On Combining Domain Modeling and Organizational Modeling for

Developing Adaptive Cyber-Physical Systems

Jan Sudeikat and Michael K

ohler-Bußmeier

Department of Computer Science, Hamburg University of Applied Sciences,

Berliner Tor 7, 20099 Hamburg, Germany

Keywords:

Cyber-Physical System, Smart Grid Architecture Model, Organization-Centered Multi-Agent Systems.

Abstract:

Cyber-physical Systems (CPS) integrate physical and computational entities into coherent Systems of Sys-

tems. Since CPS are typically both socio-technical and embedded in a physical environment, these systems

require adaptive properties, e.g. in order to respond to environmental changes. The integration of Multi-Agent

Systems (MAS) in industrial and CPS is an active research topic. In this paper, we outline our work in progress

on utilizing, combining and supplementing established modeling approaches, i.e. domain speciﬁc (reference)

architecture models and organizational MAS models for the development of decentralized, adaptive CPS.

1 INTRODUCTION

Developing Cyber-physical Systems (CPS) requires

interdisciplinary collaboration, where physical com-

ponents and digital services are constructed, aligned

and integrated into coherent Systems of Systems

(SoS). Multi-paradigm modeling (Carreira et al.,

2020) and Model-Driven Development approaches,

e.g. (Uslar et al., 2019), have consequently been pro-

posed to enable these efforts. Since these systems

have to inﬂuence and respond to their physical envi-

ronment, agent-oriented modeling and development

approaches have been proposed as well (Leitao et al.,

2016; Challenger and Vangheluwe, 2020).

A trend towards decentralized coordination and

adaptation schemes can be observed. It is neces-

sary to develop a cyber-physical and human system

(CPHS) (Wasa et al., 2020), where human stake-

holders are integral parts. When regulatory, oper-

ational or possessory circumstances prohibit direct

control of system elements, incentive-based coordi-

nation schemes are often applied, e.g. market-based

coordination as Transactive Energy approaches in the

power grid (Abrishambaf et al., 2019; Huang et al.,

2021). Also decentralized coordination mechanisms

are investigated (Frey et al., 2015).

In this paper, we outline work in progress on com-

bining agent-based and CPS-oriented development

approaches, in order to facilitate the development of

these next generation CPS. We intend a framework

for documenting best practices and supporting princi-

pled development procedures. Domain models, e.g.

(CEN-CENELEC-ETSI, 2014), facilitate a use case

decomposition where each interoperability level is

gradually concretized from the abstract conception

to the implementation level. The proposed decom-

position is straightforward for hierarchical, control-

oriented structures, but it is challenging to devise de-

centralized interaction schemes, e.g. (Huang et al.,

2021), and coaction in CPS (see Section 2). Suc-

cessful designs require a coherent alignment of multi-

disciplinary system elements.

Here Multi-Agent Systems (MAS) come into play:

MAS provide a rich set of concepts and tools for

developing ensembes of autonomous possibly self-

interested software entitites which adapt to their en-

vironment. A major design effort for CPS develop-

ment is to bridge the micro-macro-link (K

ohler et al.,

2005; Sudeikat et al., 2012), i.e. enforce coherence of

the system by adjusting microscopic activities and in-

teractions on the basis of organization-centered multi-

agent systems (OCMAS). Augmenting CPS-oriented

designs with organizational structures facilitates de-

signing and analyzing applications. Thus, we identify

possible extensions and supplements for integrating

organization-centred approaches on MAS (Dignum,

2009) with current approaches for designing CPS.

This paper is structured as follows. In the next

section, we outline related work. In Section 3, the

integration of OCMAS with reference architectures is

discussed. We identify research challenges in Section

4, before we conclude Section 5.

330

Sudeikat, J. and Köhler-Bußmeier, M.

On Combining Domain Modeling and Organizational Modeling for Developing Adaptive Cyber-Physical Systems.

