A TEAM-BASED ORGANIZATIONAL MODEL

FOR ADAPTIVE MULTI-AGENT SYSTEMS

Afsaneh Fatemi, Kamran Zamanifar, Naser Nemat bakhsh and Omid Askari

Department of Computer Engineering, University of Isfahan, Isfahan, Iran

Keywords: Team-based organization model, Adaptive multi-agent system, Capability-based task allocation.

Abstract: Proper organizational modelling is a challenging issue in complex cooperative multi-agent systems. In this

paper, we propose a team-based multi-agent organizational model, based on the Schwaninger's model of

intelligent human organizations. It provides an integrative framework to rapid task handling, the main

effectiveness requirement in many applications. Adaptation via reorganization makes the model suitable for

dynamic, uncertain environments. Fast initial team formation, greedy capability-based coalition formation,

and using the nearest neighbours’ resources improve utility compared to the identified hierarchical

organizational models.

1 INTRODUCTION

Our everyday lives and specially our social

transactions require various types of coordination

that incorporate decision making process within a

dynamic uncertain environment under multiple

constraints. Cooperation between members is an

important coordination task which aims to maximize

the overall utility. Multi-agent systems (MASs) have

been widely used to model and probe the complex

behaviors in such cooperative systems.

Using organization theory, behavior of

individual agents can be described by the roles they

adopt and behavior of MAS may be predicted as the

result of their overall actions.

Organizational models defined for MASs are

mainly adopted from analogue models in human

communities (Boella and Van Der Torre, 2006).

In real world, we may face emergency systems

which need fast task handling. This rapidity is the

main effectiveness requirement of the system.

Rescue in emergency situations is an example,

where cooperative humans tend to use all their

capabilities to rapidly perform the tasks. They may

even prefer to act out of their role-specific

responsibilities in occasional situations.

In this paper, we propose a team-based multi-

agent organizational model, based on the

Schwaninger's model of intelligent human

organizations (Schwaninger, 2009). It provides an

integrative framework to rapid task handling.

Following, section 2 discusses some related

work. Section 3 introduces the proposed

organizational model. Section 4 shows some

experimental results, and in section 5 we conclude.

2 THEORY AND RELATED

WORK

A multi-agent organization can model a MAS as a

group of distributed agents following a common

goal. The interactions between the agents, the

relationships between the agent roles, and their

coordination style make the organizational design.

Several organizational Structures are introduced

in literature (Deloach and Matson, 2004), (Horling

and Lesser, 2005), (Kolp, Giorgini, Mylopolos,

2006) for modeling MASs. Besides, a variety of

adaptation methods for different organizations have

been proposed yet (Ghijsen, Jansweijer, and

Wielinga, 2009), (Kirn and Gasser, 1998), (Kota,

Gibbins, Jennings, 2009), (Martin and Barak, 2006),

(Rosenfeld, Kaminka, Kraus, Shehory, 2008. All of

these methods attempt to enhance the system

effectiveness using adaptation.

(Ghijsen et al., 2009) and (Kota et al., 2009) are

among the latest works performed in this field. In

(Kota et al, 2009) a Decentralized structural

adaptation method is proposed, where agents need to

reevaluate all their relations in each time step. This

469

Fatemi A., Zamanifar K., Nemat bakhsh N. and Askari O..

A TEAM-BASED ORGANIZATIONAL MODEL FOR ADAPTIVE MULTI-AGENT SYSTEMS .

DOI: 10.5220/0003190604690472

In Proceedings of the 3rd International Conference on Agents and Artiﬁcial Intelligence (ICAART-2011), pages 469-472

ISBN: 978-989-8425-41-6

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

reevaluation decreases efficiency regarding

increasing computation. It doesn’t consider

environment openness.

In (Ghijsen et al., 2009), tasks are made of sub-

tasks which are distributed among agents in lower

levels of hierarchy to be performed by them.

However, in many applications tasks should be

performed by groups of agents. Besides,

organization efficiency should be improved.

3 ORGANIZATIONAL MODEL

Organization model of MAS defines the structure,

roles and interaction pattern of constituting agents,

and the goal(s) of the system (Mintzberg, 1993).

Schwaninger (2009) has presented a comprehensive

organization model for intelligent human

organizations, as follows: An intelligent

organization is capable of changing to adapt with

varying environment, mutual effect on the

environment, and viability in the environment of its

comprehending organizations. In this model design,

control and development are known as main

components in systemic management that should be

considered along with system identity structure. A

framework including five aspects of activity,

structure, behavior, ethos identity vision, and time

seems appropriate to model an organization.

