Brazier, F.M.

Eck, P.A.T. van

Treur, J.

van Eck, P.A.T.

VU Research Portal

VU Research PortalModelling Competitive Co-operation of Agents in a Compositional Multi-AgentFrameworkBrazier, F.M.; van Eck, P.A.T.; Treur, J.published inKnowledge Acquisition, Modelling and Management, Proc. of the 10th European Knowledge AcquisitionWorkshop, EKAW'97,1997document versionPublisher's PDF, also known as Version of recordLink to publication in VU Research Portalcitation for published version (APA)Brazier, F. M., van Eck, P. A. T., & Treur, J. (1997). Modelling Competitive Co-operation of Agents in aCompositional Multi-Agent Framework. In E. Plaza, & R. Benjamins (Eds.), Knowledge Acquisition, Modellingand Management, Proc. of the 10th European Knowledge Acquisition Workshop, EKAW'97, (pp. 317-322).(Lecture Notes in AI). Springer-Verlag.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 14. Sep. 2021Modelling Competitive Co-operation of Agents in aCompositional Multi-Agent FrameworkFrances Brazier, Pascal van Eck and Jan TreurDepartment of Mathematics and Computer Science, Vrije Universiteit AmsterdamDe Boelelaan 1081a, NL-1081 HV Amsterdam, The NetherlandsURL: http://www.cs.vu.nl/, Email: {frances,patveck,treur}@cs.vu.nl1  IntroductionIn many multi-agent domains competitive agents need to co-operate. In this paper ageneric model for competitive agents is introduced: a model which can be used to sup-port the design of agents in diverse knowledge-intensive domains. An example of thetype of multi-agent situation in which this model can be applied is one in which a num-ber of agents wish to access a given (information) resource and explicit knowledge isavailable (to either the accessing agents, or the resource to be accessed, or all agents in-volved) on appropriate orderings or priorities between the transactions from the differ-ent agents. This model includes explicit knowledge of possible communication and co-operation strategies for individual agents, but also identifies the type of communicationneeded between agents and the interaction needed between agents and the materialworld, specified in the compositional multi-agent system modelling frameworkDESIRE [1,2].DESIRE is briefly introduced in Section 2. In Section 3, the role of competitive co-operation in a competitive situation is discussed. A generic model of an arbitrary com-petitive agent is presented in Section 4. Section 5 describes a generic model for  limitedresource acquisition. In Section 6, a comparison is made with an algorithmic approachto limited resource access in the domain of operating systems (see for example [4]).Discussion and further research are presented in Section 7.2  The Multi-Agent Modelling Framework DESIREThe multi-agent compositional modelling framework DESIRE provides support for thedesign of a conceptual model of the behaviour of (interacting) agents. Compositionalagent models define the structure of the architectures: components in a compositionalmodel are directly related to agents and their tasks. Existing generic agent models canbe used to design specific agent models. During analysis and design, relevantcomponents in a generic model are refined by (1) more detailed analysis of the tasks ofwhich such components are comprised and/or (2) inclusion of specific domainknowledge. The five types of knowledge repres nted in the DESIRE framework at aconceptual level, detailed level and at an operational levelare:• The compositional structure of agents and their tasks. Tasks can be composed orprimitive and are characterised by their input and output knowledge structures;• Interaction within and between agents and tasks;• Temporal relations between tasks, represented by rules in a temporal logic;• Delegation of tasks to agents;• Knowledge structures.The representation at the operational level is automatically generated from the repre-sentation at the detailed level.Published in: E. Plaza and R. Benjamins (Eds.) Knowledge Acquisition, Modeling and Management. LectureNotes in Artificial Intelligence, Vol. 1319, pp. 317-322, Springer-Verlag, 1997.3  A Generic Agent ModelTo model an agent capable of competitive co-operation, a generic model of an agent,developed in other multi-agent domains, is employed [6]. This model fulfils the fourcharacteristics required for the weak notion of agency described in [7]: agents must becapable of (1) maintaining interaction with their environment by observing andperforming actions in the world: reactivity; (2) taking the initiative: pro-activeness; (3)performing social actions like communication and co-operation: s cial ability; and (4)operating without the direct intervention of other (possibly human) agents: autonomy.This generic