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��ࡱ�>��	�����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������[�	��W�bjbj����	��ΐΐ�X.#���������###T����Y#Y#Y#��#dU$,Y#~f�%�%�%�%�%�.�.�.0}2}2}2}2}2}2}$w����V}u#�.�.�.�.�.V}�%�%a	�}j7j7j7�.N��%#�%0}j7�.0}j7j7N�h��!bl�%����tyU��Y#J3N\j0}�}0~�jt���4Z��`ll���#�m\�.�.j7�.�.�.�.�.V}V}�5x�.�.�.~�.�.�.�.�����������������������������������������������������������������������.�.�.�.�.�.�.�.�.�	�:	An Analysis of Uncertain Representation of Knowledge in Artificial Intelligent and its Significant Role in Business Process Re-engineering Bechoo Lal, Research Scholar
Dept. of Computer Science & Engineering
JJT University, Rajasthan
India

Dr. Chandrahauns R. Chavan, I/c Professor-cum- Former Director , Alkesh Dinesh Modi Institute for  Finacial and Management Studies, University of Mumbai and 
Associate Professor and Former Director , Jamnalal Bajaj Institute of Management Studies, University of Mumbai
Abstract�In this paper the researchers are trying to optimize the analysis of uncertain representation of knowledge and its significant role in business process reengineering. The research study is based on the collection of data and statistical analysis report which tends to interpretation of uncertain representation of knowledge in artificial intelligent. The representation of uncertain knowledge requires attachment of an additional factor indicated the correctness of knowledge. This additional concept factor is called degree of belief. The value of this factor varies between 0 to 1.It can take any fractional value in this range. The degree of belief is the conceptual factor indicating the degree of correctness of analysis. The artificial intelligent provides the solution on the basis of facts and rules stored in the knowledge base. The objective of this research paper is to analyze the uncertain reasoning methods which are inductive, adductive and deductive and its significant role in business process reengineering.    
Keywords- KR, KBS, DAI, DSS, ES, AI
I.	INTRODUCTION
Knowledge representation (KR) is an area of artificial intelligence research aimed at representing knowledge in symbols to facilitate inference from those knowledge elements, creating new elements of knowledge. The KR can be made to be independent of the underlying knowledge model or knowledge base system (KBS) such as a semantic network.
Knowledge representation (KR) research involves analysis of how to reason accurately and effectively and how best to use a set of symbols to represent a set of facts within a knowledge domain. A symbol vocabulary and a system of logic are combined to enable inferences about elements in the KR to create new KR sentences. Logic is used to supply formal semantics of how reasoning functions should be applied to the symbols in the KR system. Logic is also used to define how operators can process and reshape the knowledge. Examples of operators and operations include negation, conjunction, adverbs, adjectives, quantifiers and modal operators. The logic is interpretation theory. These elements � symbols, operators, and interpretation theory � are what give sequences of symbols meaning within a KR.
A key parameter in choosing or creating a KR is its expressivity. The more expressive a KR, the easier and more compact it is to express a fact or element of knowledge within the semantics and grammar of that KR. However, more expressive languages are likely to require more complex logic and algorithms to construct equivalent inferences. A highly expressive KR is also less likely to be complete and consistent. Less expressive KRs may be both complete and consistent. Auto epistemic temporal modal logic is a highly expressive KR system, encompassing meaningful chunks of knowledge with brief, simple symbol sequences (sentences). Propositional logic is much less expressive but highly consistent and complete and can efficiently produce inferences with minimal algorithm complexity. Nonetheless, only the limitations of an underlying knowledge base affect the ease with which inferences may ultimately be made (once the appropriate KR has been found). This is because a knowledge set may be exported from a knowledge model or knowledge base system (KBS) into different KRs, with different degrees of expressiveness, completeness, and consistency. If a particular KR is inadequate in some way, that set of problematic KR elements may be transformed by importing them into a KBS, modified and operated on to eliminate the problematic elements or augmented with additional knowledge imported from other sources, and then exported into a different, more appropriate KR.
A good knowledge representation covers six basic characteristics:
Coverage, which means the KR, covers a breadth and depth of information. Without a wide coverage, the KR cannot determine anything or resolve ambiguities.
Understandable by humans KR is viewed as a natural language, so the logic should flow freely. It should support modularity and hierarchies of classes (Polar bears are bears, which are animals). It should also have simple primitives that combine in complex forms.
Consistency-If John closed the door, it can also be interpreted as the door was closed by John. By being consistent, the KR can eliminate redundant or conflicting knowledge.
Efficiency.
Easiness for modifying and updating.
Supports the intelligent activity which uses the knowledge base.
To gain a better understanding of why these characteristics represent a good knowledge representation, think about how an encyclopedia (e.g. Wikipedia) is structured. There are millions of articles (coverage), and they are sorted into categories, content types, and similar topics (understandable). It redirects different titles but same content to the same article (consistency). It is efficient, easy to add new pages or update existing ones, and allows users on their mobile phones and desktops to view its knowledge base [1]. 
II. REASONING WITH UNCERTAIN KNOWLEDGE
