INTELLIGENT USER INTERFACE IN FUZZY ENVIRONMENT

Transcription

1 INTELLIGENT SE INTEFACE IN FZZY ENVIONMENT Ben Khayut 1, Lina Fabri 2 and Maya Abuhana 3 1 Department of &D, IDTS at Inteigence Decisions Technoogies Systems, Ashdod, Israe ben_hi@hotmai.com 2 Department of &D, IDTS at Inteigence Decisions Technoogies Systems, Ashdod, Israe ina.fabri@gmai.com 3 Department of &D, IDTS at Inteigence Decisions Technoogies Systems, Ashdod, Israe maya.h111@gmai.com ABSTACT Human-Computer Interaction with the traditiona ser Interface is done using a specified in advance script diaog menu, mainy based on human inteect and unproductive use of navigation. This approach doesn t ead to maing uaitative decision in contro systems, where the situations and processes cannot be structured in advance. Any dynamic changes in the controed business process (as exampe, in organizationa unit of the information fuzzy contro system) mae it necessary to modify the script diaogue in ser Interface. This circumstance eads to a redesign of the components of the ser Interface and of the entire contro system. In the Inteigent ser Interface, where the diaog situations are unnown in advance, fuzzy structured and artificia inteigence is crucia, the redesign described above is impossibe. To sove this and other probems, we propose the data, information and nowedge based technoogy of Smart/ Inteigent ser Interface (II) design, which interacts with users and systems in natura and other anguages, utiizing the principes of Situationa Contro and Fuzzy Logic theories, Artificia Inteigence, Linguistics, Knowedge Base technoogies and others. The proposed technoogy of II design is defined by muti-agents of a) Situationa Contro and of data, information and nowedge, b) modeing of Fuzzy Logic Inference, c) Generaization, epresentation and Expanation of nowedge, c) Panning and Decision-maing, d) Diaog Contro, e) easoning and Systems Thining, f) Fuzzy Contro of organizationa unit in rea-time, fuzzy conditions, heterogeneous domains, and g) mutiingua communication under uncertainty and in Fuzzy Environment. KEYWODS Inteigent user interface 1. INTODCTION 1.1. Anaysis Data, information and nowedge are one of the main components in the word deveopment. Their correct use eads to the adoption of reevant decisions and effective diaog contro through the Inteigent Muti-agent ser Interface (IMAI) in Fuzzy Contro System (FCS). The traditiona ser Interface (I) is using a Menu principe to interact with their users (anaysts, experts, managers). Each of them spends an unproductive time to navigate the menu diaog instead of soving their day to day tass. They can be more productive in interacting with the I in more natura and ess predefined way, where the I itsef shoud find and process the reuired data, information or nowedge to support their tass. Changes in business process of

2 organizationa unit entai changes in the script menu diaog. This eads to a permanent and continuous redesign of the I. In these circumstances, since the technoogy doesn t support the business process need, it is impossibe to use the I in rea-time. To avoid such a discrepancy it becomes necessary to use the principe of Situationa Contro [6] in the design of II. Given the uncertainty of the environment, as we as increased compexity of business process and technoogies, the use of Fuzzy Logic [1] and Artificia Inteigence becomes necessary. A muti-ingua Processor (LP) shoud be used to support Human-Computer Interaction (HCI) to incorporate a business process into contro system. In order to ensure functionaity of the II in fuzzy environment in the paper it is suggested to use of Fuzzy Logic Inference [13], Generaization and Expanation of nowedge [15], Diaog Contro [11] and other methods. Obviousy, the considered smart/ inteigent II shoud be abe to support a variety of subject areas, natura and other anguages. The soution of these tass in this artice is focused on the systematic and inguistic approach of modeing, panning and controing of inguistic and subject area data, information, nowedge, fuzzy ogic inference, diaog contro and others, by mapping the objectives and constraints in fuzzy environment. The novety of the technoogy which designs IMAI consists of: Modeing and situationa fuzzy contro of data, information and nowedge for impementing an automatic fuzzy inference and finding a correct, accurate, timey and adeuate decision, taing into account a current situation and impact of fuzzy environment. sing of resuting decision, criteria and purpose for providing of modeing, panning and contro of the business process in the fuzzy environment. Converting and deriving images, concepts, meanings from natura anguages in various subject areas and seriaizing them into the bases of data, information and nowedge. se of these bases for muti-ingua human - machine communication using methods of diaog contro, generaization and expanation of nowedge in the inteigent fuzzy contro system. se of properties of a) atomicity of data, b) reationa reationships of information, c) figurativeness and associative connectedness of nowedge for their integration and aggregation. sing methods of wisdom, intuition, behaviour and others to obtain decision of high uaity and precision. The anaysis of the state of scientific research in the fied of design II showed that the directions and methods of impementation are reated mainy to the functionaity of the agents, controed by them. In this context, we wi hod a brief of comparative anaysis of the functionaity of agents of the II, offered by us and other authors. In [12] is given an interesting overview of the approach to the probem of Fuzzy Contro. Our approach differs from the mentioned, the fact that in addition to proposed methods of formaization we taing into account the principes of situationa contro, artificia inteigence and others. This aows reaizing fuzzy contro in situation, which unnown in advance. At the same time, we have deveoped modeing techniues [17] based on the managed data, information and nowedge [10] that aows finding reevant soution with the desired accuracy in the circumstances. This accuracy is impemented using inteigent agents of anaysis, decision-maing, panning and others [22] by using the vaues of fuzzy membership function. In [18] is represented inguistic approach for soving decision probems under inguistic information using Muti-criteria decision maing, inguistic modeing, aggregation and inguistic choice functions methods on base of ran ordering among of the aternatives for choosing the best of them. The main difference between our systematic approach and the proposa is a: Generaized notion of inguistic variabe of Fuzzy Logic, by which we evauate and tae into account not ony the morphoogica, syntactic and semantic, but aso, behavioura, psychoogica and other aspects of the terms (atomic units) of Natura Language (NL).