DOI: 10.5220/0010881200003116

In Proceedings of the 14th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2022) - Volume 1, pages 330-336

ISBN: 978-989-758-547-0; ISSN: 2184-433X

2 RELATED WORK

The construction of CPS requires interdisciplinary de-

velopment teams that combine modeling and engi-

neering approaches from different disciplines. Thus

multi-paradigm modeling (Carreira et al., 2020) is ap-

plied for the development of these systems. In this

context, the integration of agent-based modeling and

engineering techniques with model based develop-

ment approaches has been proposed in (Challenger

and Vangheluwe, 2020). While the development ap-

proach proposed here details the proposed integra-

tion, it is agnostic towards speciﬁc implementation

approaches.

Based on multi-paradigm modeling (Carreira

et al., 2020), the interdisciplinary nature of the de-

velopment of CPS leads to a mulit-paradigm devel-

opment efforts where different implementation ap-

proaches have to be integrated. A combining meta-

model has been proposed to facilitate integrating soft-

ware agents, actors and organizational models in a co-

herent application (Cossentino et al., 2019). Building

on these efforts, we aim at coherent modeling and de-

velopment procedures that facilitate coherent applica-

tion designs in the context of CPS.

The integration of Agent Oriented Software Engi-

neering (AOSE) in industrial applications poses sev-

eral conceptial challenges. For example, the appli-

cability of AOSE-Methodologies has been examined

(Cruz and Vogel-Heuser, 2017). There remains a

conceptual gap between the engineering of automa-

tion systems and their seamless integration in MAS-

based system architectures and development proce-

dures. We address this by combining established

modeling approaches, but seamless integration re-

mains a conceptual challenge.

We surveyed the utilization of SGAM in the devel-

opment of smart grid applications. Prominent exam-

ples comprise model-driven application development

(Uslar et al., 2019), the preparation of co-simulations

(Barbierato et al., 2020) as well as the analysis of se-

curity aspects (Henze et al., 2020). Also the descrip-

tion of self-organizing properties and non-functional

requirements (Lehnhoff et al., 2014) has been pro-

posed. The integration of software agents in these

designs found limited applications yet. Examples are

(Babar and Nguyen, 2018; Dethlefs et al., 2014). Spe-

ciﬁc extensions, e.g. (Szekeres and Snekkenes, 2020)

have been proposed to provide a more comprehen-

sive models of CPS. In the remainder of this work we

focus on the established SGAM model as is is most

widely excepted. We do not alter the model itself but

provide an optional extension which prepares agent-

oriented development practices.

3 INTEGRATING

SGAM AND OCMAS

Here, we advocate for integrating the SGAM-

approach with an organization-centred approach on

multi-agent systems (OCMAS) (Dignum, 2009) in

order to support the design of adaptive collec-

tives. Therefore, our models contain micro elements

(agents, goal directed behavior, learning etc.) as well

as macro elements (roles, positions etc.) (K

ohler-

Bußmeier and Wester-Ebbinghaus, 2013).

3.1 Architecture Models of

Cyber-Physical Domains

CPS development usually addresses systems in a

complex application domain with established domain

elements i.a. technical / organizational entities and

communication protocols. The inﬂuencial Smart Grid

Architecture Model (SGAM) provides a reference ar-

chitecture for the Smart Grid domain. This approach

has also been adopted for other domains, e.g. for mar-

itime logistics & Industry 4.0, where corresponding

architecture models have been developed (Gottschalk

et al., 2017). These models provide a conceptual

framework where the system context as is and the

system-to-be can be expressed.

Following the SGAM-approach (see Figure 1,

left), Domains are used to categorize the topology of

the physical space. E.g. for the Smart Grid, Domains

describe the power generation value chain (i. a. gen-

eration, transmission levels, consumption). Zones de-

scribe the automation levels for enabling remote con-

trolling of physical entities. These range from low-

level abstractions (Field- and Process-zones) up to

Enterprises and/or Markets. On top of this plane,

interoperability levels allow to describe controlling

software systems. These levels range from physical

elements populating the Component layer to the over-

all Business objectives. The accessing of low-level

elements is described in the Communication layer.

The Information layer describes the semantic infor-

mation that are exchanged with elements in the Func-

tion layer. On top the Business layer deﬁnes the en-

terprise and/or market interactions that inﬂuences and

motivates the manipulation of the physical entities

in the ﬁrst place. Applications of this modeling ap-

proach include, among others, the location of patterns

of industrial agents (Salazar et al., 2019).

