Table of Contents
- 1 BCA Business Expert System and Artificial Intelligence Notes Study Material
- 1.0.1 BCA Business Expert System and Artificial Intelligence Notes Study Material
- 1.0.2 An Expert System Solution
- 1.0.3 Business Expert System: Introduction
- 1.0.4 Knowledge Management and Artificial Intelligence in Customer Relationship Management
- 1.0.5 Expert System And Knowledge Management
- 1.0.6 Introduction to Artificial Intelligence
- 1.0.7 Artificial Intelligence in Manufacturing
- 1.0.8 Artificial Intelligence in Financial Services
BCA Business Expert System and Artificial Intelligence Notes Study Material
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BCA Business Expert System and Artificial Intelligence Notes Study Material
An Expert System Solution
General Electric’s (GE): Top Locomotive Field Service Engineer was nearing retirement.
Traditional Solution: Apprenticeship but would like:
- A more effective and dependable way to disseminate expertise.
- To prevent valuable knowledge from retiring.
- To minimize extensive travel or moving the locomotives.
- To model the way a human troubleshooter works.
(i) Months of knowledge acquisition.
(ii) 3 years of prototyping.
- A novice engineer or technician can perform at an expert’s level:
(i) On a personal computer.
(ii) Installed at every railroad repair shop served by GE.
1. (ES) Introduction
- Expert system Vs. Knowledge-based system,
- An expert system is a system that employs human knowledge captured in a computer to solve problems that ordinarily require human expertise.
- ES imitate the expert’s reasoning processes to solve specific problems.
- Attempt to imitate expert reasoning processes and knowledge in solving specific problems.
- Most popular applied Al technology.
(i) Enhance productivity.
(ii) Augment workforces.
- Narrow problem-solving areas or tasks.
- Provide direct application of expertise.
- Expert systems do not replace experts, but they:
(i) Make their knowledge and experience more widely available.
(ii) Permit non-experts to work better.
2. Structure of Expert System
- Development environment.
- Consultation (runtime) environment.
Three Major ES Components:
Basic Expert System Structure:
All ES Components:
- Knowledge acquisition subsystem
- Knowledge base
- Inference engine
- User interface
- Blackboard (workplace)
- Explanation subsystem (justifier)
- Knowledge refining system
- Most ES do not have a knowledge refinement component.
- The knowledge base contains the knowledge necessary for understanding, formulating and solving problems.
- Two basic knowledge base elements
(ii) Special heuristics, or rules that direct the use of knowledge.
(iii) Knowledge is the primary raw material of ES.
(iv) Incorporated knowledge representation.
- The brain of the ES.
- The control structure (rule interpreter).
- Provides methodology for reasoning,
- Language processor for friendly, problem-oriented communication.
- NLP, or menus and graphics.
The Human Element in Expert Systems:
- Builder and user
- Expert and knowledge engineer.
- The expert
(i) Has the special knowledge, judgment, experience and methods to give advice and solve problems.
(ii) Provides knowledge about task performance.
The Knowledge Engineer:
- Helps the expert(s) structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counter examples and bringing to light conceptual difficulties.
- Usually also the system builder.
- Possible classes of users:
(i) A non-expert client seeking direct advice-the ES acts as a consultant or adviser.
(ii) A student who wants to learn-an instructor.
(iii) An ES builder improving or increasing the knowledge base-a partner.
(iv) An expert-a colleague or assistant.
- The expert and the knowledge engineer should anticipate users’ needs and limitations when designing ES.
Business Expert System: Introduction
In now a days business surroundings where companies are faced with tough competition their main task becomes proper management of large amounts of data at their disposal in order to successfully achieve better market position by obtaining useful and relevant information and knowledge. Revealing information and knowledge hidden in data and through their proper use companies can encourage their business, improve a decision-making process and develop foundation for better market positioning. Right here lies a connection between intelligent system and knowledge management (KM) concept.
Intelligent systems rely on the expertise and experience of human-expert(s) in solving different problems and as such they simulate their reasoning and behaviour in a specific problem domain. Usually a domain expert provides “rules of thumb” about the way problem is being evaluated, either explicitly with the help of experienced system developers, or implicitly, extracting the rules from relevant data sets (Grljevic & Bosnjak, 2010). Intelligent systems can be used to create corporate knowledge, for its transfer and dissemination.
KM is an organizational practice that promotes a holistic approach in identifying managing and exchanging intellectual property of the entire organization as well as unarticulated expertise and experience of its employees. In practice, KM often involves the creation of new knowledge, access to valuable knowledge from external resources, the use of available knowledge in the decision-making process, sharing corporate knowledge and best practice, etc.
