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ABSTRACT ON THE THEME:

«The organization’s Model as the Artificial Intellectual System for the Knowledge Based Analysis and Control»

The information technologies as the aggregate of the knowledge on carrying out of the informational processes that transform the original (initial) knowledge into the derived (secondary) one are the main technologies of the informational society. The knowledge plays the role of both the resource and product of the technological process. The basic technologies of information society are information technologies which represent set of knowledge of carrying out of information processes, which transform initial (initial) knowledge in secondary knowledge. Here the knowledge here is like a resource and like a product of technological process.
This causes the necessity to make constant renewal of different knowledge of the companies and organizations which is the intellectual capital that provides their stable strategic positions at the market. The new management function is formed. It covers the intellectual capital accumulation, finding out and spreading of the available information and experience of creation of prerequisites for the knowledge spreading and transferring [1]. The knowledge becomes the source of high productivity, innovations and competitive advantages. There is a necessity in creation of the knowledge management systems (KMS), which are the interrelated aggregate of the organizational procedures, people, and information technologies providing collection, accumulation, organization, spreading and application of the knowledge to solve the problems of the high grade informational provision of carrying out of the business processes and the specialist interactive co-operation.
The integration of the great number of heterogeneous and very often spread according to the territory knowledge sources to solve general problems is the KMC distinguishing feature.
The KMC, in contrast to the traditional information documentation provision systems, transforms the knowledge into the complete product with the high consumer cost as the knowledge, compared with the information obtained in accordance with the traditional query, corresponds to the character of the task under discussion and can be applied to the solution choice.
The KCS creates the interactive environment of the people communication which generates new knowledge immediately coming to the corporate memory for its further application. Any company or organization is transformed into the educated establishment with the help of the KCS. It creates the «knowledge spiral» in which «the unknown (implicit) knowledge is to be identified and spread to become an element of the new individualized knowledge base of any member of the staff. To come to a new level and expand the knowledge base applied to different spheres the spiral is renewed»[1].
The knowledge based system can be a component of the computer training systems. The system gets the information on the activity of some object ( let us say, a student) and analyses its behavior. The knowledge base is changed in accordance with the object’s behavior. It defines the student’s abilities for the course main trends and makes up the curriculum according to the obtained data.
The intellectual tasks which are solved with the KCS have weak formalization which stipulates for illegible instructions and task solving condition description. Besides, the knowledge level and the solution evaluation criteria are different with different users. The standard steps to solve the intellectual task are as follows: The KMS can be applied at every stage of the task solving. It makes the iteration search in the corporate memory which provides every stage goal achievement.
The formalizing of the description of the system as the intellectual organization is one of the difficult solving stage. It is defined by the processes of the intensive circulation and processing of the knowledge obtained from the heterogeneous sources distributed in accordance with the territories. Because of this the network structures give efficient description of the intellectual system. They are convenient to be examined according to the artificial intellect distributed, that is multiagent, system, terms [3].
The agent is the programming module built in accordance with the knowledge based technology. It is a useful metaphor for the agent oriented system describing the knowledge circulation within the system.
It is impossible to give strict definition to the term «agent» because the formulation of this term is defined by a direction of researches and the development using the term «agent», the problems, which were given before developers.
In this case, the term «agent» is convenient metaphor for realization properties of elements of the system such: Analysis and design of this system executed on a basis of Gaia-methodology, which has been specifically tailored to the analysis and design of agent-based systems, which are intended to support the development of distributed problems solvers in which the system's constituent components are known in design time and in which all agents are expected to cooperate towards the achievement of global goal. This methodology is not suitable for the analysis and design of systems, where openness and self-interest are key factors.
Gaia is intended to allow an analyst to go systematically from a statement of requirements to a design that is sufficiently detailed that it can be implemented directly.The requirements capture phase as being independent of the paradigm used for analysis and design.In applying Gaia, the analyst moves from abstract to increasingly concrete concepts.The main models used in Gaia are summarised in Figure 1.
The basic stages of Gaia-methodology
Figure 1. The basic stages of Gaia-methodology
Analysis and design can be thought of as a process of developing increasingly detailed models of the system to be constructed.
The main Gaian concepts can be divided into two categories: abstract and concrete. Abstract and concrete concepts are summarised in Table 1.
Abstract entities are those used during analysis to conceptualise the system, but which do not necessarily have any direct realisation within the system.
Concrete entities, in contrast, are used within the design process, and will typically have direct counterparts in the run-time system.
Так понятию «агент» на стадии проектирования соответствует понятие «роль». Понятие «роль» является основным понятием на стадии проектирования системы. Gaia-методология базируется на понятии того, что многоагентная система - вычислительная организация, основанная на взаимодействии различных ролей. The main Gaian concepts can be divided into two categories: abstract and concrete; abstract and concrete concepts are summarised in Table 1.
Table 1. Abstract and concrete concepts in Gaia
Abstract concepts Concrete concepts
roles agent types
permissions services
responsibilities acquaintances
activities -
protocols -
liveness properties -
Safety properties -
The objective of the analysis stage is to develop an understanding of the system and its structure (without reference to any implementation detail). This understanding is captured in the system's organisation. Organisation is a collection of roles, that stand in certain relationships to one another. A role is defined by four attributes:
Responsibilities determine functionality and, as such, are perhaps the key attribute associated with a role. A role is created in order to do something. That is, a role has a certain functionality. Responsibilities are divided into two types:
Liveness responsibilities are those that, intuitively, state that «something good happens». Liveness responsibilities are so called because they tend to say that «something will be done», and hence that the agent carrying out the role is still alive. Liveness responsibilities tend to follow certain patterns. Liveness properties are speci ed via a liveness expression, which de nes the «life-cycle» of the role.
The general form of a liveness expression is:
RoleName = expression,
where RoleName is the name of the role whose liveness properties are being defined,
expression is the liveness expression de ning the liveness properties of RoleName.
The atomic components of a liveness expression are either activities or protocols.
Intuitively, a safety property states that «nothing bad happens» (i.e., that an acceptable state of affairs is maintained across all states of execution).
To realise responsibilities, a role has a set of permissions.Permissions are the «rights» associated with a role. The permissions of a role thus identify the resources that are available to that role in order to realise its responsibilities.Also they state the resource limits within which the role executor must operate.in order to carry out a role, an agent will typically be able to access certain information. Some roles might generate information; others may need to access a piece of information but not modify it, while yet others may need to modify the information.
The activities of a role are computations associated with the role that may be carried out by the agent without interacting with other agents. Activities are thus «private» actions.
There are inevitably dependencies and relationships between the various roles in a multi-agent organisation.Indeed, such interplay is central to the way in which the system functions. Here a protocol can be viewed as an institutionalised pattern of interaction. Viewing interactions in this way means that attention is focused on the essential nature and purpose of the interaction, rather than on the precise ordering of particular message exchanges.
A protocol definition (Figure 2) consists of the following attributes:
the protocol definition
Figure 2. The protocol definition.
After analysis stage must be fully elaborated roles model, which documents the key roles occurring in the system, their permissions and responsibilities, together with the protocols and activities in which they participate. The schema of of a role on which the analysis should be executed is resulted on Figure 3.
role shema
Figure 3. Role schema
The aim of a classical design process is to transform the abstract models derived during the analysis stage into models at a sufficiently low level of abstraction that they can be easily implemented.This is not the case with agent-oriented design, however. Rather, the aim in Gaia is to transform the analysis models into a sufficiently low level of abstraction that traditional design techniques may be applied in order to implement agents.
The Gaia design process involves generating three models:
The purpose of master's work is realization of the teaching system, which basis on application of agent-oriented technologies for analysis and design of automated systems. The structure of the this system is resulted on Figure 4.
structure of system
Figure 4. Structure MAC for the teaching of the student on discipline
On analysis stage in the projected teaching system the basic roles have been allocated.Each role was defined by four attributes: responsibilities, permissions, activities, and protocols and has been described according to above mentioned shema.As a result of an analysis stage have been received a roles model and interactions model. Then, at a design stage, roles were concretized in agent types.
Using of multiagent teaching systems in educational process possesses the following advantages:
Efficiency of application of technology multi-agent systems in the educational purposes is conclusive. The agent-oriented approach allows to realize most full an individual approach to the student. Similar agents, being in different places of a network, form distributed multi-agent system of an artificial intellect. The collective intelligence such «community» of program robots, at observance of the certain criteria of hisorganization, noticeably surpasses intellectual power of each agent and is not equal to the simple sum of intelligence of all agents included in community. Multi-agent the system of an artificial intellect is the major means of training in conditions of an information society and at a transitive stage to it.