Here, we define structural model (that shows

organization designing), activity model (that shows

the entire functionality of organization) and behavior

model (that shows cooperation process of

organization components) to introduce an

emergency-response cooperative MAS. The time

dimension of schwaninger’s model is inherently

purposed in all structure, behavior, and activity

models. The fifth dimension of the model includes

ethos, identity, and vision. It is the center of

paradigmatic change, which hardly affects on all

three domains: Structure, behavior and activity. It

will be paid more attention in our future works.

3.1 Task Model

As mentioned in (Carley and Gasser, 1999),

(Horling and Lesser, 2005) and (Dignum, Dignum,

and Sonenberg, 2004), a task is an activity that

should be performed by one or more agents to

achieve a goal or make a certain affect on the

environment.

We assume each task as a discrete event that may

occur with a given statistical distribution all around

the environment and in every point of time. Here we

suppose that the spatial and temporal distribution of

tasks’ occurrence is random.

A task is a tuple of two spatial attributes, which

describe the center of event occurrence, and a vector

of required capabilities and the minimal level of

each capability to be completed. The capabilities are

selected from a definite set in the system. We

assume that all these features are received by task

occurrence sensing agents.

Hence, if





cccC ,...,,



denotes all the n

capabilities available for agents, then











 





vcvcvcyxT ,,...,,,,,,

2211



describes the

task occurring in a point with dimensions x and y,

requiring capabilities

ccc ,...,,

with at least

vvv ,...,,

necessary levels of each. Here, these

values come from a range between 0 and 100.

3.2 Agent Model

In this paper, we supposed that the agents are

homogeneous in potential capabilities, but different

in the power to use each capability. This power is

related to available resources for the agent. Besides,

we assumed agents in two roles: Supervision and

Operation. Hence, if we have an agent set





aaaA ,...,,



in the context (p is the number of

agents), each agent



may contain a tuple of

attributes as follows:











 





vcvcvcyx ,,...,,,,,

2211

]100,0[,..1:





vnx

(1)

3.3 Structure Model

Organizational structure defines informational,

controlling, communicational patterns and features

of task environment (Kota et. al, 2009),

(Schwaninger, 2009). Our proposed organization is a

team-based organization whose initial structure

forms once the system begins to work and

reorganizes during the system operation, along with

occurrence of reorganization triggers. The

environment is a two dimensional grid space in

which a number of agents have been distributed

following a statistical distribution pattern. The

agents are distributed randomly or based on a given

map around the environment.

Figure 1 shows the proposed structure model. In

this model, the initial teams form based on the

location of each agent to minimize cost of initial

team formation. The context is partitioned to some

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

470

segments and all agents placed in each segment form

a team. The number of segments is varying as one of

the system parameters.

As mentioned in (Mintzberg, 1993), a supervisor

is required to manage each team. Here, the manager

of each team is the eldest agent among all team

members. Thus, it avoids any cost to system for this

task as well as the experience factor has been

implicitly regarded for selection of supervisor.



: Supervisor Agent

: Operator Agent

: Supervisors Communication

: Supervisor and Agent Communication

Figure 1: Organizational Structure.

3.4 Behaviour Model

Behavior model of proposed organization indicates

the way system transforms from one state to another

upon a given trigger. Occurrence of a new task

event, entrance of a new agent to the system, and

exit of the agent from the system form such triggers.

We use decentralized reorganization to coordinate

the MAS components.

It seems that coalition formation algorithm used

to select sub-teams and if needed, selection of

accommodator agents taking from adjacent teams,

are very significant in organization efficiency. In

this research, the simple greedy algorithm is used.

3.5 Activity Model

As Schwaninger (2009) defines, the activity model

describes the overall intended operations of or

actions taken by the organization. The emphasis of

change is on revising principles, goals and rules that

control and affect on the behavior of the

organization. Our proposed reorganization method

affects only on organizational structure.

We defined the utility as the rate of completed

tasks divided to the mean task accomplishment time.

Utility= TaskCompletionRate/MeanTaskCompletionTime

(2)

4 EXPERIMENTAL RESULTS

Our experiments consist of two parts. For first part,

we compare the impact of workload distribution in

team-based and hierarchical organizational models

using RoboCupRescue simulator.

In (Ghijsen et al., 2009), the performance of

organization is measured under two conditions. In

the first, civilians (tasks) are distributed randomly in

the environment to show a homogeneous task

distribution. In the second they are distributed as

clusters to form a heterogeneous workload. We run

some simulations on the Kobe map, creating 5

different homogeneous and 5 different

heterogeneous task distributions, as Ghijsen et al.

(2009) performed. Each distribution contains 9

agents (ambulances to rescue civilians) and 20 tasks

(civilians). Each simulation finishes after 300 time

steps. Figure 2 shows the results. Direct Supervision,

Standardization, and Adaptive hierarchy are three

coordination methods which are introduced,

implemented, and compared in (Ghijsen et al.,

2009).



Figure 2: Average number of tasks successfully performed

using four models (Performance).