A corresponding methodology guides the elabo-

ration of Use Cases for applications in this domain

(CEN-CENELEC-ETSI, 2014), where each interop-

erability level is gradually concretized from the ab-

stract conception to the implementation level.

On Combining Domain Modeling and Organizational Modeling for Developing Adaptive Cyber-Physical Systems

331

Figure 1: Left: Generalized view on Architecture Models (adapted from e.g. (CEN-CENELEC-ETSI, 2014)). An organization

contains elements on the Business, Function and Component Layers. Right: Besides hierarchical decomposition, we aim to

support decentralized organizational structures in CPS.

3.2 Organization-Centered Multi-Agent

Systems

From a conceptual viewpoint, MAS have a bottom-

up tendency; organization centered MAS (OCMAS)

complement this with a more top-down view in-

troducing concepts like positions, norms, roles etc.

Therefore, OCMAS are a good candidate to capture

central architectural aspects used to specify the over-

all behavior of a system at-large.

Concerning the design of adaptive CPS, we con-

sider three dimensions (see Figure 2): the administra-

tive dimension (i.e. the spectrum from systems ruled

by one central authority vs autonomous systems),

the architectural dimension (i.e. monolithic architec-

tures vs peer-to-peer systems), and the degree of self-

organization capabilities (i.e. ranging from feedback

loops over emergent self-forming to autopoetic self-

building systems). Of course, there are well estab-

lished means describing each dimension in isolation:

e.g. choreography of autonomous workﬂows, e.g.

cloud systems for distributed architectures, and e.g.

agent based simulation for self-organization. How-

ever, we see an increased demand for an integrated

approach, which has to take care of the interplay of

the three dimensions, to support the various tasks of

engineering CPS.

3.3 Combining Perspective: Layered

Desing of Adaptive CPS

An outline of the elements of a decentralized, adap-

tive cyber-physical system, based on the generic,

6-layered desing approach for adaptive CPS from

(Musil et al., 2017), is given in Figure 3. We adopted

this layered system structure particulalry to distin-

guish between and locate purely reactive as well as

deliberative control elements, e.g. (Cossentino et al.,

Analysis of Adaptive !

Agent Systems' Architectures

[Michael Köhler-Bußmeier]

administration

architecture

self-organization

centralised

monolithic

autonomous

peer to peer

emergence

micro-macro link

autopoiesys

feedback

hierarchic

We consider three dimensions: the administrative dimension (i.e. the spectrum from systems ruled by one central

authority vs autonomous systems), the architectural dimension (i.e. monolithic architectures vs peer-to-peer

systems), and the degree of self-organization capabilities (i.e. ranging from feedback loops over emergent self-

forming to autopoetic self-building systems). Of course, there are well established means describing each

dimension in isolation: e.g. choreography of autonomous workﬂows, e.g. cloud systems for distributed

architectures, and e.g. agent based simulation for self-organization. However, we see an increased demand for an

integrated approach, which has to take care of the interplay of the three dimensions, to support the various tasks of

engineering CPS.

client-server

Figure 2: Model Dimensions in OCMAS.

2019) and to indicate high-level coordination mecha-

nisms, e.g. markets (Wasa et al., 2020).

The utilization of physical components is to be

orchestrated (Physical Layer). These units provide

functions and services on a purely reactive fashion,

based on the local programming, i.e. using PLCs or

IECs. The adoption of recent M2M-protocols, e.g.

OPC UA (Schleipen, 2020), allows augmenting these

units with information and service models. Typically

these elements are controlled via computational ele-

ments on site (Proxy Layer). These elements are in-

terconnected (Communication Layer). Services are

building blocks for distributed applications (Applica-

tion Layer). The top-most layer describes the socio-

technical integration of applications e.g. in markets

or social networks. A fully ﬂedged CPS will re-

quire a comprehensive integration of third-party ser-

vices and socio-technical elements. For example, a

comprehensive mobility solution that provides a.o.

parking services and provides additional, supplemen-

tal means for the onward journey to users may re-

quire weather forecasts, billing services, and a plat-

form for communication with the human users. The

computational elements may range from traditional

ICAART 2022 - 14th International Conference on Agents and Artiﬁcial Intelligence

332

service providers to actors (Hewitt, 2015) and reac-

tive/deliberative agents. In fact it is a design effort to

decide for the appropriate implementation approach.