3 Situationa Fuzzy Contro in Fuzzy environment, by which we contro not ony information, but aso data, nowedge, decisions, agents and others. Decision-maing process is based not ony on using the ran for estimation of the aternatives, but aso on automatic Fuzzy Logic Inference, Panning, Contro of aternatives, situations and other units. Muti-ingua interaction, generaization, expanation, seriaization, storage and actuaization of nowedge in fuzzy conditions, heterogeneous subject areas, where the situations are unnown in advance, fuzzy structured and not ceary reguated. In [20] are considered adjustabe autonomous agents that possess partia nowedge about the environment. In a compex environment and unpredictabe situations these agents are ased the hep of human on base of the mode, caed HHP-MDP (Human Hep Provider MDP) and reuests, which are set in advance. The comparative anaysis of these and other wors, associated with our wor, showed, that there is no integrated, systemic and inguistic approach to the probem of situationa fuzzy contro in a fuzzy environment, incuding the techniues of situationa contro of fuzzy data, information and nowedge, modeing, panning, decision-maing, diaog contro and situationa fuzzy contro of the organizationa unit, based on the achievements of Fuzzy Logic, Situationa Contro, Artificia Inteigence, Linguistics, Knowedge base technoogies and others. In this artice, we present the resuts of our studies and the approach to the design of IMAI using our deveoped methods and toos Terminoogy Data is organized in the memory and are perceived by the person or machine as facts, numbers, words, symbos, ines and other items of information. They are not reated to each other and are found in texts, pictures and other maps of reaity. Information is a group of reated data, organized in the memory that respond to the uestions of "who", "what", "where", "when" and others. Knowedge is the image or domain mode, extracted from information and organized in memory, which in itsef are interpreted, structured, ined, associated, transformed, compared, upgraded, activated, anayzed, deduced, buit, seriaized and so on, in rea time. The mentioned image or domain shoud respond to uestions "why" and "how", consider the impact of environment and specificity of subject area, satisfying the criteria and purpose of existence. Wisdom is a method of perceiving reaity and achieving a uniue soution (answer) on the basis of inteigence, archiva nowedge (experience), principes and inference in a certain situation. Intuition is a method of perceiving reaity and a achieving a uniue soution (answer) on the basis of inteigence, archiva nowedge (experience) principes and uniue inference in an extreme situation. Modeing decisions is defined as construction of a new conceptua situation and a state of controed units (fuzzy data, information, nowedge, inference and others), which meets the criteria and purposes of the information system in fuzzy environment. The purposes are functions of the information system. Panning decisions is defined as a use of modeing resuts to create a seuence of aternative decisions that wi match to the situation and the state of information system in the subseuent stages of management of the organizationa unit. Decision-maing is defined as a process of modeing fuzzy ogic inference [13] for seection the reevant decision from imited number of aternative decisions, obtained during the panning decisions. easoning is a method (process) of maing inferences from body (premises) of data, information and nowedge. Fuzzy Contro is the process of using the modeing resuts of panning and decision-maing in fuzzy environment, in order to impement a contro action on the units (data, information,

