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Arti cial Intelligence and Expert
System
Arti cial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. According to Textbooks, the Arti cial Intelligence is “the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success”.
Arti cial intelligence has been the subject of optimism, but has also su ered setbacks and, today has become an essential part of the technology industry, providing the heavy lifting for many of the most di cult problems in computer science. All research is highly technical and specialized, deeply divided into sub elds that often fail to communicate with each other Sub elds have grown up around particular institutions, the work of individual researchers, the solution of speci c problems, longstanding di erences of opinion about how AI should be done and the application of widely di ering tools.
The use of Arti cial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. Arti cial intelligence (AI) is the eld of scienti c inquiry concerned with designing mechanical systems that can simulate human mental processes. The eld draws upon theoretical constructs from a wide variety of disciplines, including mathematics, psychology, linguistics, neurophysiology, computer science, and electronic engineering.
Some of the most promising developments to come out of recent AI research are “expert” systems or computer programs that simulate the problem-solving techniques of human experts in a particular domain.
There is a class of computer programs, known as expert systems that aim to mimic human reasoning. The methods and techniques used to build these programs are the outcome of e orts in a eld of computer science known as Arti cial Intelligence (AI). Expert systems have been built to
diagnose disease (Path nder is an expert system that assists surgical pathologists with the diagnosis of lymph-node diseases, aid in the design chemical syntheses (Example), the prospect for mineral deposits (PROSPECTOR), translate natural languages, and solve the complex mathematical problem (MACSYMA).
INTRODUCTION:
The term Arti cial Intelligence was coined by John McCarthy, in 1956, who de nes it as “the science and engineering of making intelligent machines. The eld was founded on the claim that a central property of humans, intelligence. The sapience of Homo sapiens can be so precisely described that it can be simulated by a machine. This raises philosophical issues about the nature of the mind and limits of scienti c hubris, issues which have been addressed by myth, ction and philosophy since antiquity.
Arti cial Intelligence (AI) is the key technology in many of today’s novel applications, ranging from banking systems that detect attempted credit card fraud, to telephone systems that understand speech, to software systems that notice when you’re having problems and o er appropriate advice. These technologies would not exist today without the sustained federal support of fundamental AI research over the past three decades. Arti cial Intelligence (AI) in the eld of information technology focused on creating machines that can participate in behaviors that humans consider intelligent. The possibility of intelligent machines to have human curiosity since ancient times and today with the advent of computer and 50 years of research into AI programming techniques, the dream of smart machines is a reality. Researchers create systems that can mimic human thought, understand speech, then the best player chess husband, and countless bene ts not possible before.
This mainly concerned with one of the major branches of AI, that is expert systems. Building an expert system is known as knowledge engineering and its practitioners are called knowledge engineers. The knowledge engineer must make sure that the computer has all the knowledge needed to solve a problem. The knowledge engineer must choose one or more forms in which to represent the required knowledge as symbol patterns in the memory of the computer – that is, he (or she) must choose a knowledge representation. He must also ensure that the computer can use the knowledge e ciently by selecting from a handful of reasoning methods. The practice of knowledge engineering is described later. We rst describe the components of expert systems.
In conventional computer programs, problem-solving knowledge is encoded in program logic and program-resident data structures. Expert systems di er from conventional programs both in the way problem
Understanding the Arti cial Intelligence
Expert System Architecture
An expert system is, typically, composed of two major components, the Knowledge-base and the Expert System Shell. The Knowledgebase is a collection of rules encoded asmetadata in a le system, or more often in a relational database. The Expert System Shell is a problem-independent component housing facilities for creating, editing, and executing rules. A software architecture for an expert system is illustrated in Figure 2.
The shell portion includes software modules whose purpose it is to,
Process requests for service from system users and application layer modules; Support the creation and modi cation of business rules by subject matter experts; Translate business rules, created by a subject matter experts, into machine-readable forms; Execute business rules; and Provide low-level support to expert system components (e.g., retrieve metadata from and save metadata to knowledge base, build Abstract Syntax Trees during rule translation of business rules, etc.). knowledge base, build Abstract Syntax Trees during rule translation of business rules, etc.).
Client Interface
The Client Interface processes requests for service from system-users and from application layer components. Client Interface logic routes these requests to an appropriate shell program unit. For example, when a subject matter expert wishes to create or edit a rule, they use the Client Interface to dispatch theKnowledge-base Editor. Other service requests might schedule a rule, or a group of rules, for execution by the Rule Engine.