This framework can be directly mapped to the

interoperability layers of SGAM-based reference ar-

chitectures. The Proxy layer is implicitly present in

the SGAM-based Component layer. The Applicat-

tion and Service layers can be expressed in SGAM by

the interplay of Busniess and Function layers. While

the Socio-techninal aspects are typically extended as

a top-most zone (e.g. Markets), related extensions to

the SGAM model have been proposed as well (Szek-

eres and Snekkenes, 2020).

Figure 3: Conceptual Model of a decentralized adaptive

system, adapted and adjusted from (Musil et al., 2017).

3.4 Integration

Architecture models, like SGAM, provide valuable

information about the context of a CPS. The layers

of Figure 3 can naturally be modeled by SGAM-

compliant models (see Section 3.3). However these

layers possibly contain agent societies which pre-

scribe collective decision making, e.g. when a market

zone comprises localized markets (Wasa et al., 2020;

Mahesh et al., 2019) or system elements are arranged

in holarchies (Frey et al., 2015).

Organizational models can supplement the

SGAM-based description of a use case with an agent

organization which allows to realize the intended

system behavior. Abstracting from the behaviors of

individual agents, the interplay of agents is modeled

explicitly. When an application is extended reasoning

about the existing, possibly implicit, organization

and how to supplement it is an additional modeling

concern.

The use case methodology (Gottschalk et al.,

2017) prescribes an abstract, 4-step process, where

every layer in the SGAM-model is gradually reﬁned

till the implementation is prespeciﬁed. Correspond-

ingly, agent societies in the SGAM Layers can be

prescribed as well (see Figure 4). The resulting or-

ganizational model is a crosscutting concern, because

individual agents can be located within a single layer

(e.g. see (Salazar et al., 2019)). It will comprise de-

liberative and reactive participants on multiple layers

which collaborate for realizing system requirements.

Figure 4: Organization development for SGAM models.

It is to note that a system design is based on a

number of Use Cases. For each Use Case, the cor-

responding, appropriate organizational sub-model or

organizational supplement has to be identiﬁed. This

analysis has to consider the organizational structures

that are already present and the implications of the re-

lated Use Cases.

The described combination of models also ﬁt in

generic development methodologies for CPS. E.g. in

(Leitao et al., 2016), based on (Farid and Ribeiro,

2015), CPS development is approached by detailing

high-level design principles to MAS which are then to

be integrated in other hardware and software systems.

Architecture reference architectures and system spe-

ciﬁc architectures serve as intermediate design arti-

facts in in this development approach. High-level de-

sign principles, e.g. bionic, self-organizing, market-

based, can be expressed as organizational alternatives

in CPS development and thus supplement the reﬁne-

ment of CPS designs.

We particularly aim at describing decentralized

coordination approaches (Sudeikat et al., 2012). The

inclusion of organizational models in CPS-modeling

should allow for the following usages.

3.4.1 Descriptive Use

Documenting inter-agent patterns and best practices.

E.g. in (Salazar et al., 2019) typical types of agents

in industrial settings have been identiﬁed and docu-

mented and their interplay has been shown using an

SGAM-based reference architecture. Also in (Musil

et al., 2017) pattern for the construction of adaptive

CPS have been indentiﬁed. The need for expressing

the interplay of agents has led to a multi-layered de-

scriptions, e.g. see (Musil et al., 2017). While the

identiﬁcation and documentation of Use Cases are

speciﬁc to the individual system under development,

archtetype organizational models can serve as domain

independent patterns to be adjusted. In prior works,

On Combining Domain Modeling and Organizational Modeling for Developing Adaptive Cyber-Physical Systems

333

we have approached this for self-orgnaizing systems

(Sudeikat and Renz, 2010) and also markets designs

can be understood as patterns for inter-agent coordi-

nation (Xu et al., 2019).

