Autonomous Agents in Multiagent Organizations
Marc Premm and Stefan Kirn
University of Hohenheim, Department of Information Systems 2, Stuttgart, Germany
Keywords: Multiagent Organizations, Agent Autonomy, Organizational Modelling.
Abstract: Autonomous agents are constantly gaining relevance in economic applications. Autonomy as a characteris-
tic of agents enables flexible behavior in cases of unforeseeable conditions. Multiagent systems research has
analyzed various dimensions of autonomous behavior. However, the application of agents in an organiza-
tional context requires the actors to apply externally given rules that restrict agent autonomy. While multia-
gent systems aim at maximum flexibility, economical applications in organizations require stable structures.
Multiagent organizations in terms of structured and stable multiagent systems are necessary to successfully
link autonomous agents with organizations. Modelling autonomous agents in multiagent organizations re-
quires to include the organizational structure and the operational processes, but also needs to consider the
constitutive processes that enable the creation, adaption and dissolution of multiagent organizations. We
survey extant literature from distributed artificial intelligence and management science and propose models
for organizational structure and procedure of multiagent organizations. The models address new aspects for
including autonomous agents in organizations that result from the linkage between both perspectives.
1 INTRODUCTION
Agent autonomy is a widely discussed field of re-
search that continuously gained relevance in the last
decade (Vernon, 2014). Especially the emergence of
autonomous cars has contributed to spread the term
“autonomous” both in scientific literature and in
everyday life (Gerla et al., 2014). However, the def-
inition of autonomy remains unclear and, thus, cre-
ates difficulties to integrate autonomous agents in
organizations. In the economically orientated con-
text of organizations, a trade-off between flexibility
and stability is necessary to ensure processes that are
mainly stable but may be adapted due to modified
circumstances (Brenner, 2003).
The problem of connecting autonomously work-
ing machines with organizational concepts has al-
ready been addressed by Grochla (1966). Already in
1966, he analyzed whether machines might get intel-
ligent enough that the complexity of their work may
be classified on a similar level as the work of hu-
mans. Since then, this thesis has been controversially
discussed in management science. Technical sys-
tems continuously gain a higher level of autonomy
and multiagent literature assumes agents to be au-
tonomous. Autonomy, which mainly results from
learning capabilities, enables agents to consider
unpredictable environmental effects. Built-in incor-
rect or incomplete knowledge might be compensated
by learning capabilities, which contributes to agents’
decisions that are mainly independent from the work
of the developer (Russel and Norvig, 2009).
Multiagent systems research assumes autono-
mous agents to cooperate when necessary and there-
fore participate in multiagent systems (MAS). Thus,
MAS are created to follow a single task and dimin-
ish as soon as the task is solved, creating maximum
flexibility. However, the integration of agents and
MAS in organizations requires reliable structures.
We use the term multiagent (MA)-organization to
describe a collaboration of autonomous agents that is
Figure 1: Autonomy and participation in multiagent
organizations.
Premm M. and Kirn S.
Autonomous Agents in Multiagent Organizations.
DOI: 10.5220/0006094901210128
In Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART 2017), pages 121-128
ISBN: 978-989-758-219-6
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
121
structured similar to organizations, creating stability
and means for economical applications.
Figure 1 illustrates the interconnections between
the initial influence of the developer, the learning
capabilities of the agent and its autonomous decision
on participation in MA-organizations. However, the
figure reveals an additional aspect: As autonomous
agents may participate in multiple MA-
organizations, goal conflicts between them may
occur and need to be resolved by the agent. Auton-
omy is also present in other domains and on another
level: Processes in hospitals are performed highly
autonomous by the involved departments. The cen-
tral hospital process management has only limited
influence on the process steps within the depart-
ments. The concept of fractal processes helps to
analyze and explain the interdependencies arising
with autonomous departments. We address this
problem by modelling the interdependencies be-
tween different organizational departments and frac-
tal business processes that involve autonomous
agents. This paper is based on previous work
(Premm and Kirn, 2015, Widmer et al., 2016) and its
aim is twofold: First, we survey literature from dis-
tributed artificial intelligence (DAI) and organiza-
tional theory to underline the importance of agent
autonomy in organizations. Second, we present
models for organizational structure and procedure to
analyze dependencies between them.