The list of references:

[1]
Мильнер Б.З. Управление знаниями. – М.: ИНФРА-М, 2003.-178 с.
[2]
Тельнов Ю.Ф. Интеллектуальные информационные системы в экономике. – М.: СИНТЕГ, 2002. –316 с.
[3]
Трахтенгерц Э.А. Компьютерная поддержка принятия решений. – М.: СИНТЕГ, 1998. – 376 с.
[4]
Wooldridge M.,Jennings N.,Kinny D. The Gaia-methodology for agent-oriented analysis and design, published in book: Autonomous Agents and Multi-Agent Systems,3,285-312,2000
[5]
Zambonelli F.,Jennings N.,Omicini A.,Wooldridge M. Agent-orinted software engineering for Internet applications, published in book:Coordination of Internet agents:Models,Technologies and Applications,A.Omicini,F.Zambonelli,M.Klusch,R.Tolksdorf, Springer,2000
[6]
Franklin S.,Graesser A. Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents,Proceedings of the Third International Workshop on Agent Theories, Architectures, and Languages, Springer-Verlag, 1996.
[7]
Zambonelli F.,Jennings N.,Wooldridge M. Developing Multiagent Systems: The Gaia Methodology,ACM Transactions on Software Engineering and Methodology, Vol. 12, No. 3, July 2003, Pages 317–370.

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