Modeling for Complex Adaptive System Organization

Jiang Chen Troop NO. 91872

PLA Beijing, China

jiangchen08080909@126.com

Liu Ming Factory NO. 447

PLA Baotou, China

zgykybxjz@126.com

Li Xiong, Fu Jia and Dong Fei Department of Command

Academy of Armored Force Engineering Beijing 100072, China lixiong2010@sina.com

Abstract—The complexity in organizations and their environments call for organizational adaptation in the form of well designed and yet spontaneous changes of structure, process, and strategy. An agent-based solution consists in designing and implementing several agents which co-operate in order to reach their internal goals. Therefore, based on analysis on complexity of social system organizations and complex adaptive system organization, agent-based modeling approach is used and case study of intelligence reconnaissance system modeling is studied. The results are useful to modeling for complex adaptive system organization.

Keywords-complex adaptive system; modeling; agent; agent-based modeling; intelligence reconnaissance system

I. INTRODUCTION If we look at an organization in terms of the metaphor of

the complexity theory, we perceive it as a complex adaptive system (CAS). It contains a great number of independent entities, agents that behave in accordance with its goals, and their relationship is that of mutual interaction. Due to connections and interactions there occur higher levels of organizing, cores of new structures, self-organization, and emergence.

One of the typical definition of complex system is as follows: complex systems are systems with multiple interacting components whose behaviour cannot be simply inferred from the behaviour of the components. This definition precisely points out the constitutive features of complex system. James Coleman proposes to explain “the behaviour of social systems by means of three components: the effects of properties of the system on the constraints or orientations of actors; the actions of actors who are within the system; and the combination or interaction of those actions, bringing bout the systemic behaviour”.

Multi-agent systems have been studied for the past few decades. A multi-agent system can be studied as a computer system that is concurrent, asynchronous, stochastic and distributed. These characteristics of multi-agent systems make them also a discrete-event dynamic system, and these have been studied under several analytical methodologies. In fact, agent-based modeling approach can be used in modeling for complex adaptive system organization.

II. COMPLEXITY OF SOCIAL SYSTEM ORGANIZATIONS Complexity of a social system represents a

multidimensional social reality. Researching social system is to answer the question which elements of that complex entity play the main role in explanation of some aspects of social reality. Complexity of social system organizations represents different types of social phenomena and at the same time emphasises multidimensional nature of social world. Social system is formed by the characteristic of it components.

In order to show how complex is the social system I would like to analyze few aspects of complexity. There are many forms and types of social relations and they can generate different social forms: one can distinguish so called weak and incidental interactions, more petrified relations, up to social ties and social structure. If the system is complex then its elements are numerous and they are in mutual relations.

This particular example shows how complicated may be analyzing the social relations.

Other elements of social reality may take also diverse forms. Each subsystem consists of various levels of social organization: from simple through more complicated: individuals, social entities, groups, communities, institutions. One of the most typical examples showing how parts of a system give rise to the collective behaviors of the system is to compare the individual actions and collective actions.

The interrelations of collective actions are usually more complex and the effects of such activity are different. It must be emphasized that most sociologists would agree that action takes place at the level of individual actors and the more complex level exist as emergent properties characterizing the system of action as a whole. “It is only in this sense that there is behavior of the system” [1]. If one considers the institutional aspect of social system then, again, one has to deal with patterns of behaviour, system of norms and social rules. At this point culture as a object of analysis appears. Complex Adaptive System Organization

The idea of the complex system denotes presence of many independent entities, agents that behave in accordance with their objectives, and perform mutual interactions. At that it is important to observe that the complex system does not allow simple reduction, as is the case with a multitude of

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unconnected elements. That is why it is sometimes difficult to understand the behaviour rules of the complex system since it is not possible to set up a simple and yet satisfactory model. This issue is an important task for the managers as they are faced with concrete challenges in their organizations on a daily basis.