An uncertain situation requires representation of uncertain knowledge. There are three basic type of reasoning method are:
1. INDUCTIVE: Inductive reasoning is based on generalization of the previous experiences about the problem. It is very useful in most of the artificial intelligent and it is used in machine learning.
2. ABDUCTIVE: Abductive reasoning looks back through the chain of events to perform reasoning to find the out solution in case of uncertainty. This reasoning is not very reliable and it is very well possible for other events. Abduction can be used in the cases whether the knowledge is �incomplete�, with the given evidence it can provide the best guess.
3. DEDUCTIVE: Deduction works on the standards logic. It is kind of a explicit reasoning which consider the situation of the event. This situation is terms of logical representation can be represented as:
	�Switch (off)? Not lighting (bulb)�
In deductive reasoning the reverse is not true.
III. TECHNIQUE ADOPTED FOR UNCERTAIN REPRESENTAION OF KNOWLEDGE
The facts and rules are the certain and consistent which allows us to store useful information in the database. For certain situation, where knowledge base stores consistent information, all new knowledge that is added to it is bound to be consistent with the previous knowledge suh type reasoning is known as monotonic reasoning. In monotonic reasoning when ever some conclusion is drawn as true, it remains true under all circumstances we use this method in theorem proving. However, in real life all inferences do not necessary be considered correct under all circumstances.  A monotonic reasoning system, thus cannot work effectively in real life environment because of the following reason:
Data and knowledge stored in the knowledge base or the information available is always incomplete.
Inferences methods available are insufficient.
Circumstances change during the course of action for finding solution and hence, the solution.
Solution is based on preconceived beliefs and assumptions have to be made for minimizing time to arrive at the correct solution.
What does it mean in simple terms can be viewed as logical reasoning can be realistic presentation of real world. On the contrary, intelligent beings are required to make decision and function in a world full of uncertainty [2]. 
		IV.	RELATED WORKS
Bouzid, Maroua1 (2007) analyzed the distributed artificial intelligence (DAI) which is suitable for applications where there is no central control. One of these applications with which we are concerned is transportation scheduling. We noticed that all the approaches dedicated to this application use a weak representation of time and a simple reasoning. For this purpose, the system needs to address two issues: (1) determining the current location of the truck and its proximity to the departure and arrival of the new transportation task and (2) respecting the temporal constraints. [3]
Liu, John, Yun, David Y, Klein Gary (1990) proposed the Decision Support Systems (DSS) and Expert Systems (ES) are both aimed at improving decision making. Current DSS usually concentrate on quantitative models while ES emphasize logic and reasoning. In this paper, we review previous ES approaches used by DSS to handle decision models. We propose an extension to such systems, an Agent for Intelligent Model Management (AIMM), as an interface between DSS and the users. AIMM utilizes a combination of ES knowledge representations and reasoning mechanisms to make a wide variety of models available to decision makers so that they may apply these models without becoming involved in technical or procedural aspects of implementation .[4]
Da Xu, Li1, Pei, Xuemin1, Li, Huaizu1 (2003) presented a model for cases in the domain of managerial decision-making is proposed. The scenarios in narrative texts are converted to scenario units of knowledge organization. The elements and structure of the scenario unit are defined. The scenario units can be linked together or coupled with others. Compared with traditional case representation methods based on database tables or frames, the proposed model is able to represent knowledge in the domain of managerial decision-making at a much deeper level and provide much more support for case-based systems employed in business decision-making. [5]
 Baroni, Pietro,Guida, Giovanni, Giacomin,massimilian( 2007) analyzed the experience of Knowledge Engineering, which, starting from a specific problem which occurred during the development of ASTRA, a knowledge-based system for preventive diagnosis of power transformers, turned out to provide significant insights concerning modeling of uncertain knowledge. In particular, it was observed that there are (at least) two conceptually distinct types of uncertainty affecting knowledge, namely uncertainty about applicability (A-uncertainty, for short) and uncertainty about validity (V-uncertainty, for short), which are different in nature and play different roles in uncertain reasoning. The concepts of A- and V-uncertainty are applicable in any context where uncertainty affecting domain knowledge can be ascribed to two kinds of sources: on the one hand, the existence of exceptions, on the other hand, deep-rooted doubts about the foundations themselves of the relevant domain knowledge.[6]