agent model has six top-level components as shown in Figure 2. Interac-tion with the environment is performed by the components Maintain World Informationand World Interaction Management. Social actions are managed by the componentsAgent Interaction Management and Cooperation Management. The agent’s processesare co-ordinated by the component Own Process Control, enabling the agent to actautonomously and take the initiative if this is required. Tasks specific to the agent itselfare included in the component Agent Specific Tasks. In most domains of application,these six components are further refined, as illustrated for competitive co-operation inSection 5 below. In general, refinement of a generic model involves specialisation (i.e.,components within components are distinguished) and instantiation (i.e., (domain) spe-cific instances of signatures and knowledge are defined). Agent models differ in thelevel of refinement required depending on the relative importance of the characteristicsinvolved. Knowledge required to refine agent models includes (1) knowledge of anagent’s priorities with respect to its processes, (2) knowledge of which and how infor-mation is exchanged with other agents and the external world, (3) knowledge of howinformation received from the external world and other agents is to be analysed and (4)knowledge of how co-operative an agent is in given situations in relation to otheragents. (In [8], other approaches to agent-based knowledge modelling can be found).Within DESIRE, agents are modelled as components that run concurrently, equippedwith information links for inter-agent information exchange. Task control at the toplevel of a multi-agent system is minimal: it is most often restricted to the initiation ofagents, an external world and the links between agents. As components, agents havetheir own internal agent task control.  This task control knowledge specifies(asynchronous) information exchange to other agents and the world, and (concurrent)activation or suspension of internal components.4  Competitive Agents in Co-operative Information SystemsIn many real life situations, co- peration is an effective approach for allocation oflimited resources. In this paper, housing is the limited resource sed to illustrate the useof a generic model for competitive co-operation. Allocation of apartments within thecity to individuals (with a monthly rent between x and y) is regulated by governmentalpolicy. Two key characteristics of the policy are the role assigned to the real estateagents, namely that of co-ordination agents, and the use of a static priority scheme(duration of an individual’s subscription).Practice shows that groups of subscribed individuals co-operate—together they deter-mine their chances and determine individual and group strategies. In deadlock situa-tions the real estate agent is responsible for conflict resolution. To design an agent sys-tem to support an individual in need of an apartment, a model of the interaction andknowledge required to effectuate strategies such as those discussed above, is needed.This example is used below to illustrate the generic aspects of competitive co-opera-tion.5  A GenericModel of aCompetitive Co-operative AgentCompet i t i ve  co -operation for resourceallocation requires atleast two agents and amaterial world.  InFigure 1, three agentsare depicted for thepurpose of illustration.In this figure, therounded boxes represent (composed or primitive) components, in this case agents. Thesmall boxes attached to the components’ sides, represent the agents’ input and outputinterfaces. The links in Figure 1 between agents are defined to allow communicationbetween agents; the links between the agents and the material world are defined toallow observations and actions to be performed. Task control is not depicted.Figure 2 shows the composition of an individual agent and the information exchangebetween its components. In the following paragraphs, each of these components will bedescribed. Due to space restrictions, it is not possible to provide more detail; instead theinterested reader is referred to [5].Maintain World Information  The component Maintain World Information stores theinformation an agent has about the world state, namely presence of agents andresources. This information can be acquired by observation of the world (via WorldInteraction Management), communication with other agents, or (defeasible) reasoning.Agent Specific Tasks  In this generic agent model, only one agent specific task ismodelled, namely Obtain Resource. Other agent specific tasks are not specified in thegeneric model (e.g. tasks in which the need to access a resource is determined).Cooperation Management  The component Cooperation Management consists of thefollowing four components (see Figure 3):• Update Current Agent Information  All relevant information on other agents,often obtained by communication and/or (defeasible) reasoning, is maintained here.