Complexity as a characteristic feature occurs and grows when interdependence of the elements within the system becomes relevant. In such systems each part or agent has significance of its own, and removal of certain element from the system leads toward destruction of the existing system’s behaviour. CAS are open systems whose components are firmly interrelated and have the ability of self-organizing and dynamics. There are also certain local rules that apply to these components or agents. The dynamics is present because of interrelations, interactions and influences of numerous agents. As a result, CAS are subject to constant and discontinuous changes.

The aforementioned interactions among system's elements may result in occurrence of certain higher levels of organization, cores of new structures, and this phenomenon is called emergence. Elements or agents in organizations are individuals, organizational units, groups and so on. The occurrence of well-known informal organizational groups that significantly distort the structure defined by the purposeful design of organization, can be explained by the complexity conditions. Agents connect in accordance with their specific goals and interests. However, in real organizations they often connect at the expense of real, declared organizational goals. One desired scenario is the situation when self-organizing is motivated by learning within the organizations with a purpose of adapting the structure to external challenges and thus improving performances of the system itself.

Organizational adaptation to environment with the option of changing its structure is an important phenomenon in both theory and practice of the organizational design and organizational changes.

According to the system theory, the effects of the process balancing in the traditional control paradigm are achieved by means of negative feedback (Fig. 1). The behaviour of the system can be controlled by sending the output results relative to certain desired values back to the input segment of the process development. Such mechanism may serve to control the behaviour of social systems and it represents a contribution to the organizational theory studies.

Figure 1. Negative feedback (an example).

While the negative feedback acts as a stabilizer of the system, the positive feedback activates the process of amplifying that may lead toward instability after a certain time. Nonlinearity is a phenomenon that can be explained in

different ways. Due to numerous connections and interactions in CAS, the outcomes of processes and events are nonlinear with regard to the values of input variables. In the environment of organizational activities nonlinear processes are mainly unwanted because they decrease the possibility of control and adequate responses to impacts and events in the environment. Nonlinear occurrences imply circumstances of disproportional relative changes in the input-output states of the processes, for example, if some company is successfully increased their production but this phenomena does not have consequences in proportional growth of their profit, due to the saturation of markets.

However, when applied to the creation of responses to the challenges of environment, nonlinearity may be useful and desirable. Organizations represent adaptive and intelligent entities since they can take actions that were not pre-planned, and the final outcome is not just a simple sum of isolated individual efforts. The actual performance is also a result of the included nonlinear processes [3, 4]. The systems that possess distinctive CAS attributes demonstrate emergent rather than deterministic behaviors. The type of control in these systems is self-organizing and, to a lesser extent, centralized and hierarchical control.

Figure 2. Commitment to “Just in time” system – influences.

The amplifying mechanism in the case of CAS is often joined with the stabilizing process that includes conditions of limitation and, consequently, keeps the growth inside certain regular boundaries. Such pattern for CAS was illustrated by Senge in the example of introduction of Just in Time System in business (Fig. 2).

III. AGENT-BASED MODELING APPROACH AND CASE STUDY

An intelligent agent is a software component which exhibits specific characteristics such as autonomy - having its own execution context (code and data), reactivity - capability to react to notifications, pro-activity/capability to make a decision based upon its own internal knowledge, social ability/capability to exchange knowledge with other agents by using specific communication channels (ACC) and a specific language (ACL).

The concept of intelligence means that the agent is provided with knowledge of the user's wishes and makes use of

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this knowledge, it also refers to the intelligent behaviour emerging from the agents societies. Developing multi-agent systems requires a bottom-up approach. This has an advantage - multi-agent systems can solve problems that have not been envisaged during system development. Consequently, they are not developed for a specific task, but are designed for the general solution of problems.

On information battlefield, the internal members of intelligence reconnaissance system (IRS) run with autonomy and interaction. The general reconnaissance platforms (photo-reconnaissance vehicles, electronic reconnaissance vehicles, armored reconnaissance vehicles, UAVs) accept the instructions and orders from information processing vehicles, and take actions including scouting advance, alertness reconnaissance, awaiting orders. The information processing vehicles receive the intelligence from the general reconnaissance platforms, carry through information fusion and make decision. There are also a lot of cooperation activities among different general reconnaissance platforms. IRS is so alike a multi-agent system in behaviors that we can set up mappings from the internal members of IRS to agents, e.g., photo-reconnaissance vehicle � photo-reconnaissance vehicle agent, UAV � UAV agent.