 Bielskis, Antanas Andrius, Dzemydien, Dal, Denisov, Vitalij,Andziulis,Arunas,,Drungilas,Darius(2009),focused on the construction of intellectualized equipments for people with moving disabilities to help them in sustainable integration into environment. The problem is to reveal main components of diagnosis of disabled persons, as well as to develop decision making models which are integrated into the control mechanisms of the special equipments, that are assigned to the class of bio-robots. This paper analyses the approach of the construction of such type of bio-robots with possibilities to integrate different knowledge representation techniques for the development of the reinforcement framework with multiple cooperative agents for the recognition of the diagnosis of emotional situation of disabled persons. Large-scale of multi-dimensional recognitions of emotional diagnosis of disabled persons often generate a large amount of multi-dimensional data with complex recognition mechanisms, based on the integration of different knowledge representation techniques and complex inference models. Sensors can easily record primary data; however, the recognition of abnormal situations, cauterization of emotional stages and resolution for certain type of diagnosis is an oncoming issue for bio-robot constructors. [7]
 Raghunathan, Srinivasan, Tadikamalla, Pandu(1992) analyzed the Knowledge-based systems support the decision-making process with the help of domain specific knowledge bases. The knowledge bases almost always have uncertainty associated with them. A variety of approaches have been proposed in the artificial intelligence (AI) literature for the construction of and reasoning with uncertain knowledge bases. Building on this stream of research, we focus on how stochastic simulation can be used to construct and reason with knowledge bases that have uncertainties. An advantage of the simulation methodology is that it may not have to make many of the assumptions made by other approaches. It also allows the designer of the knowledge-based system to control the methodology based on accuracy and time requirements. The simulation approach to knowledge base construction is a modified version of the concept induction procedure used in AI. [8] 
Liu, Jianhui, Zhang, Songmao (2011) focused on color knowledge representation is an essential aspect for designing intelligent graphic systems. Since the mapping between low-level color numerical values and high-level semantics is crucial, the theory of fuzzy numbers is greatly needed for representing color concepts. The limited linguistic color words bring about the requirement for representing intermediate color concepts. However, there is not corresponding definitions in the fuzzy realm. To overcome this representational inadequacy, this work addresses the issue of formally defining the intermediate fuzzy concept. [9]
 Mueller, Erik T (2009) discussed the automation of commonsense reasoning, examining the manner in which the field of artificial intelligence will build applications that are more user-friendly and more aware of the world. Commonsense reasoning, the article states, is the human ability to make inferences about properties in the everyday world. Other topics include computational approaches to commonsense reasoning, the use of analogical processing, probability theory, and qualitative reasoning, and methods based on natural language. [10]
Loutfi,Amy,Coradeschi,Silvia,Daoutis,Marios,Melchert,Jonas(2008) introduced the symbolic knowledge representation and reasoning capabilities to enrich perceptual anchoring. The idea that encompasses perceptual anchoring is the creation and maintenance of a connection between the symbolic and perceptual description that refer to the same object in the environment. In this work we further extend the symbolic layer by combining a knowledge representation and reasoning (KRR) system with the anchoring module to exploit knowledge inference mechanisms. [11]
 Vranea, Sanja, Stanojevi, Mladen(1995), explored the knowledge-based systems which  suffered the frequent criticism of having little relevance to the real world, an increasing number of current applications deal with complex, real-world problems. Due to the complexity of real-world situations, no one general software technique can produce adequate results in different problem domains and artificial intelligence usually needs to be integrated with conventional paradigms for efficient solutions. The complexity and diversity of real-world applications have also forced the researchers in the AI field to focus more on the integration of diverse knowledge representation and reasoning techniques for solving challenging, real-world problems. [12]
Qiu, Guo Fang, Li, Huai Zu, Xu, Li Da, Zhang, Wen Xiu(2003)  discussed the probability reasoning method, fuzzy reasoning method, evidential reasoning method and other reasoning methods are main techniques employed in intelligent systems for processing uncertain and vague information. The concept of inclusion degree was proposed earlier and it has been proved that the methods mentioned above are examples of inclusion degrees.[13] 
Blair, Andrew, Debenham, John1,Edwards, Jenny(1997)described  a comprehensive study conducted to understand the methodologies which are being used to design Intelligent Decision Support Systems (IDSSs) and to identify the key methodological problems and benefits with using these methodologies. This comprehensive study consists of two parts. The first part is two surveys which together identify the design methodologies being used by a group of IDSS developers and how acceptable they believe their methodologies were for designing their IDSSs. The second part is a comparison of six major formal IDSS design methodologies recently published in the literature and which are not yet known to many developers. [14]
Ho, G. T. S, Lau, H. C. W, W. H, Ning, A.(2004)focused on developing techniques on the analysis, design and management of whole logistics chains in particular in the domain of data mining, data analysis and data classification. While the data mining techniques and computational intelligence techniques are good independently for solving specific tasks, they can be synergized through the formation of an integrated and unified model, which can take advantage of the goods and offset the flaws of the two techniques. In this paper, an intelligent information infrastructure, which is characterized by its ability to encompass a rich collection of knowledge representation formalisms for dealing with a logistics information flow problem, is presented. [15]