KM significantly exceeds realms of design and use of tools and technologies for gathering, analyzing and transfer of data, since its primary focus are individuals and groups as creators and users of knowledge. However, since it significantly contributes to the development of the organization and its better market positioning, any technology or method that supports development and dissemination of knowledge is considered as key success factor of today’s organizations.
Knowledge Management and Artificial Intelligence in Customer Relationship Management
The main precondition of strengthening company’s market position and development of competitive advantage lies in customer satisfaction. In order to establish an effective relationship with customers, organizations must have qualitative customer knowledge that implies not only structured data, such as historical data about a contact, account, etc., but also unstructured information such as letters and faxes from customers, as well as additional types of information useful for marketing, sales and service activities.
In order to raise the level of customer satisfaction with products, services and even the company itself, it is necessary that a company provides high quality data, which includes its accuracy, relevance, timeliness, safety and the possibility of their exchange and portability. It is noted in (Buttle, 2009) that the fulfilment of a CRM vision depends upon how well the knowledge is set on points of sail and customers points of access. For these reasons, companies are increasingly turning towards utilization of artificial intelligence and the development and implementation of expert systems for knowledge management.
Expert systems have the possibility of collecting, storing, organizing, interpreting and distributing knowledge to users at their access points in order to meet the goals of marketing, sales and service.
Initiatives and efforts of CRM and knowledge management are directed towards the same goal: achieving continual improvement aimed at customers. This business orientation is named in literature as customer knowledge management and it is narrowed to the usage of knowledge for, from and about customers in order to enhance and improve all aspects of business and customer relationships.
- Knowledge about customers includes basic information about customers, their purchase history and tendencies, etc. based on this kind of knowledge company will have better understanding of customers requirements and a possibility to meet their needs more successfully.
- Knowledge for customers is information customers require to interact with the company, such as knowledge about sales, marketing, services and information about products. This kind of knowledge is usually implicit because it relies entirely on the experience of workers in sales or employees in marketing and services sector. Intelligent system, especially expert system, successfully translate this kind of knowledge into explicit knowledge.
- Knowledge from customers is a feedback received from customers regarding products and services and how they understand and perceive the shopping experience and it helps to enhance products and services.
Note: Knowledge management and customer relationship management are strongly supported by artificial intelligence products because of their capability of reasoning, knowledge representation and ontologies.
Expert System And Knowledge Management
Knowledge management cw56omprises several basic functions: knowledge identification, creation and its exchange. Once the knowledge is discovered and formalized, the company’s goal is to use such knowledge for business improvement and to insure it to retain the knowledge in the realms of company. Many concepts of knowledge management are not new. Its roots can be found in the area of expert system and artificial intelligence in general. From the aspect of knowledge lifecycle, expert systems can be applied in all its three phases:
- Knowledge creation
- Knowledge transfer
- Usage of knowledge
Each expert system is embedded in an appropriate manner a large amount of high-quality knowledge of the problems from a certain domains of human activity. Expert system, as an intelligent program, can then process embedded knowledge in order to successfully solve problems from its field of expertise, in a way that would be considered intelligent if the same problems would solve man. Knowledge implemented in an ES is situated in the knowledge base that is separated from the program that uses knowledge to solve problems, so called inference engine.
Knowledge acquisition phase of expert system development process is directly associated with the identification of knowledge in knowledge management. Since developed expert system offers the possibility of formalizing and automating acquired knowledge they are used to retain the knowledge in the organization for its further usage after the domain expert is gone.
Knowledge representation for knowledge management purposes is related to the knowledge representation and methods and techniques of coding the knowledge in the field of expert system.
Thus, many aspects of knowledge management are derived from expert system and artificial intelligence domain which leads to a conclusion that expert system should be an integral part of knowledge management system. For example, the U.S. Department of Labour (www.dol.gov) is using for years a Web-based expert system as an integral part of their knowledge management system. One of the functions of this ES is determination of the benefits of war veterans.
Expert system can be used for job training, integration of different sources of knowledge, and solving interdisciplinary problems. Furthermore, their application provides consistency in decision making; they make expertise more accessible, and promote and improve the exchange of knowledge.
Expert system is the ideal technology for formalizing existing knowledge in the organization, for its preservation and documentation, especially in today’s business environment where organizations are often reorganized, number of employees is reduced and older and more experienced workers are gone due to different reasons. Therefore, expert system is very useful for building the institutional memory of the organization before intellectual capital is lost.
Knowledge Management In Business Efficiency Expert System
Business efficiency can be expressed as a level of savings in achieved business results, and as such it measures the business success against the amount of inputted labour, fixed assets, capital and services necessary for its achievement. To measure the efficiency, one has to consider both the current and the externalized labour – the elements of a work process.