• Determine Access  Input facts about (1) the world; e.g., obtained by observation,communication, or (default or closed world) assumptions, (2) priorities betweenagents (received from the component Determine Priority) and (3) co-operativenessof other agents (received from the component Determine Cooperation), are used toanalyse a world state (no matter how it was reached) and to draw conclusions aboutaccess to a resource.CompetitiveAgent 1CompetitiveAgent 2CompetitiveAgent 3MaterialWorldCompetitive Co-operationFig. 1:   Three agents and the material world.Most of the knowledge used in Determine Access specifies the conditions underwhich access to the resource will not be granted. The two rules shown below deter-mine whether there is a conflict in the sense that another agent and the agent itselfare both interested in accessing the resource. Note that the knowledge specified inthis knowledge base does not refer directly to the application domain described inSection 4. It is generic in the sense that it can in principle be used for all domains inwhich limited access to resources plays a role.if wants_resource(A) and wants_resource(self) then conflicting_needs(A,self)if conflicting_needs(A,self) and has_priority_over(A,self) then access_blocked_by(A)• Determine Priority  The task of the component Determine Priority is to determinewhich of two agents may access the resource first, if a conflict ex s s. The instan-tiation of Determine Priority contains domain specific knowledge, such asknowledge required for the domain outlined in Section 4. It is important to note thatthis knowledge may not always be sufficient to derive a unique conclusion: theremay be circumstances in which no priority can be assigned.• Determine Cooperation  Knowledge of which agents are willing to co-operatewith which other agents acquired through observation and reasoning, is used by thiscomponent to determine the level of co-operation. In the generic agent model, threeoptions for static facts about the agent self are specified: one for a shy agent, one fora bold agent and one for a moderate agent:if concluded(no_access_decision) and modest(self) then to_communicate_to(A,ok)World Interaction Management, Agent Interaction Management and Own ProcessControl  The tasks of the component World Interaction Management are (1) to performobservations (including observation of the presence and relevance of other agents) and(2) to perform the action proposed by Obtain Resource in the component AgentSpecific Tasks. The component Agent Interaction Management manages communica-tion between an agent and other agents. The role of the component Own ProcessControl is to determine which information is needed to decide whether access to aresource is allowed, and where this information is to be found.Agent Task ControlOwnProcesscontrolWorldInteractionManagementCooperationManagementMaintainWorldInformationAgentInteractionManagementAgentSpecificTasksCompetitive AgentnFig. 2:   Agent task composition and information exchange.A g e n t  t a s kcontrol Thecomponent OwnProcess Controldetermines whichinformat i on  i sneeded to decidewhether access to aresource is allowedand where thisinformation is tobe found. Taskcontrol knowledgespecifies activationof the componentOwn Process Control in one of the following ways: (1) The component AgentInteraction Management notices that one of the other agents requests attention, forexample to access a resource, andnotifies Own Process Control of the fact that newinformation has been receivd. On the basis of this new information, Own ProcessControl may decide that Cooperation Management is to be activated to determinewhether access is to be granted. (2) The component Agent Specific Tasks expresses aneed for information to the component Own Process Control, which decides whichspecific information is neded and where it is to be found. If Own Process Controlrecognises the need to access a limited resource, the component CooperationManagement is activated first to determine whether access is allowed. If access isallowed, Obtain Resource is activated.6  Comparison with an Algorithmic ApproachThe specification of the task model presented in this paper can be compared to ap-proaches to mutual exclusion problems in more conventional environments, such as thealgorithm described by Ricart and Agrawala [4]. Ricart and Agrawala’s algorithmassumes the following conditions hold: (1) each agent notices each other agent’s