V.	STATISTICAL ANALYSIS
Statement: Uncertain representation of knowledge in artificial intelligent and its significant role in business process reengineering.
Ho= There is no significant relationship between uncertain representation of knowledge in artificial intelligent tends to business process reengineering..
Artificial Intelligent

 FrequencyPercentValid PercentCumulative PercentValidPoor23.64.04.0 Below Average35.56.010.0 Average1018.220.030.0 Good2647.352.082.0 Excellent916.418.0100.0 Total5090.9100.0 MissingSystem59.1  Total55100.0  
Descriptive Statistics

 NMinimumMaximumMeanStd. DeviationArtificial Intelligent501.005.003.7400.96489Valid N (list wise)50    
Test Statistics

 Artificial IntelligentChi-Square(a)37.000df4Asymp. Sig..000
Note: a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 10.0.

Statistical Analysis: The data are analyzed by using the chi-square test at 0.05 level of significant, the probability of statistics of chi-square test for the uncertain representation of knowledge and its significant role in business process reengineering is 37.00 and its P-value is 0.000 which is less than 0.05, so the null hypothesis is rejected and relationship is the significant between uncertain representation of knowledge in artificial intelligent tends to business process reengineering.. 


Data Analysis

The above graph is the statistical report of the uncertain representation of knowledge in artificial intelligent which tends to business process reengineering. It is analyzed by using chi-square test goodness of fit. In this statistical analysis the role of the uncertain representation of knowledge in artificial intelligent 4.0% are poor, 6.0% are below average, 20.0% are average, 52.0% are good and 18.0% are excellent which are enforcing to implement the artificial intelligent in business process reengineering on the basis of uncertain representation of knowledge.
IV.	Conclusion
In this research the researchers are analyzed the different cases of uncertain representation of knowledge in artificial intelligent and its significant role in business process reengineering. During this research study the researchers found the there are number of uncertainty of knowledge representation in artificial intelligent which can be solved by the statistical analysis and does not produce accurate result. The researchers also find the out, it is a significant research area in artificial intelligent which tends to business process reengineering.

ACKNOWLEDGEMENT
I would like to express my deep gratitude to Dr. Chandrahauns R Chavan , my research supervisor for his kind patient guidance, enthusiastic encouragement and useful critiques of this research work. He is an MCM, M. Phil (Business Admin.), Ph. D.( Business Mgmt.), PGDCHE, Certified Corporate Director from World council of corporate Governance,(UK) Professor & Former Director, Jamnalal Bajaj Institute of Management Studies University of Mumbai�Former I/c Professor-Cum-Director, Alkesh Dinesh Modi Institute of Financial �Management Studies,�University of Mumbai.
 
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