4 nowedge, decisions, organizationa unit and others) to shift them and their contro system to a new state that matches a specified criterion. nder the fuzzy ogic inference [13] we mean procedure for determining the vector of interna i m and externa output fuzzy variabes b V using a new vector of the vaues of the input fuzzy i m variabes a, which transforms the IMAI in its new state. This procedure is impemented on the extensiona, intentiona and reformative eves of modeing nowedge [14]. nder the Diaog Contro we means the process of presenting partners (II-Human or II-II) of common commands (uestions) to each other and providing by them targeted actions (issuing repies) reevant to the subject of the diaogue and the situations in which it occurs. The process aows using the natura and\ or other anguages). Inteigent fuzzy contro system is understood as a nowedge based system, which is reiaby eectronic autonomous system, and which a) operates at a high-eve operating system b) connected to the Internet, d) executes a native or coud-based appications, e) anayzes the coected data, information and nowedge, and e) reaizes the human-machine functions for soving probems in fuzzy environment. Traditiona I is an interface, which is woring on base of "menu scenarios and is not autonomous. The II is understood by us as nowedge based system, which contros the inteigent functionaity of a agents in fuzzy contro system considering the situation and conditions of uncertainty and the impact of them surrounding Fuzzy Environment. According Wiipedia the Organizationa nit O (Figure 2, Figure 4) represents a singe organization with mutipe units (departments) within that organization. The business process is an activity or set of activities in organizationa unit O that wi reaizes a specific organizationa goa. Subject area understood by us as branch of nowedge and technoogies, where the organizationa units are functioning. Environment is the surrounding reaity, consisting of organizationa units, information systems, robots, agents, agent systems and so on, which interact with each other under the infuence of the environment. Environments Subject areas (Domains) Organizationa units (Agents) II and I Figure 1. II and I environments Figure 1 depicts nesting of the above-defined concepts. A muti-agent system (MAS) [19] is a computationa system where agents cooperate or compete with others to achieve some individua or coective tas.

5 Agent is a rea-word or artificia entity, which is a person (in the first case) and an object (in the second case), and which are capabe of performing some action or service or otherwise, interacting with other entities. Thus, the above defined and impemented in the computer concepts are agents. Combining, nesting and integrating the agents into the groups according to their objectives and functiona features turn them into an Inteigent Muti-agent System in the paper. Fuzzy Contro provides a forma methodoogy for representing, manipuating, and impementing a human s heuristic nowedge about how to contro a system [12]. According Wiipedia the Fuzzy Contro System is a contro system based on fuzzy ogic a mathematica system that anayzes anaog input vaues in terms of ogica variabes that tae on conditions vaues between 0 and 1, in contrast to cassica or digita ogic, which operates on discrete vaues of either 1 or 0 (true or fase, respectivey). 2. CONTENT 2.1. Conception The main purpose of the II function is to faciitate the user to perform anaysis, panning, decision-maing, management and coordination of organizationa unit. nder the faciitating in II is understood the fu use of inteigence of the system to perform these and other functions by interacting with it in natura and/ or other anguage. In this regard, the function of the II shoud be based on the perception, processing and synthesizing nowedge in rea time. Given the fact that these processes occur in unexpected situations and Fuzzy Environment (peope, robots, nature, space, hardware, software, other information and agent systems, and so on) becomes necessary to use the theories Situationa Contro [6], Fuzzy Logic [1] and other methods. x, w Fuzzy Environment Organizationa nits Situationa Inteigent Muti-agent Contro System Traditiona Contro System AppSys HP Menu scenarios for diaog Commands Queries Scripts OS / DBMS / KBMS Hardware. LP MEN I ControSys.. LP II Inteigent Agents NL NP Figure 2. Human Interaction using Smart/ Inteigent and Traditiona I. The paper proposes the creation technoogy of the IMAI that integrates the achievements of Situationa Contro, Fuzzy Logic, Linguistics, Artificia Inteigence and others for reaization of the foowing functionaities: To use of modes of representation of inguistic and subject area data, information and nowedge in Fuzzy Environment.