pres-ence; (2) communication never fails; (3) each agent has the same complete knowledgeof priorities between agents and (4) if agent A has higher priority than agent B, agent Bis assumed to communicate that it grants access to the resource to agent A.If these conditions hold, the task model described in this paper specifies the sameprocess as the algorithm. The conditions describe a rather strictly defined domain ofapplication, as found in, for example, the domain of operating systems. For less strictlydefined real world domains, however, incompleteness of observations, defeasiblecommunications, incomplete knowledge of priorities, inconsistencies between conclu-sions drawn by different agents, uncooperative agents, etc. are most common. Thecompositional model introduced here is particularly suitable in these domains for thefollowing reasons: (1) by virtue of the reflective structure of the model, different strate-gies can be modelled with minimal effort, (2) assumptions with respect to for instancecommunication, priorities and co-operation appear explicitly in the model, and (3) thedifferent types of knowledge are explicitly distinguished. The distinction betweendifferent types of knowledge and different types of behaviour results in flexibility,adaptability and transparency: essential characteristics of a knowledge-based approach.Cooperation ManagementDetermineCooperationDetermineAccessDeterminePriorityUpdate Current Agent InfoCooperation Management Task ControlFig. 3:   Composition of Cooperation Management.7  Discussion and Future ResearchIn this paper, a generic model is presented for competitive agents in a domain thatrequires limited access to a given resource. Modelling the specific types of knowledgeinvolved and the behaviour of agents in relation to each other results in a transparentcompositional model. One specific domain of co-operation has been used, in whichresource access is granted first on the basis of (given) priorities, and (if no decision canbe taken) on the basis of dynamically observed co-operativeness of other agents.However, the modularity of the architecture, in which both static and dynamicbehaviour are explicitly specified, allows for flexible adaptability to other strategies.Current research focuses among others on the further development of the semantics ofthe framework ([2]), on verification and validation ([3]) and on modelling beliefs,desires, intentions and commitments in multi-agent systems.Acknowledgements This research was partly supported by ESPRIT III BRA project6156 DRUMS II on Defeasible Reasoning and Uncertainty Management Systems.References1. Brazier, F.M.T., Treur, J., Wijngaards, N.J.E., and Willems, M. Formalspecification of hierarchically (de)composed tasks. In: Gaines, B.R. and Musen,M. (eds.), Proc. of the 9th Banff Knowledge Acquisition for Knowledge-BasedSystems Workshop, Vol. 2, pp. 25/1-25/20, SRDG Publications, Deptartment ofComputer Science, University of Calgary, 1995.2. Brazier, F.M.T., Treur, J., Wijngaards, N.J.E., and Willems, M. Temporalsemantics of complex reasoning tasks. In: Gaines, B.R. and Musen, M. (eds.),Proc. of the 10th Banff Knowledge Acquisition for Knowledge-Based SystemsWorkshop, Vol. 1, pp. 15/1-15/17, SRDG Publications, Deptartment of ComputerScience, University of Calgary, 1996. Extended version to appear in Data andKnowledge Engineering.3. Cornelissen, F., Jonker, C. and Treur, J. Compositional verification of knowledge-based systems: A case study for diagnostic reasoning. In: Proc. European Know-ledge Acquisition Workshop EKAW’97, Springer-Verlag, 1997 (this volume).4. Ricart, G. and Agrawala, A.K. An optimal algorithm for mutual exclusion in com-puter networks. Comm. ACM, Vol. 24(1), pp. 9-17, Jan. 1981.5. Brazier, F.M.T., Van Eck, P.A.T. and Treur, J. Modelling cooperative behaviourfor resource access in a compositional multi-agent framework. In: Fiadeiro, J.L.and Schobbens, P.-Y. (eds.) Proc. 2nd Workshop of the ModelAge Project,ModelAge’96, Universidade de Lisboa, 1996.6. Brazier, F.M.T., Dunin Keplicz, B., Jennings, N. and Treur, J. DESIRE: ModellingMulti-Agent Systems in a Compositional Formal Framework, InternationalJournal of Cooperative Information Systems, Vol. 6, Huhns, M. and  Singh, M.(eds.), Special Issue on Formal Methods in Cooperative Information Systems:Multi-Agent Systems, 1997.7. Wooldridge, M.J. and Jennings, N.R. Intelligent agents: Theory and practice. TheKnowledge Engineering Review, 10(2):115-152, 1995.8. Dieng, R., Corby, O. and Labidi, S. Agent-based knowledge acquisition. In:Steels, L., Schreiber, G. and Van de Velde, W. (eds.) A Future for KnowledgeAcquisition. Proc. EKAW’94. LNAI, Vol. 867, pp. 63-82, Springer-Verlag, 1994.