The remainder is organized as follows. In section
2, we discuss the state of the art. Section 3 presents a
modelling approach for MA-organizations. Section 4
discusses the linkage between organizational struc-
ture and procedure. Section 5 concludes.
2 STATE OF THE ART
This section surveys extant literature from DAI and
organizational literature, with a focus on agent au-
tonomy, basic problems of DAI and the fractal com-
pany approach introduced by Warnecke (1993).
2.1 Agent Autonomy
Autonomy has been attributed to software agents
widely in DAI literature. A definition, often quoted,
defines autonomous agents as agents that “have
control both over their internal state and over their
own behavior” (Jennings, 2000). However, this
definition lacks a specification, when an agents has
control over its internal state or its own behavior,
including its own reconfigurability (Dennis et al.,
2014): Software agents still have a developer who
has large influence on its behavior and, thus, also
over its internal state. Therefore, 4 levels of autono-
my can be distinguished in the context of DAI (Mül-
ler-Hengstenberg and Kirn, 2016): (i) Autonomy of
the developer: Besides the agents, the developer also
a certain kind of autonomy in developing the agent.
This includes choosing the software architecture and
programming language. (ii) Autonomy by design is
part of the product respectively agent definition and
insures that the agent is protected against external
influence. (iii) Technical autonomy addresses the
feasibility of intelligent and autonomous behavior
with respect to sensors and actuators as well as
available resources, e.g. energy, time, storage.
(iv) Autonomy of MAS describes the ability of MAS
to develop and maintain problem solving capabilities
partially independently from the participating soft-
ware agents. Here, we stick to the level of technical
autonomy, i.e. the technical feasibility of autono-
mous actions, and thus will first analyze the term
autonomy that is broadly used in literature.
In current technical development the term auton-
omous car is widely used to describe cars that are
capable of driving to a specified destination without
interaction of a human driver. But are these “auton-
omous” cars really autonomous? The goals of the
car are partly given by the developer, e.g. re-
strictions, rules, and partly given by human users,
e.g. destination, maybe even restrictions on the pos-
sible route. Thus, the car has no control over its own
goals as they are externally given and consequently
only partly over its internal state. So, we might ra-
ther term it a “fully automated” car as it is only au-
tomated in the sense of following directions. How-
ever, we might also think of an autonomous taxi that
may act on behalf of a goal function, e.g. earn mon-
ey, and may decide on its own with whom it is will-
ing to make a contract. This taxi would be classified
as “more autonomous” than a purely automated car.
Autonomy in general includes the ability to tem-
porarily give control away, e.g. an autonomous or
fully automated car may decide to join a group of
other cars to optimize travelling characteristics:
Safety distance may be reduced to decrease air re-
sistance and space occupied on the road. These par-
ticipation decisions are directly connected to organi-
zational structuring as each participant brings along
new competences and responsibilities for new tasks
(Widmer et al., 2016). Although, these organization-
al structures do not include humans, maximum flex-
ibility – as provided by MAS – would not be expe-
dient: Other cars of the group need to rely on fore-
seeable behavior of each participant. These MA-
organizations, however, inherently limit the autono-
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
122
my of the participating agents as organizational rules
limit their scope of action (Schillo and Fischer,
2004). Besides these self-controlled restrictions by
participation decisions, agents also face continuous-
ly restrictions by authorities: Not only do autono-
mous or automated cars need to follow traffic rules,
but these restrictions may be even increased in the
case of a police car or ambulance in action passing
their way. Consequently, autonomy is never a char-
acteristic that can be ultimately applied. Instead,
autonomy is in general restricted in some way and is
always a relation concept (Castelfranchi, 1995).