As figure 2 shows, team-based method causes

better performance than hierarchical ones. It is

because of rapid initial team formation and proper

load distribution between agents as teams. In

homogeneous task distribution, this is done better

because the tasks are almost uniformly distributed

between agents. In heteregeneous distribution, the

tasks are distributed as clusters and the agents near

that clusters are mostly involved in task handling.

So, the team-based model doesn’t improve the

performance as in homogeneous one.

For second part, we compare the hierarchical

and team-based models against the rate of successful

task handling. For small numbers of agents, the

models are comparable and their effectiveness is in

A TEAM-BASED ORGANIZATIONAL MODEL FOR ADAPTIVE MULTI-AGENT SYSTEMS

471

the same range. But for agents more than 50, the

team-based model had much better results. The

results show smooth changes in utility function

when increasing the problem size. It shows that the

proposed team-based model is scalable enough to be

used in medium-scaled multi-agent environments.

Figure 3 shows the changes of utility function

with increasing problem size. It seems that fast team

formation, proper load distribution between agents,

and team-based task handling cause the system to

perform effectively.

Figure 3: Utility of team-based model in different problem

sizes.

5 CONCLUSIONS

AND FUTURE WORK

In this paper, the problem of decentralized

adaptation is addressed and a team-based

organizational model is proposed based on

schwaninger’s model of intelligent organizations.

The main reason for this selection was the

importance of changeability for organizations acting

in open, dynamic and uncertain environments. The

agents are coordinated through reorganization via

fast coalition formation, and a greedy task allocation

method is used.

Experiments show the better effectiveness of

team-based model against the hierarchical one.

Adaptation via reorganization, fast initial team

formation, greedy capability-based coalition

formation, and using the nearest neighbors’

resources, improve utility.

Future work will involve proposing new

coalition formation algorithms and testing the effect

of task and environment factors on system

efficiency. We are going to develop a more effective

simulation environment to be able to support the

open, dynamic, and uncertain environment’s

properties. Varying agent capabilities, different

types of tasks, variable number of segments,

changeable agents’ sights, and controllable output

information are some features to be added to

developed tool as soon.

REFERENCES

Boella, G. and Van Der Torre, L. (2006). Coordination

and Organization: Definitions, Examples and Future

Research Directions. Electronic Notes in Theoretical

Computer Science, 3-20.

Carley, K. and Gasser, L.(1999). Computational

organization theory. In Weiss, G.(Ed.), Multi-Agent

Systems, A Modern Approach to Distributed Artificial

Intelligence (pp. 200-330): MIT-press.

DeLoach, S., and Matson. E. (2004). An Organizational

Model for Designing Adaptive Multiagent Systems. In

Proceedings of the Eighteenth National Conference

on Artificial Intelligence (AAAI-04). San Jose, CA.:

AAAI Press.

Dignum, V., Dignum, F., and Sonenberg L. (2004).

Towards dynamic reorganization of agent societies. In

Proceedings of Workshop on Coordination in

Emergent Agent Societies (pp. 22-27).

Ghijsen, M.; Jansweijer, W. N. H., and Wielinga. B. J.

(2009). Adaptive Hierarchical Multi-Agent

Organizations. In Babuska, R. and Groen,

F.C.A.(Eds.) Interactive Collaborative Information

Systems. in press.

Horling, B. and Lesser, V. (2005). A Survey of Multi-

Agent Organizational Paradigms. The Knowledge

Engineering Review, 19(4), 281-316.

Kirn, S. and Gasser, L. (1998). Organizational Approaches

to Coordination in Multi-Agent Systems, Technical

report, IImenau Technical University, Germany.

Kolp, M., Giorgini, P., and Mylopolos, J. (2006). Multi-

Agent Architectures as Organizational Structures.

Autonomous Agents and Multi-Agent Systems, 13(1),

3-25.

Kota, R., Gibbins, N., and Jennings, N. (2009).

Decentralized Structured Adaptation in Agent

Organizations. In: Organized Adaptation in Multi-

Agent Systems. Lecture Notes in Computer Science,

5368, 54-71.

Martin, C. and Barber, K. S. (2006). Adaptive Decision

Making Frameworks for Dynamic Multi-Agent

Organizational Change. Autonomous Agents and

Multiagent Systems, 13, 391-428.

Mintzberg, H. (1993). Structures in Five: Designing

Effective Organizations. Engelwood Cliffs,

N.J.:Prentice Hall.

Rosenfeld, A., Kaminka, G., Kraus, S., and Shehory, O.

(2008). A Study of Mechanisms for Improving

Robotic Group Performance. Artificial Intelligence,

172, 633-655.

Schwaninger, M. (2009). Intelligent Organizations:

Powerful Models for Systemic Management (2nd ed.):

Springer.

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

472