3.4.2 Static Analysis

After an SGAM-based use case is augmented with an

organizational model of the resulting system, the re-

sulting architecture can be evaluated. While SGAM-

based use cases describe the operation of system el-

ements, a corresponding organizational model relates

these elements to each other and their physical coun-

terparts. A graph-based analysis of the interacting el-

ements, e.g. analogous to (Razo-Zapata, 2017; Menci

et al., 2020) allows to infer critical communication

links and allows reasoning for resilience and robust-

ness of the intended system. Based on speciﬁc per-

formance indicators the recommendations of speciﬁc

organizations can be derived.

3.4.3 Simulation-based Analysis

Simulation-based examinations of applications de-

signs are particularly facilitated by OCMAS-models.

Since these abstract from agent details, agent-based

models of the organization can be derived. During

iterative development, SGAM-based domain mod-

els allow to reason about the possible adjustments

of physical and computational elements that form

the context of the CPS. Use Cases and the suppele-

menting domain information prescribe the context of

the CPS. The component layer describes the phys-

ical elements, i.e. the devices (sensors / actuators)

as well as the computational resources which can be

used to host applications. This includes the proper-

ties of physical elements and deployment constraints.

This simulation-based analysis is exempliﬁed in (zum

Felde et al., 2021), where relevant domain artifacts are

identiﬁed and an adaptive, distributed application de-

sign is examined and iterated using agent-based mod-

eling. Based on a SGAM-based model of a drone-

based logistics system, an iterative analysis allows to

explore how architectural decisions affect the perfor-

mance of the application-to-be.

3.4.4 Self-adaptive Systems

Explicit models for MAS organizations can be made

subjects for adaptations. The organizations model can

serve as a system element itself, which can be explic-

itly adjusted in order to adapt the system. Thus a fu-

ture use is to enable self-adaptations by adjusting they

organizational structure at run-time. This may range

from creation of additional groups / teams at run-time

to switching between organizational modes.

4 CHALLENGES

In order to support principled development proce-

dures, e.g. extending current requirements analysis

procedures, we see the following research challenges:

• Integration of the SGAM-based modeling and the

corresponding use case methodology with follow-

up development procedures. After a set of use

cases have been derived, their implementations

will split up the development / extension of the

involved physical elements, service providers and

agents. For each of these system elements mod-

eling and development processes are available.

Thus the integration and alignment of these devel-

opments has to be structured. We propose orga-

nizational models as an integrative, cross-cutting

view for this purpose.

• Deriving objective criteria for deciding between

application designs (hierarchical, decentralized

or hybrid) and high-level design principles (e.g.

bionic, self-organizing, holonic) (Leitao et al.,

2016)). A mapping of architectural and organiza-

tion patterns to qualitative criteria facilitates their

selection in development projects. This requires

a coherent view on the system context, require-

ments, and additional constraints. A starting point

could be extended use case deﬁnitions for describ-

ing the intended macroscopic system dynamics

and constraints.

• A conceptual model for describing and integrating

decentralized application designs in CPS. Based

on models for agent-based software development,

organizational structures, actors, physical ele-

ments (see Section 3), decentralized coordination

processes (Sudeikat et al., 2012) can be described

and serve as patterns for application development.

• (Semi-) automated generation of simulations and

testbeds for decentralized CPS, since simulations

require integrating both physical elements (Car-

reira et al., 2020) and decentralized coordination

processes (Sudeikat et al., 2012). The selection

of different coordination processes requires sim-

ulation based analysises, possibly supported by

model-driven techniques (e.g. see (Uslar et al.,

2019)).

ICAART 2022 - 14th International Conference on Agents and Artiﬁcial Intelligence

334

5 CONCLUSIONS

In this position paper, we outline work in progress

on integrating and supplementing CPS-development

with organizational models of MAS. We argued that

organizational models, as used in MAS development,

facilitate the design of coherent ensembles of sys-

tems. These systems originate from multi-paradigm

modeling, thus a consistent view on their logical inter-

actions and integration is beneﬁcial. Besides the iden-

tiﬁcation of the beneﬁts of utilizing organizational

modeling in CPS development, the integration in cur-

rent development practices, the main usages and re-

sulting challenges are outlined.