Agent autonomy is inevitably linked to the re-
sponsibility for actions. As an autonomous agent
needs the freedom to act autonomously, the question
arises whether the agent is able to be responsible for
its actions. However, we have to distinguish between
two types of responsibilities: (i) the implementation
responsibility for actions that lead to a successful
solution of the task and (ii) the juristic responsibility
that offers the possibility for contractual partners to
claim contractual performances (Widmer et al.,
2016). While autonomous agents may take the first
type of responsibility, they cannot be a contractual
partner and cannot be responsible in a legal sense.
MA-organizations primarily aim at structuring
the decisions of autonomous agents in a long-term
manner (Hübner et al., 2010). In contrast to MAS
that dissolve after the problem is solved, MA-
organizations have a long-term goal. Thus, the
members of a MA-organization have to restrict their
own freedom to act and give up some of their auton-
omy. On the other side the MA-organization needs
to address the still existing autonomy of the agents
by creating motivation to work towards the global
goal. However, the MA-organization is not able to
prevent agents from withdrawing from the organiza-
tion, in case their own goals run contrary to those of
the MA-organization. These problems are largely
analyzed in organizational theory (Pfeffer and Sa-
lancik, 1978), however, the involvement of autono-
mous software agents in organizations and the crea-
tion of MA-organizations is unattended in literature.
2.2 Basic Problems of DAI
Bond and Gasser define basic problems of DAI and
distinguish five basic questions that DAI needs to
address (Bond and Gasser, 1988). Agent autonomy
has major influence on these basic problem, but is
often neglected in literature. The influence of agent
autonomy on each of the five basic problems of DAI
is analyzed in the following paragraphs:
Problem Description and Solving. The first basic
problem of DAI is about “how to formulate, de-
scribe, decompose, and allocate problems and syn-
thesize results” (Bond and Gasser, 1988). However,
problem description is usually performed on a global
basis, neglecting the fact that agents act autono-
mously and might use their own models. Especially
in an organizational context, intelligent agents need
to align their own models, including problem de-
scriptions, with those of the MA-organization. On
the other side, due to single agents’ autonomy, Ma-
organizations have only restricted influence on in-
ternal models of their members.
Communication. In general, interaction and com-
munication between agents needs communication
languages or protocols. In a MA-organization,
communication languages and protocols are usually
defined by the MA-organization itself and may in-
tend strict rules. The purpose of strict rules are fore-
seeable behavior of the involved actors. However,
the autonomy of each agent may hinder communica-
tion by disregarding communication rules or the
sequence of a protocol, e.g. the initiation of an inter-
action. Analogously to other rules, motivation, e.g.
by incentives or sanctions, is necessary to ensure
agents following given rules.
Local Decisions. Autonomous agents are inevitably
associated with local decisions and the effects of
local decisions form the basis for decentralized con-
trol. In an MA-organization, decisions of each mem-
ber need to be aligned with the goals of the MA-
organization. However, the participating agents will
act on behalf of their own goals in the first place.
Thus, the regulations for their membership need to
include incentive mechanisms that ensure local deci-
sions in the MA-organization’s interest.
Coordination. Coordination in MAS is often char-
acterized and influenced by approaches from man-
agement science especially organizational theory.
Subsumed under the term coordination, this basic
question includes “how to enable individual agents
to represent and reason about the actions, plans,
and knowledge of other agents in order to coordi-
nate with them” (Bond and Gasser, 1988). This in-
cludes the information exchange of autonomous
agents, which is especially relevant in an organiza-
tional context and is directly connected to the local
decisions of each agent: Without the willingness to
share information the MA-organization might not
profit from additional knowledge generation, e.g. by
combining knowledge, or might suffer from incon-
sistencies in distributed knowledge bases.
