. Johnson, M. Pechoucek, and M. Tambe, “Agentpolis:towards a platform for fully agent-based modeling of multi-modal transportation,” in Proceedings of the 11th Interna-tional Conference on Autonomous Agents and MultiagentSystems-Volume 3, pp. 1501–1502, International Founda-tion for Autonomous Agents and Multiagent Systems, 2012.

[25] L. Quadrifoglio, M. Dessouky, and F. Ordonez, “A sim-ulation study of demand responsive transit system de-sign,” Transportation Research Part A: Policy and Practice,vol. 42, pp. 718–737, may 2008.

[26] M. Wooldridge, “Agent-based software engineering,” IEEProceedings - Software Engineering, vol. 144, no. 1, p. 26,1997.

[27] M. Guckert and T. Peus, “Problemlosungsmethodenreloaded: Integration von domanenwissen zur anwendungallgemeiner losungsstrategien,” Integration und Konnexion,p. 70, 2013.

[28] W. Clancey, Classification problem solving. Stanford Uni-versity Stanford, CA, 1984.

[29] G. Schreiber, H. Akkermans, A. Anjewierden, N. Shad-bolt, R. de Hoog, W. Van de Velde, R. Nigel, B. Wielinga,et al., Knowledge engineering and management: the Com-monKADS methodology. MIT press, 2000.

[30] R. Likert, “A technique for the measurement of attitudes.,”Archives of psychology, 1932.

[31] M. Bratman, D. Israel, and M. Pollack, “Plans and resource-bounded practical reasoning,” Computational Intelligence,vol. 4, pp. 349–355, sep 1988.

[32] T. Farrenkopf, M. Guckert, N. Urquhart, and S. Wells, “On-tology based business simulations,” Journal of Artificial So-cieties and Social Simulation, vol. 19, no. 4, 2016.

[33] M. Haklay and P. Weber, “OpenStreetMap: User-generatedstreet maps,” IEEE Pervasive Computing, vol. 7, pp. 12–18,oct 2008.

[34] Statistische Amter des Bundes und der Lander, Zensus2011: Methoden und Verfahren. Wiesbaden, Hesse, Ger-many: Statistisches Bundesamt, 2015.

[35] GfK Consumer Panels, Consumers’ choice ’17 - neueMuster in der Ernahrung: die Verbindung von Genuss,Gesundheit und Gemeinschaft in einer beschleunigten Welt

: eine Publikation anlasslich der Anuga 2017. GfKConsumer Panels and Bundesvereinigung der DeutschenErnahrungsindustrie e.V., 2017.

[36] N. Pestel and E. Sommer, “Analyse der verteilung voneinkommen und vermogen in deutschland,” tech. rep., In-stitute of Labor Economics (IZA), 2016.

[37] P. Hart, N. Nilsson, and B. Raphael, “A formal basis forthe heuristic determination of minimum cost paths,” IEEETransactions on Systems Science and Cybernetics, vol. 4,no. 2, pp. 100–107, 1968.

[38] U. Engel and M. Potschke, “Mobilitat und verkehrsmit-telwahl 1999/2000.” GESIS Datenarchiv, Koln. ZA4203Datenfile Version 1.0.0, https://doi.org/10.4232/1.11591,2013.

[39] L. Braubach, W. Lamersdorf, and A. Pokahr, “Jadex: Im-plementing a bdi-infrastructure for jade agents,” 2003.

[40] Statista, “Lebensmittelkauf in deutschland,” 2020.

AUTHOR BIOGRAPHIES

JOHANNES NGUYEN is a researchassistant at Technische Hochschule Mittel-hessen in Friedberg from which he receivedhis master’s degree. He is also a PhDstudent at Edinburgh Napier University.His research interests include multi-agentsystems, mechanism design and semanticmodelling particularly applied to the fieldof smart cities and future mobility.

THOMAS FARRENKOPF is a lec-turer at the at Technische Hochschule Mit-telhessen. He completed a PhD degree inthe School Of Computing at EdinburghNapier University examining the use ofsoftware agents and ontologies for busi-ness simulation, applied to business games.His research areas include multi-agent sys-tems, semantic modelling and computerscience applied to industry projects.

MICHAEL GUCKERT is a Profes-sor of Applied Informatics at TechnischeHochschule Mittelhessen and a head of de-partment at Kompetenzzentrum fur Infor-mationstechnologie (KITE). He received aPhD in Computer Science from PhilippsUniversity Marburg. His research ar-eas are multi-agent systems, model drivensoftware development and applications ofAI.

Simon T. Powers is a lecturer at Ed-inburgh Napier University. He gained hisPhD from the University of Southampton,examining evolutionary explanations forcooperative social group formation. Hisresearch interests include computationalsocial, political, and economic science inwhich he investigates the links betweeninstitutions, computer science, and multi-agent systems.

NEIL URQUHART is a lecturer atEdinburgh Napier University where he isProgramme Leader for Computing Sci-ence. He gained his PhD from EdinburghNapier University in 2002, examining theuse of Software Agents and EvolutionaryAlgorithms to solve a real-world routingoptimisation problem. His research in-terests include Evolutionary Computationand Agent-based Systems and their appli-cation to real-world problems.