English

Modelling Competitive Co-operation of Agents in a Compositional Multi-Agent Framework

DSpace at VU

Modelling Competitive Co-operation of Agents in a
Compositional Multi-Agent Framework
Frances Brazier, Pascal van Eck and Jan Treur
Department of Mathematics and Computer Science, Vrije Universiteit Amsterdam
De Boelelaan 1081a, NL-1081 HV Amsterdam, The Netherlands
URL: http://www.cs.vu.nl/, Email: {frances,patveck,treur}@cs.vu.nl
1  Introduction
In many multi-agent domains competitive agents need to co-operate. In this paper a
generic model for competitive agents is introduced: a model which can be used to sup-
port the design of agents in diverse knowledge-intensive domains. An example of the
type of multi-agent situation in which this model can be applied is one in which a num-
ber of agents wish to access a given (information) resource and explicit knowledge is
available (to either the accessing agents, or the resource to be accessed, or all agents in-
volved) on appropriate orderings or priorities between the transactions from the differ-
ent agents. This model includes explicit knowledge of possible communication and co-
operation strategies for individual agents, but also identifies the type of communication
needed between agents and the interaction needed between agents and the material
world, specified in the compositional multi-agent system modelling framework
DESIRE [1,2].
DESIRE is briefly introduced in Section 2. In Section 3, the role of competitive co-
operation in a competitive situation is discussed. A generic model of an arbitrary com-
petitive agent is presented in Section 4. Section 5 describes a generic model for  limited
resource acquisition. In Section 6, a comparison is made with an algorithmic approach
to limited resource access in the domain of operating systems (see for example [4]).
Discussion and further research are presented in Section 7.
2  The Multi-Agent Modelling Framework DESIRE
The multi-agent compositional modelling framework DESIRE provides support for the
design of a conceptual model of the behaviour of (interacting) agents. Compositional
agent models define the structure of the archi tectures: components in a compositional
model are directly related to agents and their tasks. Existing generic agent models can
be used to design specific agent models. During analysis and design, relevant
components in a generic model are refined by (1) more detailed analysis of the tasks of
which such components are comprised and/or (2) inclusion of specific domain
knowledge. The five types of knowledge represented in the DESIRE framework at a
conceptual level, detailed level and at an operational level are:
• The compositional structure of agents and their tasks. Tasks can be composed or
primitive and are characterised by their input and output knowledge structures;
• Interaction within and between agents and tasks;
• Temporal relations between tasks, represented by rules in a temporal logic;
• Delegation of tasks to agents;
• Knowledge structures.
The representation at the operational level is automatically generated from the repre-
sentation at the detailed level.
Published in: E. Plaza and R. Benjamins (Eds.) Knowledge Acquisition, Modeling and Management. Lecture
Notes in Artificial Intelligence, Vol. 1319, pp. 317-322, Springer-Verlag, 1997.
3  A Generic Agent Model
To model an agent capable of competitive co-operation, a generic model of an agent,
developed in other multi-agent domains, is employed [6]. This model fulfils the four
characteristics required for the weak notion of agency described in [7]: agents must be
capable of (1) maintaining interaction with their environment by observing and
performing actions in the world: reactivity; (2) taking the initiative: pro-activeness; (3)
performing social actions like communication and co-operation: social ability; and (4)
operating without the direct intervention of other (possibly human) agents: autonomy.
This generic agent model has six top-level components as shown in Figure 2. Interac-
tion with the environment is performed by the components Maintain World Information
and World Interaction Management. Social actions are managed by the components
Agent Interaction Management and Cooperation Management. The agent’s processes
are co-ordinated by the component Own Process Control, enabling the agent to act
autonomously and take the initiative if this is required. Tasks specific to the agent itself
are included in the component Agent Specific Tasks. In most domains of application,
these six components are further refined, as illustrated for competitive co-operation in
Section 5 below. In general, refinement of a generic model involves specialisation (i.e.,
components within components are distinguished) and instantiation (i.e., (domain) spe-
cific instances of signatures and knowledge are defined). Agent models differ in the
level of refinement required depending on the relative importance of the characteristics
involved. Knowledge required to refine agent models includes (1) knowledge of an
agent’s priorities with respect to its processes, (2) knowledge of which and how infor-
mation is exchanged with other agents and the external world, (3) knowledge of how
information received from the external world and other agents is to be analysed and (4)
knowledge of how co-operative an agent is in given situations in relation to other
agents. (In [8], other approaches to agent-based knowledge modelling can be found).
Within DESIRE, agents are modelled as components that run concurrently, equipped
with information links for inter-agent information exchange. Task control at the top
level of a multi-agent system is minimal: it is most often restricted to the initiation of
agents, an external world and the links between agents. As components, agents have
their own internal agent task control.  This task control knowledge specifies
(asynchronous) information exchange to other agents and the world, and (concurrent)
activation or suspension of internal components.