Consistency. Consistency, e.g. of distributed
knowledge, in a group of agents depends on the
local decisions of single agents. Comparing MAS
Autonomous Agents in Multiagent Organizations
123
with agents in an organizational context, the prob-
lem of inconsistencies between the knowledge bases
of different agents even gains importance. As agents
in MA-organizations will work on a long-term basis,
inconsistencies might have severe impact on organi-
zational outcome. Influencing the coordination
mechanism and encouraging agents to resolve in-
consistencies in their knowledge base or conflicting
intensions is key to organizational performance.
The basic problems of DAI form a basis to dis-
cuss agent autonomy in an organizational context.
Addressing corresponding questions helps to under-
stand the impact of deploying agents in organiza-
tions and, hence, leads to an improvement of organi-
zational performance. While MAS evolve to solve a
problem and dissolve after a solution is found, MA-
organizations need to address the basic problems of
DAI from a long-term perspective.
2.3 Fractal Company
Enterprise modelling is constantly advancing by
including additional aspects of the real world. A
topic that has been addressed lately is the view on
enterprises as fractal companies (Witte, 2001; Sand-
kuhl and Kirikova, 2011). Ever-changing competi-
tion on globalized markets and the corresponding
complexity of decisions reveals new challenges for
the involved actors. Decentralization is one key for
enterprises to address these challenges and involves
decisions about organizational structures: Which
degree of autonomy is appropriate for which hierar-
chical level? Which resources and responsibilities do
the members need for decentralized decision (e.g.
Warnecke, 1993, Tapscott and Caston, 1993)? Or-
ganizations are forced to substitute strict hierarchical
structures with decentralized patterns of coordina-
tion. Conversely, organizational subunits that al-
ready show autonomous characteristics, increase
their degree of freedom and thus their level of au-
tonomy. To address the emerging challenges,
Warnecke (1993) introduces the term organizational
fractals that are characterized by the following four
major criteria: (i) Self-similarity are the structural
characteristics of organizations and modalities of
generating added value enabling resource sharing
between different organizational fractals. (ii) Self-
organization and self-optimization represents a de-
centralized approach addressing the strategic, the
tactical as well as the operational level of autono-
mous local decisions for solving tasks that have
previously unknown conditions. (iii) Goal-
orientation enables continuous measurement of each
organizational fractal’s performance, controlling
their autonomous behavior, e.g. by motivation. (iv)
Dynamic adaptation to unforeseeable changes of the
environment are enabled by autonomous behavior.
Organizational fractals in the sense of Warnecke
(1993) have a high degree of local autonomy, self-
control, and self-organization skills. The paradigm
has been transferred to multiagent literature under
the term holonic multiagent systems (Fischer et al.,
2003). Like autonomous agents in organizations,
organizational fractals aim to maximize their local
utility (e.g. in terms of workload or profit). Local
decisions as one basic problem of DAI is also rele-
vant in terms of organizational fractals: They auton-
omously decide whether to cooperate with other
organizational fractals. There are no means to force
organizational fractals to act in a specific way. Alt-
hough a direct influence is not possible, one way to
manage the behavior of organizational fractals or
groups of collaborating fractals is motivation. Ad-
dressing goal-orientation, the individual goal-
systems of organizational fractals needs to be
aligned to a globally consistent objective system by
incentives as well as sanctions (Warnecke, 1993). As
human members are usually only bounded rational,
establishing consistent goal hierarchies in organiza-
tions is nearly impossible (March and Simon, 1958).
Consequently, parallel existing goals within an or-
ganization are lacking consistency and, thus,
knowledge about internal objectives and dependen-
cies between them remains incomplete or even false.
In this context, consistency as one of the basic prob-
lem of DAI is directly affected by the autonomy of
the organizational members and their internal deci-
sion mechanisms. This inconstancies only address
organization internal goals. However, goal conflicts
also emerge on a higher level, as objectives between
the organization and its customers or other cooperat-
ing or competitive organizations may differ.