The function agents in Red force include unmanned aerial vehicles agents (UAVA), photo-reconnaissance vehicle agents (PRVA), radar reconnaissance vehicle agents (RRVA), armored reconnaissance vehicle agents (ARVA), electronic reconnaissance vehicle agents (ERVA), land sensor agents (LSA), and combat command vehicle agent (CCVA). They are aggregated into the Red agents federation.

The function agents in Blue force are similar to those Red force agents, but some different agents, e.g., armored cavalry vehicle agents (ACVA), and information processing vehicle agents (IPVA) are designed since there are some differences in force organization. They are aggregated into the Blue agents federation.

Figure 3. Framework of multi-agent IRS.

Of course, we can add or cut down some function agents in the Red or Blue agents federation according to the actual simulation design and development.

The administration agents and service agents include federation manager agent, declaration manager agent, time manager agent, data distribution manager agent, and so on, which play the roles of demonstration control (DC), simulation evaluation (SE), data base (DB), situation displaying (SD), command practice (CP) and battlefield environment (BE). These agents can be aggregated into the “White” agents federation.

In this way, we can design the basic organization of platform-level distributed multi-agent IRS system as shown in Fig. 3.

In this paper, we only take one entity agent in the Red or Blue agents federation as example to illuminate the architectures of agents. In fact, the operation principium of agents in White agents federation is accordant. There are only some differences in the definitions and operation contents because of the differences in their functions [6], [7].

Since Contract Net Protocol (CNP) [13, 14] proposes episodic rounds of inter-communication acts (announcements, bids, award messages) and shows its usefulness widely, in this paper we use a modified CNP. In our case, the warfare system consists of a red armored force unit (one combat command vehicle and nine tanks) and a blue army troop (one information processing vehicle, one tank, one missile launch vehicle, one trench mortar, and some other fire platforms). The Contract-Net initiator as a manager represents the combat command vehicle agent or information processing vehicle agent, and all other participants as contractors represent the other entity agents.

In our model the manager wishes a task to be performed by one or a group of agents according to some arbitrary function which characterizes the task. The manager issues the call for proposals, and other interested agents can send proposals. In contrast to the original Contract Net Protocol, there is no need to do anything if an agent playing a role of a potential contractor is not interested in submitting proposals. That means that our contract-net model from the very beginning relies on the notion of timeout, i.e. some actions need to be performed in the event of a lack of enough proposals or even in the case of a complete lack of proposals.

Figure 4. Custom contract net protocol.

Simulation infrastructure

Blue agents federation

LSA

PRVA

ACVA

ERVA

ARVA

UAVA

IPVA

Blue agents

Red agents federation

LSA

Red agents

PRVA

RRVA

ERVA

ARVA

UAVA

CCVA

White agents federation

DB

SE

SD CP

BE

DC

White agents

Call for Proposals

Action

Propose Conditions

Refuse Reason

Accept

Failure Reason

Inform Done(action)

Cancel Reason

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The proposals are collected by the manager, and then they are refused or accepted. The accepted proposals can be cancelled, either, by the manager via a cancel action, or by the contractor via a failure action. In case of cancellation other submitted proposals can be reconsidered, or a completely new call for proposals can be issued. The schematic representation is presented on Figure 5.

TABLE I MESSAGE CONTENT

CollaborationMessageContent → TYPE CollaborationMessageContent is GenericMessageContent content: MsgType, Item, ReconnaissanceTaskCost, Agent (* a proposal *) ! MsgContent content: MsgType, Agent, Item, Agent, ReconnaissanceTaskCost, Reconnaissance-TaskCost (* call for proposal *) ! MsgContent ENDTYPE

TABLE II OUTGOING AND RECEIVING MESSAGES

Deciding the actively outgoing messages Deciding the receiving messages

Send !msg(id, ..., cnt(callProposal,...)) [] Send !msg(id, ..., cnt(CollaborationSuccess,...)) [] Send !msg(id, ..., cnt(CollaborationFailure,...))