6 Situationa fuzzy Modeing, Decision-maing, Contro and Panning in conditions of the absence, incompeteness, vagueness and ambiguity of nowedge. Fuzzy Logic Inference. Generaization and Expanation of nowedge. Fuzzy Diaog Contro. easoning and Systems Thining. Linguistic Processor for Muti-ingua interaction in Fuzzy Environment. The main features of the proposed technoogy of design IMAI ies in the use of methods: Situationa contro of data, information and nowedge and impementation by it of automatic processes of inference, maing the right decisions, generaization and expanation nowedge, diaog contro, panning and management of organizationa unit. Creation LP, which is a part of the II and enabing peope to interact with him in Natura Language without "menu" diaog. The group of highy professiona users (HP) (Figure 2) incudes users, which were trained in the use and maintenance of hardware, software, networs, data, information and nowedge bases. A group of ess professiona users (LP) in (the mentioned above resources) are deveopers: system anaysts, appication programmers, testers, operators and others. The group of not professiona users (NP) is composed of experts in their fied, who use the functionaity of information system to soving their functiona tass. This group incudes the decision maers, anaysts, experts, consutants, managers and other experts in their subject area. MEN I is the user interface of diaogue menu in Traditiona Information System, with whom interacts user LP using script diaogue anguage. This user aso interacts with OS / DBMS / KBMS interfacesi, using the uery anguage. OS / DBMS / KBMS I is a group of I, used by users HP for system support the Operation Systems (OS), Data Base Management Systems (DBMS) and Knowedge Base Management Systems (KBMS) using commands anguages. AppSys ControSys, are respectivey, Appication and Contro systems, which reaize the functionaities of the considered Traditiona Contro System and other systems in it. LP II is II with buit in LP and with whom interacts the user NP using natura anguage. OS / DBMS / KBMS MEN AppSys This interface is connected with interfacesi, I and with systems, ControSys for the use of the existing functionaities of other information systems. Thus, IMAI integrates the functionaity of existing traditiona I and contro systems to sove simpe probems and its inteectua capabiities to sove compex probems in fuzzy environment. Hardware are hardware resources, supported by user HP. LP The interface II interacts with the organizationa entities, performing a functionaity of FCS by returning to user NP the resuts in the reuired form. Simiar wor is done by the user LP LP, which interacts with the organizationa units through the interface II. Inteectuaization of abor of the LP and HP users is not considered in this paper and wi be the subject of further study. These studies invove the introduction of artificia inteigence in hardware, software and networs, and the incusion of these groups in the NP group Methods The modes and methods of representation of inguistic and subject data, information and nowedge in Fuzzy Environment. For storing and representation of subject and inguistic data, information and nowedge we integrated, generaized and used the anguage features of X-codes, syntagmatic chains, semantic networs, and universa semantic code and frames anguages [21]. These anguages

7 are used to estabish conformity between reationa eve of nowedge representation and ogicpithy eve of representation of data, as we as their in a formaized genera presentation of nowedge using procedures of pithy information processing. We described the apparatus for constructing these modes and estabish this conformity on decarative, procedura and transformative eves of representation data, information and nowedge in the form of aggregated and integrated semantic networ of computing frames [10]. In [10], [13] were extended the concept of a inguistic variabe, forma and semiotic modes with using the principe and method of situationa contro [6] by taing into account of the accepted methods of representation, organization, integration, processing and synthesis of data, information and nowedge [3-5], [7,8], [14]. Through the use of fuzzy sets theory and situationa contro mode were defined inguistic and thematic units, attributes, corteges and dictionary entries in inguistic and thematic reationa bases of data, information and nowedge. These modes define a conceptua means of presenting and structuring of data, information, nowedge in fuzzy environment, and are aso used for modeing, panning, decision-maing and fuzzy contro [22] in II. Thus, the inteectuaity of the Data, Information and Knowedge Contro System consists in providing of interaction of decision-maer with consutants and experts (ast among themseves) in order to organize diaogue between them in a natura anguage using II Situationa Fuzzy Modeing, Decision-maing, Contro and Panning in conditions of the absence, incompeteness, vagueness and ambiguity of nowedge. The feature of soving the probem of modeing nowedge is reated to the construction of mutiingua inguistic processor, modeing of fuzzy ogic inference, generaization and expanation of nowedge, and reasoning using the methods of situationa data, information and nowedge contro in the particuar domain of organizationa contro. For this purpose: a) deveoped fuzzy mode of modeing, representation and synthesis of nowedge, b) generaized the notion of inguistic variabe of fuzzy ogic, c) formaized and constructed semiotic mode of situationa contro of thematic and inguistic nowedge, and d) deveoped a method of modeing nowedge, based on buit semantic networ of computationa frames. The principe of the method of modeing nowedge [14] is consists in processing of aggregate semantic networ of concepts using nowedge units, where each generaized inguistic variabe is modeed in a particuar segment of accumuation of hypothetica poyhedron through the dictionary entry and cortege in the thematic and inguistic nowedge bases. The vertices of said poyhedron are considered as a resut of integration, aggregation and meaningfu interpretation of the various points of view on a particuar aspect of the rea word, expressed on natura anguage. In this case, the nowedge modeing processes are impemented on natura anguage and machine eves by switching from one to another (i.e. from verba to numeric ratings) is being reaized on base of the mode of situationa nowedge contro using of nowedge modues and LP. In order to contro an organizationa unit it is reuired to now its structure, the purpose of its existence and its contro criteria [6]. The tas becomes more compicated when there is a need to contro organizationa units in rea time, in situations unexpected in advance, using variety of natura anguages and subject areas. In these circumstances, arises a probem of decision maing in fuzzy environment [2] based on the data, information and nowedge.

