CHALLENGES AND CURRENT TRENDS IN KNOWLEDGE BASED SYSTEM Dr. Priyanka Sharma

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1 Review Article CHALLENGES AND CURRENT TRENDS IN KNOWLEDGE BASED SYSTEM Dr. Priyanka Sharma Address for Correspondence Professor & Head, MCA, Institute of Science and Technology for Advanced Studies and Research (ISTAR), Vallabh Vidya Nagar, Gujarat. India. KNOWLEDGE BASED SYSTEM (KBS) - Conference on AI in The term has now TODAY S NEED: The realization of Power lies in the knowledge by the scientist of AI during 1960s led to the development of a DSS and KBS. Pfeifer & Luthi (1987) noted that DSS and KBS as no longer opposed but as complementary. Watkins and Mekinney (1995) defined DSS as: A DSS is an integrated, interactive computer system, consisting of analytical tool and information management capabilities, designed to aid decision makers in solving relatively large, unstructured problems. However, it is important to note that most DSS applications described in the literature are dynamic, growing systems, in which new capabilities, problems, and analytical tools are continually being added to the existing structure. This realization much of the work done in AI has been related to KBS, including work in vision, general problem solving commercially, and learning. So, KBS is a subset of AI, sometimes said to be the successful part of AI mainly because they operate in a restricted domain of expertise such as law, agriculture etc. Since KBS use specialized sets of coded Knowledge to reason and perform limited intelligence tasks. Edward (1977) emphasized the fact that the real power of an expert system comes from the Knowledge it possess rather than the particular inference schemes and other formalisms it employs in his paper at the International Joint virtually replaced Expert systems. According to Computer User High Technology Dictionary, KBS is defined as KBS is computer system that is programmed to initiate human problem solving by means of AI and reference to a database of knowledge on particular subject. KBS is a technology that is useful for solving ill-defined problems i.e. the problems that cannot be solved by using algorithms which requires large Knowledge from Knowledge Base and the problems where there is no guarantee that the solutions can be found or the solution can be best. Initially, KBS was largely used in solving standalone problems like diagnostic problem solving in medical, in engineering, in provision of advice, in construction etc. At present, KBS is routinely used in thousands of real world applications of semi structured or unstructured nature. Most of such applications involve knowledge bases containing millions of units. Developing such applications will require a technology of building, accessing and managing large knowledge bases. Such a technology will be founded on extensions of current techniques for knowledge bases as well as databases and addressing issues of physical storage management, query optimization, concurrency control, constraint enforcement and others.

2 CHALLENGES AND CURRENT TRENDS IN KBS: In the past the main focus of agronomic research has been concerned with crop production and profitability. However, the use of knowledge-based crop growth models to predict crop production and water and nutrient dynamics in the soil and plant, has become common place recently [9]. Further, now a day in addition to profitable crop production, the quality of environment has become an important issue that must be addressed by all sectors of agriculture. Agricultural managers require strategies for optimizing the profitability of crop production while maintaining soil quality and minimizing environment degradation [10]. Development of an intelligent system itself is a challenge in terms of knowledge acquisition, knowledge representation and knowledge inference since the base of the human intelligent vary from one individual to other. Further, various components of farming systems interacting with each other, there is a degree of uncertainty and risk in decision making process [8]. Also the traditional system analysis and development methodologies may not be always applicable. Modeling of large size of knowledgebase as though KBS focuses in a narrow application domain, the amount of knowledge chunks to be stored in it is high and having different representation schemes. Due to this, managing and using the large sized knowledge base becomes complicated and sometimes a difficult task. Thus, the future KBS typically may require huge amount of data, intelligent inference engines, vision, speech recognition, natural language processing and intelligent programming. Today s knowledge bases are quite diverse. They may be distributed, thus being created by multiple sources at different times to a wide variety of information input. Some of the information input may be in the form of natural text and hence text analytic techniques are required to generate logical statements automatically. These logical statements may then provide a portion of or be an entire knowledge base form which knowledge can be derived at the later. Although development of agriculture based KBS is not so simple but rather making these systems available to farmers is too difficult. While these systems are used extensively in research settings, they are infrequently incorporated into the decision making process. Boone (1997) noted that the farmer reject software because they required too many inputs and value of some of them are not known to them scientifically. Farmers mainly use their experience and mind to process relevant data and make decisions mostly by intuition. New tools have to be developed for encoding knowledge directly to computer into, removing the role of knowledge engineer [37]. A user interface is directly affected by culture of community under consideration. Development of a regional based intelligent user interface is also to be considered. Hypermedia techniques may enhance the effectiveness of such user interface. The Service Oriented Architecture (SOA) is also equally important coming up area because of diverse and dynamic requests may be from different locations simultaneous. The SOA can be implemented using many different environments. The major difficulty in order for SOA to work is the interoperability standards related to all aspects of service operations are required, which includes Service identification,