4  Competitive Agents in Co-operative Information Systems
In many real life situations, co-operation is an effective approach for allocation of
limited resources. In this paper, housing is the limited resource used to illustrate the use
of a generic model for competitive co-operation. Allocation of apartments within the
city to individuals (with a monthly rent between x and y) is regulated by governmental
policy. Two key characteristics of the policy are the role assigned to the real estate
agents, namely that of co-ordination agents, and the use of a static priority scheme
(duration of an individual’s subscription).
Practice shows that groups of subscribed individuals co-operate—together they deter-
mine their chances and determine individual and group strategies. In deadlock situa-
tions the real estate agent is responsible for conflict resolution. To design an agent sys-
tem to support an individual in need of an apartment, a model of the interaction and
knowledge required to effectuate strategies such as those discussed above, is needed.
This example is used below to illustrate the generic aspects of competitive co-opera-
tion.
5  A Generic
Model of a
Competitive Co-
operative Agent
C o m p e t i t i v e  c o -
operation for resource
allocation requires at
least two agents and a
material world.  In
Figure 1, three agents
are depicted for the
purpose of illustration.
In this figure, the
rounded boxes represent (composed or primitive) components, in this case agents. The
small boxes attached to the components’ sides, represent the agents’ input and output
interfaces. The links in Figure 1 between agents are defined to allow communication
between agents; the links between the agents and the material world are defined to
allow observations and actions to be performed. Task control is not depicted.
Figure 2 shows the composition of an individual agent and the information exchange
between its components. In the following paragraphs, each of these components will be
described. Due to space restrictions, it is not possible to provide more detail; instead the
interested reader is referred to [5].
Maintain World Information  The component Maintain World Information stores the
information an agent has about the world state, namely presence of agents and
resources. This information can be acquired by observation of the world (via World
Interaction Management), communication with other agents, or (defeasible) reasoning.
Agent Specific Tasks  In this generic agent model, only one agent specific task is
modelled, namely Obtain Resource. Other agent specific tasks are not specified in the
generic model (e.g. tasks in which the need to access a resource is determined).
Cooperation Management  The component Cooperation Management consists of the
following four components (see Figure 3):
• Update Current Agent Information  All relevant information on other agents,
often obtained by communication and/or (defeasible) reasoning, is maintained here.
• Determine Access  Input facts about (1) the world; e.g., obtained by observation,
communication, or (default or closed world) assumptions, (2) priorities between
agents (received from the component Determine Priority) and (3) co-operativeness
of other agents (received from the component Determine Cooperation), are used to
analyse a world state (no matter how it was reached) and to draw conclusions about
access to a resource.
Competitive
Agent 1
Competitive
Agent 2
Competitive
Agent 3
Material
World
Competitive Co-operation
Fig. 1:   Three agents and the material world.
Most of the knowledge used in Determine Access specifies the conditions under
which access to the resource will not be granted. The two rules shown below deter-
mine whether there is a conflict in the sense that another agent and the agent itself
are both interested in accessing the resource. Note that the knowledge specified in
this knowledge base does not refer directly to the application domain described in
Section 4. It is generic in the sense that it can in principle be used for all domains in
which limited access to resources plays a role.
if wants_resource(A) and wants_resource(self) then conflicting_needs(A,self)
if conflicting_needs(A,self) and has_priority_over(A,self) then access_blocked_by(A)
• Determine Priority  The task of the component Determine Priority is to determine
which of two agents may access the resource first, if a conflict exists. The instan-
tiation of Determine Priority contains domain specific knowledge, such as
knowledge required for the domain outlined in Section 4. It is important to note that
this knowledge may not always be sufficient to derive a unique conclusion: there
may be circumstances in which no priority can be assigned.
• Determine Cooperation  Knowledge of which agents are willing to co-operate
with which other agents acquired through observation and reasoning, is used by this
component to determine the level of co-operation. In the generic agent model, three
options for static facts about the agent self are specified: one for a shy agent, one for
a bold agent and one for a moderate agent:
if concluded(no_access_decision) and modest(self) then to_communicate_to(A,ok)
World Interaction Management, Agent Interaction Management  and Own Process
Control  The tasks of the component World Interaction Management are (1) to perform
observations (including observation of the presence and relevance of other agents) and
(2) to perform the action proposed by Obtain Resource in the component Agent
Specific Tasks. The component Agent Interaction Management manages communica-
tion between an agent and other agents. The role of the component Own Process
Control is to determine which information is needed to decide whether access to a
resource is allowed, and where this information is to be found.