[mt eq subscribe] [] ... [] [mt eq propose]

TABLE III REACTIVE ACTIONS AND CONSTRAINTS

Deciding the reactively outgoing messages Add more constraints

[mt eq cancelSubscription] ! ( Send !msg(id, ..., cnt(acceptCancelSub, ...) [] Send !msg(id, ..., cnt(rejectCancelSub, ...) )

[mt eq cancelSub] ! ( [sender ne winner] ! Send !msg(id, ..., cnt(acceptCancelSub, ...)); ... [] [sender eq winner] ! Send !msg(id, ..., cnt(rejectCancelSub, ...)); ... )

In this paper, the Contract Net (CN) protocol is used to study collaboration of multiple intelligence reconnaissance platform agents. The protocol proposes episodic rounds of inter-communication acts (announcements, bids, award messages). It is a simple and widely used protocol and does not affect too much the system responsiveness [5].

The generic collaboration framework based on the CN protocol provides generic data types for reuse, and templates for building correct protocols. Specialization of the framework consists of the following steps:

(1) Extending, renaming and overloading existing data types.

(2) Deciding the types of messages.

(3) Deciding the states reflecting the evolution of a protocol.

(4) Adding constraints to remove undeterminable choices due to incompleteness.

(5) Refining the protocol to a suitable level by repeating the above steps.

A message content consists of a message type and other data. We can define different message content by overloading “content”, as shown in Table I. As far as behavior specialization, we need to decide all the message types a role can send out actively and will receive, as shown in Table II.

Then, we can decide the actions a role will perform after it receives a specified type of message and add more constraints to remove choices that are not deterministic (See Table III).

IV. CONCLUSIONS In view of organization, the system approach extends to the

theory of complexity, which, in terms of social systems, primarily affirms the important concept of the so-called complex adaptive systems (CAS). CAS are characterized by several key attributes that can be concisely described by terms reflecting the behaviour of these systems: complexity, agents, emergence self-organizing, adaptability, nonlinearity. Conventional modeling methods, e.g., linearization, can not cater for the requirements on autonomy and interaction of entities in military simulation systems. In this paper, we proposed a cooperative multiple intelligence reconnaissance platform agents model by using multi-agent-oriented modeling method to solve the problems. We put forward the framework of multi-agent-oriented IRS and the architecture of intelligence reconnaissance platform agents, and furthermore set up the collaboration model by the CN protocol approach to provide an insight into the self-organized criticality in this entity agents network. Although there are only a few entities in the demonstration system of our distributed multi-agent-oriented IRS simulation model and it needs to be studied furthermore to be more practical, it shows that our model can be used to understand the external, complex and intelligent combat resources application activities and can realize the dynamic platform-level battlefield entities simulation.

REFERENCES

[1] Zhongzhi Shi, Intelligent Agents and Their Applications. Beijing: Science Press, 2000, pp.16-29.

[2] Junichi K., Tomoki H., etc, “Multi-agent-based autonomous power distribution network restoration using contract net protocol,” Electrical Engineering in Japan, 2009, vol.166, pp. 45-58.

[3] T. J. Norman, A. Preece, and S. Chalmers, etc, “Agent-based formation of virtual organizations,” International Journal of Knowledge Based Systems, vol. 17, pp. 103-111, 2004.

[4] Xiong Li, Gaotian Pan, Zhiming Dong, Dianbo Cui and Hongwei An, “Designing of Multi-agent-based of Complex Warfare System Simulation Model,” DCDIS, Series A, 2006, 7(S3), pp. 953-959.

[5] S. Paurobally, J. Cunningham, and N. R. Jennings. “Verifying the contract net protocol: a case study in interaction protocol and agent communication language semantics,” 2nd IWLCMS, 2004, pp. 98-117.

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