8 The soution to this probem is impemented by a) methods of modeing, panning and controing of inguistic and subject area data, information, nowedge [10], [22], fuzzy inference [13], decision maing, reasoning and others, b) mapping the objectives and constraints in fuzzy environment [17]. The methods of Fuzzy Modeing, Panning, and Decision-maing were described in [22] Fuzzy Logic Inference Given the compex character of functioning of the II, its design is impossibe without the use of theories situationa contro [6], fuzzy sets [1] and the proposed above of modes of representation, synthesis, modeing, panning and management of data, information and nowedge. Therefore, the modeing method of fuzzy inference can be appied to data contro system, satisfying the foowing principes [13], [10]: A information about the data, information and nowedge (about the organizationa unit) may be communicated to the contro system as a set of phrases of Natura Language. Contro mode is fundamentay shoud be open and never ends the creation of the fina forma mode. Description of the data management (information, nowedge) process is possibe in the form of natura phrases and \ or another anguage. In these circumstances, the proposed modeing method of fuzzy inference is impemented by a system of situationa data contro and dispays a inguistic approach to the probem. The method aows reaizing the inductive and deductive inference in natura anguage in integrated subject areas, based on incoming fuzzy fragments (parces) of the anguage. To do this, we used the heuristic agorithms, methods of wisdom, intuition, behaviour and other agorithms and methods that invoe the modues of modeing of data, information and nowedge. The agorithms and methods uses generaized inguistic variabes, fuzzy sets, rues and facts (situations), previous decisions and their subsets (segments), extracted from bases of data, information and nowedge to obtain reevant decision. The description of the fuzzy ogic inference is given in [13] and is understood as a process for i m determining the vector of interna and externa output fuzzy variabesb V using a new i m vector of the vaues of the input fuzzy variabes a, which transforms the system in its new state by means of fuzzy matching in a fuzzy modeing reation, through compositiona mapping rues, objectives, constraints, associations and measures of opportunities. This process is impemented by means of modeing nowedge [14] and is reaized as a procedure of X j commutative mapping of verba and numerica vaues of the ogic functions ( i, i, ) and the reationship (Figure 3). X i j,, i j,, in a fuzzy reationship of modeing, presented in [6], [13] by the diagram B Lij,, j a A f T f ' A f b State of the environment B Q f B ' State of the mode Figure 3. Diagram of a fuzzy reationship of modeing.

9 f The a, functions B Lij ( A, B, f T, f b are respectivey maps, defined by matrices of verba and numerica estimates of X i j,, X j i, j, ) and reationship i,, i, j,. ' A - are states of environment. ' B - are modes associated with these states in the environment. f Q - are respectivey transformations in environment and in mode of situationa contro. f - are frames L ij of modeing. m nder the fuzzy matching we mean the frame : V that dispays the matching of base fuzzy sets rues B Lij L ij m and V in the procedure of inference, through the compositiona mapping ( X ) ( X ) (1), where: A Lij L ij (X ), (X ) are respectivey, the resutant and initia membership functions in the B Lij A Lij considered generaized fuzzy reation ; X { x ij } is a vector of the discrete domain of definition; o is sign of the compositiona mapping; L ij - is fuzzy matching in the procedure of inference; A Lij, B Lij are heterogeneous muti-dimensiona fuzzy sets; L ij L ij - are heterogeneous distributive attices of measurement intervas of the domains of considered membership functions; defines eves of representation of the data, information and nowedge, respectivey on X codes, niversa Semantic Code and Semantic Frames [21] eves. i,, m = (1, n). Then B Lij (X ) = ( (X ) ) pp K A Lij ji ' ( aijm / am Poss ) (2) defines the mentioned above compositiona mapping rues of the fuzzy inference by using ' measures of opportunities Poss( a ijm / a ) on eves of modeing data, information and nowedge. m ' It shoud be note that Poss ( a ijm / a ) defines the measure of the possibiity, that the composite concept in the externa representation is characterized by its vaues the interna representation. m a ijm of a fuzzy set A Lij in