3 MODEL
Modelling MA-organizations has to involve soft-
ware agents as well as human agents and, thus,
needs to link the perspectives of information systems
with those of economics: First, constitutive process-
es are necessary to analyze the evolvement, adaption
and dissolution of MA-organizations. Second, the
organizational structure has to be mapped to the
model as organizations are usually divided into de-
partments with local decision competences, refer-
enced as organizational fractals. Third, organization-
al procedures involve multiple of these organiza-
tional fractals working together in multiple ways.
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
124
3.1 Constitutive Processes
The constitution of a MA-organization is necessary
to distinguish it from MAS that do not show long-
term stability. Therefore, the constitutional processes
help to scale up from sole non-binding interaction to
organizational structures with the corresponding
active processes (Cooren and Fairhurst, 2009).
Constitutive processes include the constitution,
adaption and dissolution of a MA-organization. The
processes that constitute organizations have already
been analyzed for their transferability to MA-
organizations: Kirn and Gasser (1998) provide a life-
cycle framework based on the Vienna Development
Method with three types of constitutive processes:
Constitution. The constitution of MA-organizations
requires agents willing to found and participate in a
new MA-organization. The constitution includes the
creation of organizational structures and processes
(see section 3.2 and 3.3). Hence, constituting a new
organization is dependent on the local decision of
several agents and is usually performed if all partici-
pants expect to have a positive return.
Adaption. While the initial constitution is designed
for a long-term period, changing environmental
circumstances and evolving goal definitions may
occur, but may also be initiated by a subset of the
organizational members. It includes the reorganiza-
tion of the organizational structure and procedures as
well as the admittance of new members or the exclu-
sion of existing ones.
Figure 2: Constitutive process of multiagent organizations.
Dissolution. If running the organization is not bene-
ficial for the remaining members anymore, dissolu-
tion processes are necessary. These processes have
to determine how the (non-)physical resources are
spread between the current set of members.
Constitutive processes aim at creating, maintain-
ing and if necessary dissolving the organizational
structure and procedure. Figure 2 shows the consti-
tutive processes for MA-organizations in an UML
activity diagram. The presented models are precon-
ditions for describing and applying the constitutive
processes creating and running a MA-organization.
3.2 Organizational Structure
As the agent’s autonomy forms the basis for its local
decisions, the organizational structure also influ-
ences the negotiations between the organization and
potential members. Term definitions for a common
language between both negotiators are necessary to
prevent inconsistency in alter stages. Therefore, we
adapt terms from literature of organizational theory.
The smallest autonomously acting organizational
unit is the position, which is in general constituted
independently of instantiations. The position and the
occupying agent are linked, while the unidirectional
cardinality is set to a maximum of 1. Thus, a posi-
tion can only be occupied by a single agent. Howev-
er, an agent may occupy multiple positions, possibly
in multiple organizations. A position is linked to one
or more tasks and thus to the corresponding compe-
tences and responsibilities: (i) A task is the target
performance linked to the position. Reaching the
target performance is one goal of the agent occupy-
ing the position. Handling tasks requires the agent in
charge to provide a set of capabilities and access to
resources. (ii) Competences denote the right to act in
a certain way in organizational literature (Hill et al.,
1994) and are the formal basis for position-specific
influence on the work of agents. (iii) The obligation
to act in a certain way (especially with respect to an
assigned task) is denoted responsibility. The respon-
sibility has an executive (performing an action) and
a legal dimension (bear the legal consequences).
Assigning a task to a position and consequently
to an agent implies expectations of other members
for task fulfillment. These expectations in MA-
organizations are called roles. Three types of roles
can be found in an organizational context (Hill et al.,
1994): (i) Task-specific roles specifying expectations
in relation to a task. (ii) Position-specific roles as
expectations related to the position that is occupied
by a member/agent. (iii) Individual-specific roles are
expectations that are founded in individual behavior
Autonomous Agents in Multiagent Organizations
125
or certain characteristics of a member, e.g. former
behavior of a member that is expected for future
actions. Figure 3 gives an overview on the main
UML classes for modelling hierarchical structures.