While the outlined lightweight, simulation-based

analysis approach is used to teach the development

of adaptive CPS, the indicated supplements and chal-

lenges are prospects for future work and collabo-

ration. Future work comprises the development of

guidelines and tooling for description of collabora-

tion patterns in CPS, the static analysis of SGAM-

based use cases and the utilization of OCMAS-

models as ﬁrst class elements for adapting CPS at

run-time. Here we focus on SGAM-based models

which address Smart Grids. Since comparable models

have been derived for other domains as well, a trans-

ferability of our approach can be expected but remains

to be examined in future works.

ACKNOWLEDGEMENTS

J.S. would like to thank Wolfgang Renz (Hamburg

University of Applied Sciences) for inspiring discus-

sion.

REFERENCES

Abrishambaf, O., Lezama, F., Faria, P., and Vale, Z. (2019).

Towards transactive energy systems: An analysis on

current trends. Energy Strategy Reviews, 26:100418.

Babar, M. and Nguyen, P. H. (2018). Analyzing an agile

solution for intelligent distribution grid development:

A smart grid architecture method. IEEE.

Barbierato, L., Estebsari, A., Bottaccioli, L., Macii, E., and

Patti, E. (2020). A distributed multimodel cosimu-

lation platform to assess general purpose services in

smart grids. 56(5):5613–5624.

Carreira, P., Amaral, V., and Vangheluwe, H. (2020).

Multi-Paradigm Modelling for Cyber-Physical Sys-

tems: Foundations, pages 1–14. Springer.

CEN-CENELEC-ETSI (2014). Overview of sg-cg method-

ologies. Technical report, Smart Grid Coordination

Group. Version 3.0.

Challenger, M. and Vangheluwe, H. (2020). Towards

employing abm and mas integrated with mbse for

the lifecycle of scpsos. In Proceedings of the

23rd ACM/IEEE International Conference on Model

Driven Engineering Languages and Systems: Com-

panion Proceedings. ACM.

Cossentino, M., Lopes, S., Renda, G., Sabatucci, L., and

Zaffora, F. (2019). A metamodel of a multi-paradigm

approach to smart cyber-physical systems develop-

ment. In 20th Workshop ”From Objects to Agents”.

Cruz, S. L. A. and Vogel-Heuser, B. (2017). Comparison

of agent oriented software methodologies to apply in

cyber physical production systems. IEEE.

Dethlefs, T., Preisler, T., and Renz, W. (2014). Multi-

agent-based distributed optimization for demand-side-

management applications. IEEE.

Dignum, V., editor (2009). Handbook of Research on Multi-

Agent Systems: Semantics and Dynamics of Organiza-

tional Models. IGI Global.

Farid, A. M. and Ribeiro, L. (2015). An axiomatic design

of a multiagent reconﬁgurable mechatronic system ar-

chitecture. IEEE Transactions on Industrial Informat-

ics, 11(5):1142–1155.

Frey, S., Diaconescu, A., Menga, D., and Demeure, I.

(2015). A generic holonic control architecture for het-

erogeneous multiscale and multiobjective smart mi-

crogrids. 10(2):1–21.

Gottschalk, M., Uslar, M., and Delfs, C. (2017). The Use

Case and Smart Grid Architecture Model Approach.

Springer International Publishing.

Henze, M., Bader, L., Filter, J., Lamberts, O., Ofner, S.,

and van der Velde, D. (2020). Cybersecurity research

and training for power distribution grids – a blueprint.

In CCS ’20: Proceedings of the 2020 ACM SIGSAC

Conference on Computer and Communications Secu-

rity, page 2097–2099. ACM.

Hewitt, C. (2015). Actor model of computation: Scalable

robust information systems.

Huang, Q., Amin, W., Umer, K., Gooi, H. B., Eddy, F. Y. S.,

Afzal, M., Shahzadi, M., Khan, A. A., and Ahmad,

S. A. (2021). A review of transactive energy systems:

Concept and implementation. Energy Reports.

ohler, M., Moldt, D., R

olke, H., and Valk, R. (2005).