Furthermore, business efficiency shows the degree to which the set business goals are achieved, while the necessary resources for their fulfilment are saved up. The business is run efficiently if the achieved output is the result of economic disburses of assets, third-party services, labour force and if the produced goods can be sold at preset prices. The increase of efficiency is based on the sparse usage of assets and maximization of revenues.
The higher this indicator, the better the credit rating. The threshold is 1. If the indicator is less than 1, then the company is in a very bad situation, because its purchase prices are greater than sales prices.
Problem areas addressed by expert system:
- Interpretation system
- Prediction system
- Diagnostic system
- Design system
- Planning system
- Monitoring system
- Debugging system
- Repair system
- Instruction system
- Control system
Expert system’s benefits:
- Improved decision quality
- Increased output and productivity
- Decreased decision-making time
- Increased process(es) and product quality
- Capture scarce expertise
- Can work with incomplete or uncertain information
- Enhancement of problem solving and decision making
- Improved decision-making processes
- Knowledge transfer to remote locations
- Enhancement of other MIS
Expert system lead to:
- Improved decision making
- Improved products and customer service
- Sustainable strategic advantage
- May enhance organization’s image
Problems and limitations of expert system:
- Knowledge is not always readily available.
- Expertise can be hard to extract from humans.
- Expert system users have natural cognitive limits.
- ES work well only in a narrow domain of knowledge.
- Knowledge engineers are rare and expensive.
- Lack of trust by end-users.
- ES may not be able to arrive at valid conclusions.
- ES sometimes produce incorrect recommendations.
Introduction to Artificial Intelligence
Business applications utilize the specific technologies mentioned earlier to try and make better sense of potentially enormous variability (for example, unknown patterns/relationships in sales data, customer buying habits, and so on). However, within the corporate world, Al is widely used for complex problem-solving and decision-support techniques in real-time business applications. The business applicability of Al techniques is spread across functions ranging from finance management to forecasting and production.
In the intensely competitive and dynamic market scenario, decision-making has become fairly complex and latency is inherent in many processes. Fraud is a million dollar business and it is increasing every year. The PWC global economic crime survey of 2009 suggests that close to 30% of companies worldwide reported fallen victim to fraud in the past years. In addition, the amount of data to be analyzed has increased substantially. Al technologies help enterprises reduce latency in making business decisions, minimize fraud and enhance revenue opportunities.
Al & Importance of Al in Business Competitiveness
Al has a broad discipline in today’s world that promises to simulate numerous inherent human skills such as automatic programming, case-based reasoning, neural networks, decision-making, expert systems, natural language processing, pattern recognition, speech recognition and market competition due to technological advancement etc. AI technologies bring more complex data analysis features to existing applications. I think of AI as a science that investigates knowledge and intelligence, possibly the intelligent application of knowledge.
Knowledge and Intelligence are as fundamental as the universe within which they exist, it may turn out that they are more fundamental. Enterprises that utilize Al-enhanced applications are expected to become more diverse, as the needs for the ability to analyze data across multiple variables, fraud detection and customer relationship management emerge as key business drivers to gain competitive advantage.
Artificial Intelligence is a branch of Science which deals with helping machines, finds solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behavior appears.
Artificial Intelligence in Manufacturing
As the manufacturing industry becomes ever more competitive, sophisticated technology has emerged to improve productivity. Artificial Intelligence in manufacturing can be applied to a variety of systems. Al is involved in many areas of the automotive industry. Robots have been enhanced by the advancements in AI allowing for improved production Although intelligent assist devices are more expensive, they greatly increase production. It can recognize patterns, time consuming and mentally challenging tasks.
Artificial Intelligence can optimize the production schedule and production runs. For businesses, being capable of delivering high quality goods at low costs and short delivery times is similar to operating in a current environment like the Devil’s Triangle, and this is not a easy task for any organization. Managing so that production takes place at the right time, on the right equipment and using the right tools will minimize any deviations in delivery dates promised to the customer.
Utilizing equipment, personnel and tools to their maximal efficiency will no doubt improve any organization’s competitive strength. In return proper utilization of these capabilities will result in lower costs for the organization
Optimal scheduling of jobs on equipment, without the use of computer software, is a truly difficult undertaking. Performing planning using the “Deterministic Simulation Method” will provide you with schedules that will indicate job loads per equipment.
Even ” the case limited to a single piece of equipment, as the number of jobs to schedule that equipment increases, finding the right solution in the “Possible Solutions Ser become next to impossible. And in the real world, the difficulties arising from the lar size of the solutions set due to the recipes formed by jobs, equipment and products, and shaped by the technological restrictions, as well as the complexity in finding a close to ideal solution, are readily apparent.