3 Service location, Domain definition, Service governance and so on. Besides these, one more aspect to consider for Research & Development is the availability of specific and direct support of Verification and Validation methods for new knowledge and stored knowledge at given time interval. For executives in different areas, direct and intelligent KBS support is desired. For this tailor made Expert system may be one of the solution. Present day KBS faces challenges which impediment them to become completely successful AI system. Some of them are listed below [38]. Complex & Changing requirements: One of the reasons knowledge systems are justified is that the functionality being computerized is complex. But one of the reasons that applications becomes complex is that their requirements are constantly changing. For example: underwriting policies, tax laws, pricing policies etc do change frequently. Costly: Although KBS may in some cases be easier to change than conventional code, the cost of such changes can damage the economics of an application. Due to frequent changes often in the real world, KBS needs constant updating. Thus the use of KBS technology does not save on software maintenance. Furthermore, due to high functionality, the number of users of any KBS application seems to be big and any costs must be retrieved from the benefits to this small number of users. Fujitsu and other companies reported found that 60 to 70 percent of the development cost of a KBS was spent on coding the user interface. This results into high cost of producing user Interface. Also, now a day s physical location is not barrier. But required Infrastructure for KBS setup is not available everywhere. Thus KBS cannot be shared easily and setting infrastructure every time becomes costly. Poor Performance: KBSs, particularly those with large knowledge bases, were found to run very slowly and to consume substantial computing resources. This affected the choice of applications. Realtime applications were mostly avoided, but so were others where resource consumption was a problem. Programmer Education: Customers had little familiarity with LISP, in which most of the initial KBSs were written. Now that KBS products are being rewritten in C (and, in addition, Fujitsu has made available both FORTRAN/KR and COBOL/KR, which add rule capability to both of these languages), the language problem is going away. Not Complete Solution: KBS are not, in general, a complete solution, but must be combined with other technologies to solve almost any real problem. The KBS approach is more of a technique and a skill than a solution. Cannot decide cost: When choosing techniques to solve a problem, such as choosing between C or LISP, or between linear programming and a KBS, one cannot normally attribute the costs or benefits to the technology employed. These attributes belong to the application. No CASE Methodology: There are no well established standards for creating a

4 KBS. Techniques for testing a KBS to determine its scope of applicability are also not well established. The current trend is of hybrid systems that derive their expertise by combining automated extraction of knowledge from data with human experts in specific knowledge domains [29]. These hybrid systems will become increasingly popular as increasingly digital world gives rise to massive amounts of data that require analysis. The signs show that the traditional marketplace for KBS vanished. Nowadays, they are intrinsically integrated in various Knowledge Management tools and there is a strong tendency of seeing them as accessories of knowledge workers, rather than a possible substitute for their role. As per SRI Consulting Business Intelligence (2003) some of the trends of the moment involving KBS deployment are: distributed Artificial Intelligence; real-time KBS; visualization software; standards development; the semantic web; open knowledge bases. The basic categories of research in KBS are knowledge representation, knowledge use or problem-solving and knowledge acquisition. Finally, research is underway to explore the use of new parallel computing methods in the implementation of expert systems and advanced KBS. The new wave of computing is multi-processor technology. CONCLUSION In the 20 th century we have seen more technological change then preceding centuries. The 21 st century will see almost a thousand times greater technological change. AI research and development will continue and all of various subfields will evolve and improve. Special search, pattern matching and symbolic computing chips will be developed [20]. Further, new software techniques and hardware will be discovered for easier development of expert systems and other AI applications. Natural language interfaces, intelligence databases will be more sophisticated. At social level, improved communication and information will lead to reduced energy consumption, reduced pollution and lead to better environment. Some of the fields, which will improve common man s life are better land record management, efficient natural resource planning, better banking and financial serviced, improved legal delivery system, networked educational systems etc. [23]. When AI researchers finally solve the general learning problem, it will be possible to build machines that learn to give humans what they want even before they knew they want it. Ray Kurzweil, in The Age of Spiritual Machines (2000), predicts that by the year 2099 there will be a strong trend towards a merger of human thinking with the world of machine intelligence that the human species initially created. There is no longer any clear distinction between humans and computers. In conclusion, we see that AI has made great progress in its short history, but the final sentence of Alan Turing s (1950) paper on Computing Machinery and Intelligence is We can see only a short distance ahead, but we can see that much remains to be done, which is still valid today. REFERENCES 1. A. Mirsepassi- Application of Intelligent System for water treatment plant operation Iranian Journal of ENV. Health Sc. Sugg. 2004, Vol. 1, No-2, PP Alavi M., and D Leidner Knowledge Management Systems : Issues, Challenges

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