The link between a member of a MA-
organization and a position is the main representa-
tion of organizational membership. The position
itself is linked to several tasks, competences and the
corresponding responsibilities to use the competen-
cies to solve the tasks. The member provides a set of
capabilities and resources that are useful for the
MA-organization. “Usefulness” in the context of
organizational membership is represented by match-
ing capabilities used for solving specific tasks. Thus,
tasks are linked to capabilities and resources that the
agent provides and that are required for solving the
task. The class role is divided into three major sub-
classes representing expectations with respect to a
specific subject: (i) Individual-specific roles linked
to members, (ii) position-specific roles linked to
positions and (iii) task-specific roles linked to tasks.
Figure 3: UML class definition of organizational structure.
3.3 Organizational Procedure
Besides organizational fractals in the sense of auton-
omous departments, network-wide business process-
es consist of flexibly coordinated process fractals
autonomously making local decisions. These process
fractals may involve actors from multiple depart-
ments. Two dependent organizational problems
evolve in the context of process fractals: (i) the intra-
organizational structure of each process fractal and
(ii) the overall inter-fractal procedure that isn’t under
control of a single fractal. Process fractals, thus,
have to coordinate their actions for an appropriate
global output. Table 1 presents a meta-model for
modelling supply chains consisting of autonomous
process fractals. The logistics term transshipment
describes the actions connecting different logistics
process steps and, thus, allows the output of one
fractal to be used as an input for the next one.
While the participation of an agent in an organi-
zation is due to its autonomy dependent on its will-
ingness to participate, the presented meta-model
takes a look at higher level organizational proce-
dures. The process fractals within an organization
usually also have a certain degree of freedom in their
actions: In general, they cannot decide on the partic-
ipation at a certain process itself, but the fractals are
able to decide on their internal processes. The organ-
izational management has only limited influence on
the internal processes and thus has to motivate the
fractals to behave in a manner beneficial for the
organization. The meta-model visualizes this aspect,
which is especially important for agents in organiza-
tions, as they are usually able to handle considerably
more parallel tasks in different organizational pro-
cesses than their human co-workers.
Table 1: Meta-model of organizational procedure.
Label Symbol Description
Process
Fractal
Self-contained and self-organized
series of activities that involves
actors and is available via interfaces
Actor
Autonomous organizational entity
in a process fractal that has the
competency to make individual
decisions within a given scope
Interface
Coupling point of a process fractal
that allows for incoming or out-
going products/services from or to
another process fractal
Interac-
tion Path
Bidirectional communication link
between two actors of a fractal
Trans-
shipment
Transition of a product/service from
one process fractal to another one
4 LINKAGE OF STRUCTURE
AND PROCEDURE
The preceding sections presented two types of frac-
tals that occur in enterprises: (i) Organizational frac-
tals that may be embodied in departments or teams,
hence, hierarchical structures and (ii) process frac-
tals that have to solve specific logistic tasks as inter-
dependent steps of an overall enterprise output. Or-
ganizational structures and organizational proce-
dures are interconnected in various ways. In general,
there are numerous processes in enterprises that
involve multiple departments. Thus, one cannot
assume that the process fractals follow the same
pattern as the organizational structure. Consequent-
ly, organizational structure and procedure have to be
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
126
linked for a holistic view on the enterprise. Figure 4
shows the linkage of structure and procedure via an
identifier of the acting position. The example in-
volves a care robot that supports processes in a hos-
pital. Due to their ethical high responsibility for their
patients, physicians and their corresponding organi-
zational fractal are usually characterized by highly
autonomous decisions. Thus, hospital management
face the problem of efficient overall processes in-
volving multiple process fractals. As agents might
be included in more the one fractal, but lack legal
responsibilities and need mechanically readable
problem descriptions, the linkage between organiza-
tional structure and procedure needs to consider
these circumstances. However, the problems stated
in the context of hospitals may be also transferred to
other domains, e.g. autonomous production islands
in a manufacturing enterprise.