Research and studies are being conducted worldwide on the subject of scheduling. Software vendors working in this area follow developments closely, and they are coming out with new products to better meet demands. “Genetic Algorithms”, “Artificial Intelligence”, and “Neural Networks” are some of the technologies being used for scheduling.
- View your best product runs and the corresponding settings.
- Increase efficiency and quality by using optimal settings from past production.
- Artificial Intelligence can optimize your schedule beyond normal human capabilities.
- Increase productivity by eliminating downtime due to unpredictable changes in the schedule.
Artificial Intelligence in Financial Services
Al has found a home in financial services and is recognized as a valuable addition to numerous business applications. Sophisticated technologies encompassing neural networks and business rules along with Al-based techniques are compliant positive results in transaction-oriented scenarios for financial services. Al has been widely adopted in such areas of risk management, compliance, and securities trading and monitoring, with an extension into customer relationship management (CRM).
Warren Buffet is known as the ultimate investor in this age. So good is he, in fact, that artificial intelligence software developed in Carnegie Mellon that predicts stock movements was named after him by. But can machines really take the place of human traders, much less surpass them? When Deep Blue defeated Chess Grandmaster Kasparov in 1997, AI was propelled into the limelight.
Indeed, if a machine can whiz through the intricacies of the ultimate game of strategy, why not beat man in other fields as well – thereby facilitating work, decreasing costs and errors and increasing productivity and quality. This study focuses on applying AI in Finance, particularly in stock trading. In the field of Finance, artificial intelligence has long been used.
Some applications of artificial intelligence are:
- Credit authorization screening
- Mortgage risk assessment
- Project management and bidding strategy
- Financial and economic forecasting
- Risk rating of exchange-traded, fixed income investments
- Detection of regularities in security price movements
- Prediction of default and bankruptcy
- Security and or Asset Portfolio Management.
Artificial intelligence types used in finance include neural networks, fuzzy logic, genetic algorithms, expert systems and intelligent agents. They are often used in combination with each other. One such case is Fidelity Investments. In this paper, I set the stage by describing how traditional stock trading differs from Al-powered stock trading. We define the various Al systems available and also explore the various solutions available in the market, their IT foundations and how salient they are.
Then, we move into how Al systems for stock trading will affect traders, companies and individuals. Benefits, risks and competitive strategy will be defined and real-world examples cited, as grounding for our recommendations in the end. Recommendations include getting management buy-in, implementing the system and managing the whole structure to succeed.
Artificial Intelligence in Marketing
Advances in artificial intelligence (AI) eventually could turbo-boost customer analytics to give companies speedier insights into individual buying patterns and a host of other consumer habits. Artificial intelligence functions are made possible by computerized neural networks that simulate the same types of connections that are made in the human brain to generate thought.
Currently, the technology is used mostly to analyze data for genetics, pharmaceutical and other scientific research. It’s seeing little use in CRM right now, though it has tremendous potential in the future Al-enhanced analytics programs also provide survival modeling capabilities suggesting changes to products based on use. For example, customer patterns are analyzed to learn ways to extend the life of light bulbs or to help decide the correct dosage for medications. High-tech data mining can give companies a precise view of how particular segments of the customer base react to a product or service and propose changes consistent with those findings.
In addition to further exploring customers “buying patterns, analytics could help companies react much more quickly to the marketplace“. According to Meta Group vice president Liz Shahnam, intelligent agents could let companies make real-time changes to marketing campaigns. “New technologies would have the model refreshed on the fly based on each new incoming piece of customer information – reaction to the campaign for a more targeted offer.”
Artificial Intelligence in HR
It is widely believed that the role of managers is becoming a key determinant for enterprises’ competitiveness in today’s knowledge economy era. Owing to fast development of information technologies (ITs), corporations are employed to enhance the capability of human resource management, which is called human resource information system (HRIS). Recently, due to promising results of artificial neural networks (ANNs) and fuzzy theory in engineering, they have also become candidates for HRIS.
The artificial intelligence (AT) field can play a role in this, especially; in assuring that the fuzzy neural network has the characteristics and functions of training, learning and simulation to make an optimal and accurate judgment according to the human thinking model. The main purposes of the study are to discuss the appointment of managers in enterprises through fuzzy neural network, to construct a new model for evaluation of managerial talent, and accordingly to develop a decision support system in human resource selection.
Therefore, the research methods of reviewing literature, in-depth interview, questionnaire survey and fuzzy neural network are used in the study. The fuzzy neural network is used to train the concrete database, based on 191 questionnaires from experts, for getting the best network model in different training conditions. In order to let decision-makers adjust weighted values and obtain decisive results of each phase’s scores, we adopted the simple additive weighting (SAW) and fuzzy analytic hierarchy process (FAHP) methods in the study. Finally, the human resource selection system of Java user interface has been constructed by FNN in the study.