Linking micro and macro description of scalable so-

cial systems using reference nets. In Socionics: Socia-

bility of Complex Social Systems, volume 3413, pages

51–67.

ohler-Bußmeier, M. and Wester-Ebbinghaus, M. (2013).

Model-driven middleware support for team-oriented

process management. Transactions on Petri Nets and

Other Models of Concurrency, 8.

Lehnhoff, S., Rohjans, S., Holzer, R., Niedermeier, F., and

de Meer, H. (2014). Mapping of self-organization

properties and non-functional requirements in smart

grids. In Self-Organizing Systems. Springer.

Leitao, P., Karnouskos, S., Ribeiro, L., Lee, J., Strasser,

T., and Colombo, A. W. (2016). Smart agents in in-

dustrial cyber–physical systems. Proceedings of the

IEEE, 104(5):1086–1101.

On Combining Domain Modeling and Organizational Modeling for Developing Adaptive Cyber-Physical Systems

335

Mahesh, A. N., Shibu, N. B. S., and Balamurugan, S.

(2019). Conceptualizing blockchain based energy

market for self sustainable community. ACM.

Menci, S. P., Baut, J. L., Domingo, J. M., L

opez, G. L.,

ın, R. C., and Silva, M. P. (2020). A novel method-

ology for the scalability analysis of ICT systems for

smart grids based on SGAM: The InteGrid project ap-

proach. 13(15):3818.

Musil, A., Musil, J., Weyns, D., Bures, T., Muccini, H.,

and Sharaf, M. (2017). Patterns for Self-Adaptation

in Cyber-Physical Systems, pages 331–368. Springer

International Publishing, Cham.

Razo-Zapata, I. S. (2017). A method to assess the eco-

nomic feasibility of new commercial services in the

smart grid. IEEE.

Salazar, L. A. C., Ryashentseva, D., L

uder, A., and Vogel-

Heuser, B. (2019). Cyber-physical production sys-

tems architecture based on multi-agent’s design pat-

tern—comparison of selected approaches mapping

four agent patterns. The International Journal of

Advanced Manufacturing Technology, 105(9):4005–

4034.

Schleipen, M., editor (2020). Praxishandbuch OPC UA.

Vogel Communications Group.

Sudeikat, J., Stegh

ofer, J.-P., Seebach, H., Reif, W., Renz,

W., Preisler, T., and Salchow, P. (2012). On the com-

bination of top-down and bottom-up methodologies

for the design of coordination mechanisms in self-

organising systems. Information and Software Tech-

nology, 54(6):593 – 607.

Sudeikat, J. O. and Renz, W. (2010). Systemic modeling

of agent coaction: A catalog of decentralized coordi-

nating processes. Electronic Communications of the

EASST, Volume 27: Selbstorganisierende kontextsen-

sitive verteilte Systeme 2010.

Szekeres, A. and Snekkenes, E. (2020). Representing

decision-makers in SGAM-h: The smart grid archi-

tecture model extended with the human layer. pages

87–110. Springer International Publishing.

Uslar, M., Rohjans, S., Neureiter, C., Pr

ostl Andr

en, F.,

Velasquez, J., Steinbrink, C., Efthymiou, V., Migli-

avacca, G., Horsmanheimo, S., Brunner, H., and

Strasser, T. I. (2019). Applying the smart grid archi-

tecture model for designing and validating system-of-

systems in the power and energy domain: A european

perspective. Energies, 12(2).

Wasa, Y., Tanaka, K., Tsuji, T., Managi, S., and Uchida,

K. (2020). Economically enabled energy manage-

ment: Overview and research opportunities. In Eco-

nomically Enabled Energy Management, pages 1–32.

Springer Singapore.

Xu, Y., Ahokangas, P., Yrj

a, S., and Koivum

aki, T.

(2019). The ﬁfth archetype of electricity market: the

blockchain marketplace. 27(6):4247–4263.

zum Felde, N., K

ohler-Bußmeier, M., and Sudeikat, J.

(2021). Improving drone-based parcel delivery in ade-

livery system at its capacity limit. In Proc. of the Int.

Workshop on Petri Nets and Softw. Eng. (PNSE’21).

ICAART 2022 - 14th International Conference on Agents and Artiﬁcial Intelligence

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