10 Generaization and Expanation of nowedge The tas of generaization of nowedge is reduced to finding the target (uniue) situation of data, information and nowedge by using of their current situation j Q Q and process of contro them on base of mode [15]. The decision, correspond to the found situation of the data, information and nowedge, shifts them from the current situation Q into a newq. j This decision (action) determines the impact rues I on data (information and nowedge), which must be met in the overa situation S, so that they and contro system woud correspond i to the new (changed) situation Q. The target function in the mode of generaization and expanation of nowedge defines the purpose of contro of data, information and nowedge. The purpose may be the stirring up of processes of modeing, decision-maing, panning, contro, generaization and expanation nowedge and other. The process of generaization is seen as a search for the objective function (situation) by modeing the nowedge and fuzzy inference using the semantic structure of data, information and nowedge. The concepts are interpreted as a group of exica units of a anguage (of generaized inguistic variabes, economic indicators) designed to refer to the facts, phenomena and of eements using of dissimiar verba and numerica estimates ( - cuts) on the interna and externa eves of representations by humans and computers. The formaized mode of generaization and expanation of nowedge (data, information) in the II in [22] is represented by ratio x, u, w N T A, K, ( ),( S : Q Q : I) (3), where: i j N A is a mode of reevant decision, received from a arge number of aternatives A, which contains inguistic variabes (generaized situations); K is a forma system of data, information and nowedge modeing (generaized nowedge modue), which is woring on base of rues and reations in the segments of semantic networ of computationa frames; T and are, respectivey, the vaues (estimates) of fuzzy ogic functions in fuzzy nowedge contro, and generaization and expanation processes; According of [6], [13] the S x, u, w i : Q j Q : I determines the eementary act of contro in the processes of modeing and seecting the reevant decision, that transforms the contro system in the new situation S i, which characterizes its new state Q after the state Q j was shifted toq. The process of expanation nowedge is reaized as inversed process of generaization nowedge.

11 Diaog Contro In this paper, we propose an approach to the contro of the diaogue [11] in II using the modeing and contro data, information and nowedge [10], fuzzy inference [13], generaization and expanation nowedge [15], easoning and Systems thining and others. Together with these contro systems, Diaogue is controed by panning system, which uses D the mode, created by interpreter of diaogue (Figure 4) and sub systems of fuzzy inference I, generation and expanation of nowedge G Z, reasoning and systems thining. Those sub systems (agents) provide processing and forming the input and output messages of natura anguage in nowedge management system using bases of subject area and inguistic data, information and nowedge. L L D I The II is an II, witch contains the LP. Together with the Manager the II,,, D Z A, are representing the Decision-maing system. The (anayst, reviewer) and E (expert, approver) are inteigent subsystems, which are support the decision-maing process. Fuzzy Environment x,w I O u D NL L II G E A NP Z (M,x,w) Figure 4. Diaog Contro in human interaction. The tas of diaog contro is formaized in [22] by the ratio x, u, w N D T A, K, ( ( )),( S : Q Q : I) where D i j T =f ( ( ), u, x, w) - is a fuzzy ogic function, which depends on the other T mentioned ogica functions, and managed of environmenta infuences u, x, w on the organizationa unit with the target to mae reevant decisions on contro, respectivey, at the intentiona, extensiona and transformative eves of their modeing easoning and Systems Thining The essence of the methods of easoning and Systems Thining consists in extracting of reevant data, information and nowedge from texts (speech) in different natura anguages, within dissimiar subject areas to impement situationa fuzzy contro of data, information and nowedge with a view of representing them in nowedge bases and organizing on basis of them processes of reasoning and thining under uncertainty. On the basis of thining are formed, associated concepts, which are reated them with the appropriate symbos of anguage (4),