When autonomous agents are involved in organi-
zations, other factors have to be considered: Auton-
omous agents usually act in behalf some natural or
juristic person. Hence, somebody is responsible for
the agent’s action and has to stand in for contracts
that have been signed. In the example of autono-
mous cars, this leaves the question open who is re-
sponsible in case of an accident: The owner, the
manufacturer or the driver – who actually didn’t
drive. The same question arises in organizations:
Autonomous agents will usually work in some envi-
ronment, receiving orders from hierarchically super-
ordinate instances, e.g. a care robot in a hospital (see
Figure 4). However, the agent needs access to some
(hardware) resource and needs to be maintained.
These tasks are usually undertaken by the developer
or operator of the agent, e.g. the hospital internal IT
department or an external service provider. These
assignments have to be represented in the organiza-
tional structure, therefore autonomous agents in
organizations will be assigned to at least two organi-
zational departments: (i) IT department, developing
and maintaining the agent or manage service con-
tracts with the corresponding provider, and (ii) the
department, the agent is actually working for.
In the comparison with autonomous cars, the IT
department takes the role of an “owner”, when the
agents is developed by an external provider, as their
main task is to manage service contracts. The role
external service provider in this case is similar to a
manufacturer of autonomous cars. However, if an
autonomous agent is completely developed and
maintained within the organization, the IT depart-
ment would play a dual role as “owner” and “manu-
facturer”. In both cases, it is important to clarify who
is responsible for the agent’s activity, as the agent
itself isn’t able to take the juristic responsibilities.
Is the agent completely developed and main-
tained within the organization, internal organization-
al regulations may help to solve the problem of un-
clear responsibilities. However, if the agent has been
developed outside of the organization and autono-
mously decided to join an organization, the respon-
sibilities in a legal sense are unclear and strongly
depend on the involved countries. When country of
residence of the developer, the provider or that of
the application differ, it might be even unclear which
law is applicable.
Figure 4: Linkage-Example of organizational structure and
procedure.
5 CONCLUSIONS
In contrast to MAS that maximize flexibility, stable
structures for the interaction of autonomous agents
are necessary to create reliability in economical
applications We contribute to this problem by
providing models for three different perspectives on
MA-organizations: (i) the constitutive processes that
allow for creation, adaption and dissolution of or-
ganizations, (ii) the organizational structure that
represents a hierarchical order of capabilities and
resource requirements, and (iii) the organizational
procedures that represent fractal operational pro-
cesses. We discuss the problems arising with con-
necting organizational structure and procedure as
competences and responsibilities may remain un-
clear. The complexity increases significantly when
autonomous agents are assigned to multiple organi-
zational fractals and contribute to various fractals.
Autonomous Agents in Multiagent Organizations
127
Visualizing the emergent difficulties is a signifi-
cant step towards efficient integration of autono-
mous agents in organizations. However, further
research needs to develop new methods to address
these upcoming challenges. Working with the mod-
els presented enables their evaluation in an organiza-
tional context. Managing the integration of autono-
mous agents in economically orientated organiza-
tions is key to their application in general. Merely if
companies see the benefits of applying autonomous
agents in real world scenarios, their full potential
may be exploited. This particularly includes the
transition from solely flexible – and from an external
point of view unpredictable – behavior to stable
organizational processes.
ACKNOWLEDGEMENTS
This work has been supported by (1) the project
SmartSite (BMWi, FKZ 01MA13002) and (2) the
project InnOPlan (BMWi, FKZ 01MD15002), both
funded by the German Federal Ministry for Econom-
ic Affairs and Energy.
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