Agent Task Control
Own
Process
control
World
Interaction
Management
Cooperation
Management
Maintain
World
Information
Agent
Interaction
Management
Agent
Specific
Tasks
Competitive Agentn
Fig. 2:   Agent task composition and information exchange.
A g e n t  t a s k
control The
component Own
Process Control
determines which
informat i o n  i s
needed to decide
whether access to a
resource is allowed
and where this
information is to
be found. Task
control knowledge
specifies activation
of the component
Own Process Control in one of the following ways: (1) The component Agent
Interaction Management notices that one of the other agents requests attention, for
example to access a resource, and notifies Own Process Control of the fact that new
information has been received. On the basis of this new information, Own Process
Control may decide that Cooperation Management is to be activated to determine
whether access is to be granted. (2) The component Agent Specific Tasks expresses a
need for information to the component Own Process Control, which decides which
specific information is needed and where it is to be found. If Own Process Control
recognises the need to access a limited resource, the component Cooperation
Management is activated first to determine whether access is allowed. If access is
allowed, Obtain Resource is activated.
6  Comparison with an Algorithmic Approach
The specification of the task model presented in this paper can be compared to ap-
proaches to mutual exclusion problems in more conventional environments, such as the
algorithm described by Ricart and Agrawala [4]. Ricart and Agrawala’s algorithm
assumes the following conditions hold: (1) each agent notices each other agent’s pres-
ence; (2) communication never fails; (3) each agent has the same complete knowledge
of priorities between agents and (4) if agent A has higher priority than agent B, agent B
is assumed to communicate that it grants access to the resource to agent A.
If these conditions hold, the task model described in this paper specifies the same
process as the algorithm. The conditions describe a rather strictly defined domain of
application, as found in, for example, the domain of operating systems. For less strictly
defined real world domains, however, incompleteness of observations, defeasible
communications, incomplete knowledge of priorities, inconsistencies between conclu-
sions drawn by different agents, uncooperative agents, etc. are most common. The
compositional model introduced here is particularly suitable in these domains for the
following reasons: (1) by virtue of the reflective structure of the model, different strate-
gies can be modelled with minimal effort, (2) assumptions with respect to for instance
communication, priorities and co-operation appear explicitly in the model, and (3) the
different types of knowledge are explicitly distinguished. The distinction between
different types of knowledge and different types of behaviour results in flexibility,
adaptability and transparency: essential characteristics of a knowledge-based approach.
Cooperation Management
Determine
Cooperation
Determine
Access
Determine
Priority
Update 
Current 
Agent Info
Cooperation Management Task Control
Fig. 3:   Composition of Cooperation Management.
7  Discussion and Future Research
In this paper, a generic model is presented for competitive agents in a domain that
requires limited access to a given resource. Modelling the specific types of knowledge
involved and the behaviour of agents in relation to each other results in a transparent
compositional model. One specific domain of co-operation has been used, in which
resource access is granted first on the basis of (given) priorities, and (if no decision can
be taken) on the basis of dynamically observed co-operativeness of other agents.
However, the modularity of the architecture, in which both static and dynamic
behaviour are explicitly specified, allows for flexible adaptability to other strategies.
Current research focuses among others on the further development of the semantics of
the framework ([2]), on verification and validation ([3]) and on modelling beliefs,
desires, intentions and commitments in multi-agent systems.
Acknowledgements This research was partly supported by ESPRIT III BRA project
6156 DRUMS II on Defeasible Reasoning and Uncertainty Management Systems.
References
1. Brazier, F.M.T., Treur, J., Wijngaards, N.J.E., and Willems, M. Formal
specification of hierarchically (de)composed tasks. In: Gaines, B.R. and Musen,
M. (eds.), Proc. of the 9th Banff Knowledge Acquisition for Knowledge-Based
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Modelling Competitive Co-operation of Agents in a Compositional Multi-Agent Framework

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