12 in a particuar subject area. Taing into account the scheme of reasoning and its modeing suggested by [16] we adhere to the concept, where the main objective of nowedge representation is to promote the modeing process of Fuzzy Logic Inference using methods of fuzzy contro and actuaization of data, information, nowedge in nowedge bases. Thus, the easoning is considered in this paper as a process of gaining new nowedge and parces on basis of the mentioned a) ogic inference process, b) generaization and expanation nowedge, and c) diaog contro, which were situationa fuzzy controed by the processes of fuzzy modeing and contro of data, information and nowedge, according the objective under uncertainty in Fuzzy Environment. The formaized tas of easoning and Systems Thining can be presented as x, u, w N Z D T A, K, ( ( ( ))),( S : Q Q : I) (5). i j In this case, Z D T ( x) ( ( x) ( x) ( x) Poss( a / a ) is a composite rue of reasoning and Z thining in IMAI, where the rue (x) is interpreted as a desired fuzzy ogic function, which identifies the disjunction of conjunctions of moduated assessments of vaues of characteristic D fuzzy ogic functions (x), T (x), (x) and conformity measure Poss ( a / a ) Linguistic Processor for Muti-ingua interaction in Fuzzy Environment The users interact with II using natura anguage, which is processed by LP. The LP contains the Interpreter and Synthesizer agents. Their impementation invoves the functionaity [3, 5] of: Providing meaningfu machine transation for identification concepts. The adeuacy of the mapping meanings of the concepts expressed by the term in a particuar anguage in the context of a particuar subject area. Opportunity to endow a specific term by attributes of grammar, ogic, semantics, pragmatics, psychoogy and others in accordance with its meaning in a particuar subject area and the context of use. Impementation of the "understanding" by means the agorithms of synthesis output expressions of natura anguage based on ogica-semantic characteristics of intrainguistic representations of meaning. esoution disambiguation expressions used anguages by samping exica and semantic characteristics and vocabuary of anguages and conduct on the basis of their exica and semantic anaysis to determine the semantic matching of input and output anguage euivaents. The possibiity of impementing a system of automatic machine dictionaries and of thesauri in inguistic databases with conceptua connection to the subject databases and nowedge. Impementation of Machine Transation terminoogica phrases incudes the bocs of: Logica-semantic, grammatica anaysis of input combinations and the identification of concepts of its output euivaents. Prior authorization of exica ambiguity on input phrases using conceptua codes. Extraction of grammatica information. Grammatica ambiguity resoution based on the stems of input combinations. Extracting ogica information. Fina resoution of exica ambiguity. esoution of exica and grammatica ambiguity of input and output combinations of stems. Logica and semantic disambiguation of input and output terminoogica expressions.

13 Identification of concepts and the formation of natura anguage expressions of output terminoogica phrases. The LP reaizes transformation target s actions, which are expressed in NL and other anguages. The method of reaization of the LP is represented in [3]. 3. CONCLSIONS The proposed methods and technoogy are oriented for design and deveopment of autonomous Smart/ Inteigent II, which wi be operated in fuzzy environment, interacting with peope and other systems and agents in different anguages and dissimiar subject areas, where the situations and factors of infuence on the II, contro system and organizationa unit cannot be determined and structured in advance. A distinctive feature of this approach is to use an II to hep users to impement modeing and contro of fuzzy inguistic and subject data, information, nowedge, aternative soutions, objectives and constraints in order to find accurate, reevant and right decisions, which are suitabe to the situation in Inteigent Fuzzy Contro systems. The resuts of this wor focused on the creation muti-agent II for mutiingua interaction in autonomous, situationa, inteigent, muti-agent information contro systems of robots, unmanned production and spacecrafts and others, functioning in the fuzzy environment and unforeseen situations in advance. EFEENCES [1] L. A. Zadeh, (1970) Fuzzy Sets, Information and Contro, 8, New Yor, pp [2]. E. Beman & L. A. Zadeh, (1970) Decision-maing in fuzzy environment, Management Sciences, Caifornia, vo. 14, pp [3] B. Z. Khayut, G. I. Nioaev, A. N. Popesu, V. A. Chigaovsy, (1983 ) eaization Linguistic Data Base for Machine transation foreign anguages texts using DBMS INES, Coection of papers, Internationa Machine Transation Symposium, Moscow, pp [4] B. Z. Khayut & G. I. Nioaev, (1984 ) Data organization for interactive systems understanding of natura anguage and mutiingua interaction, Worshop, Interactive systems and their practica appication, Chisinau, pp [5] G. I. Nioaev & B. Z. Khayut, (1985 ) Logica-semantic method of identifying noun phrases of natura anguage, Worshop, Logic and combinatoria methods in artificia inteigence and pattern recognition, Chisinau, pp [6] D. A. Pospeov, (1986 ) Situationa Contro, Science, Moscow, 288 pages. [7] B. Z. Khayut, (1986 ) An approach to the integration of heterogeneous databases in design of inteigent access, Worshop, Interactive systems and their practica appication, ussia, Kainin, pp [8] B. Z. Khayut, (1986 ) The method of constructing an integrated system of inteigent access and data contro, Worshop, Methods for buiding integrated software systems of data contro, ussia, Kainin, pp [9] G. I. Nioaev, Y. N. Pechersy, B. Z. Khayut, (1986) The conceptia approach to the presentation and synthesis of nowedge about natura anguage and subject area for the maninteigent robots, Worshop, Automation and robotizing of manufacture with the use of microprocessor-based producrs, Chisinau, pp [10] B. Z. Khayut & Y. N. Pechersy, (1987) Situationa Data Contro, VINITI, Deposited manuscript, Moscow, 29 pages.

14 [11] Y. N. Pechersy & B. Z. Khayut, (1988 ) Approach to Diaog Contro in the situationa decision-maing system, Worshop, Interactive toos in automated contro systems, Chisinau, pp [12] K. M. Passino & S. Yurovich, (1988 ) Fuzzy Contro, Ohio State niversity, Addison- Wesey, Meno Par, Caifornia, pages 502. [13] B. Z. Khayut, (1989 ) Modeing of fuzzy ogic inference in the decision maing system, Science, Issue 110, Academy of Sciences of Modova, Chisinau, pp [14] B. Z. Khayut & Y. N. Pechersy, (1989 ) Knowedge modeing in decision support system VINITI, Deposited manuscript, Moscow, 12 pages. [15] Y. N. Pechersy & B. Z. Khayut, (1989 ) Generaization of nowedge in decision support system, Worshop, Questions of deveopment computer faciities, Chisinau, pp [16] D.A. Pospeov, (1989), Modeing of easoning, Moscow: adio and Communication, 184 pages. [17] B. Z. Khayut, (2000) The method of modeing and decision-maing using fuzzy data, fourth coection of scientific papers, Creative searches of scientific Israe today, Asheon, pp [18] F. Herrera & E. Herrera-Viedma, (2000) Linguistic decision anaysis: steps for soving decision probems under inguistic information, Esevior Science, Fuzzy sets and systems, vo. 115, Issue 1, pp [19] J. W. Krupansy, (2005 ) Advancing the Science of Software Agent Technoogy, Agtivity. [20] N. Cote, A. Canu, M. Boused, A, Mouaddib, (2012) Humans-obots Siding Coaboration Contro in Compex Environments with Adjustabe Autonomy, Web Inteigence and Inteigent Agent Technoogy (WI-IAT), IEEE/WIC/ACM Internationa Conferences, vo. 2, pp [21] V.I. aov, (2012), System Anaysis (initia concept), Lecture Knowedge representation anguages (ogica and reationa anguages), Moscow: adio and Communication. [22] B.Z. Khayut, L. Fabri, M. Abuhana, (2014), Modeing, Panning, Decision-maing and Contro in Fuzzy Environment, Advance Trends in Soft Computing Studies in Fuzziness and Soft Computing Voume 312, TX, SA Antonio: Springer, pp

15 Authors Ben Khayut graduated MSc in Mathematics at the niversity of Chisinau and PhD in Mathematica Cybernetics (competed studies) at the Institute of Mathematics of the Academy of Sciences of the Modova in the fied of appied mathematics, computer science, artificia inteigence, fuzzy ogic, where he defended the pre-thesis "Data Contro in decision-maing on natura anguage. Has experience (over 30 years) in design and deveopment of Information Contro systems in industry using inguistic and subject area data, information and nowedge bases, and agorithms of machine transation as researcher, designer, programmer, project manager, using modern scientific methods, software and hardware. Have 25 scientific papers. In 2007 he founded the Inteigence Decisions Technoogies Systems (IDTS). Lina Fabri graduated Bacheor, Economics and Management at the Coege of Business Management. Have 14 years of successfuy eading and executing Business Inteigence and Technoogy strategies and soutions to drive business vaue. Proven abiity to define and drive technoogy soutions and services with soid trac record of high performing standards and exceence. Has good scientific abiity and experience in &D of Information Contro Knowedge based Systems, Panning and Decision-maing using business strategy methods in various subject areas using methods of Situationa Contro, Linguistics and Artificia Inteigence. Maya Abuhana graduated Information systems and Management at the Coege of Business Management. Have 9 Years of successfuy eading and executing Business Projects of Financia and Trading systems to drive business vaue. Proven abiity to define and drive technoogy soutions and services with soid trac record of high performing standards and exceence. Has a good scientific abiity and experience in &D in the design of nowedge bases, architecture and agorithms for compex financia contro systems, as we methods of human interaction using Linguistics and Database technoogies.