Knowledge Base Editor
The Knowledge-base Editor is a simple text editor, a graphical editor, or some hybrid of these two types. It provides facilities that enable a
In translating rules from one form to another, the structure of the original rule is never lost. It is always possible to recreate a human- readable rule, exactly, from its Knowledge-base representation or from its AST representation.
Rule Engine
The Rule Engine (often referred to as an inference engine in AI literature) is responsible for executing Knowledge-base rules. It retrieves rules from the Knowledge-base, converts them to ASTs, and then provides them to its rule interpreter for execution. The Rule Engine interpreter traverses the AST, executing actions speci ed in the rule along the way. This process is depicted in Figure 6.
Rule Object Classes
The shell component, Rule Object Classes, is a container for object classes supporting,
Rule editing; AST construction; Conversion of ASTs to rule metadata; Conversion of rule metadata to ASTs; and Knowledge-base operations (query, update, insert, delete).
Constructing an Expert System
The construction of an expert system is less challenging than one might think, given the almost magical powers attributed to this class of programs. The task is made easier because,
Large portions of the Rule Translator can be generated automatically using lexical analyzer and parser generators, and Text editors (e.g., TextPad) can be purchased, inexpensively, and integrated into the Expert System Shell.
The design and the construction of the expert system involve the four major steps depicted below Figure.
Advantages
Smarter arti cial intelligence may replace human jobs, freeing people for other pursuits by automating manufacturing and transportation. Self-modifying, self-writing and learning software can relieve programmers of the burdensome tasks of specifying the functions of di erent programs. Arti cial intelligence will be used as cheap labour, thus increasing pro ts for corporation. Arti cial intelligence can make deployment easier and less resource intensive Compared to traditional programming techniques, expert-system approaches provide the added exibility (and hence easier modi ability) with the ability to model rules as data rather than as code. In situations where an organization’s IT department is overwhelmed by a software-development backlog, rule-engines, by facilitating turnaround, provide a means that can allow organizations to adapt more readily to changing needs. In practice, modern expert-system technology is employed as an adjunct to traditional programming techniques, and this hybrid approach allows the combination of the strengths of both approaches. Thus, rule engines allow control through programs (and user interfaces) written in a traditional language, and also incorporate necessary functionality such as inter-operability with existing database technology.
Disadvantages
Rapid advances in AI could lead to massive structural unemployment. Unpredictable and unforeseen impacts of new features. An expert system or rule-based approach is not optimal for all problems, and considerable knowledge is required so as to not misapply the systems. Ease of rule creation and rule modi cation can be double-edged. A system can be sabotaged by a non-knowledgeable user who can easily add worthless rules or rules that con ict with existing ones. Reasons for the failure of many systems include the absence of (or neglect to employ diligently) facilities for system audit, detection of possible con ict, and rule lifecycle management (e.g. version control, or thorough testing before deployment). The problems to be addressed here are as much technological as organizational.
Applications:
Introduction to Machine Learning Approaches Fuzzy Logic Improves Decision Support Software
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According to many experts, faster than the majority of us think or are prepared for. “we will have both the hardware and the software to achieve human level arti cial intelligence with the broad suppleness of human intelligence including our emotional intelligence by 2029.” If that sounds like something from a scary movie (“Terminator” may come to mind). Its not to worry, such super machines will also have morals and respect us as their creators (the people in scary movies rarely think that anything bad will happen to them either). He also believes that humans themselves will be smarter, healthier, and more capable in the near future by merging with our technology. For example, tiny robots implanted in our brains will work directly with our neurons to make us smarter (this may call to mind some other movies). AI began as an attempt to answer some of the most fundamental questions about human existence by understanding the nature of intelligence, but it has grown into a scienti c and technological eld a ecting many aspects of commerce and society. Even as AI technology becomes integrated into the fabric of everyday life, AI researchers remain focused on the grand challenges of automating intelligence. Work is progressing on developing systems that converse in natural language, that perceive and respond to their surroundings, and that encode and provide useful access to all of human knowledge and expertise.
Conclusion
Its now the time to sit and think upon for the future of arti cial intelligence in expert systems whether as to go with traditional technologies or to adapt the science of arti cial intelligence. The overall motivation behind this paper is to modernize our ancestral methods so as to bring in a rapid change in the growth of highly developed expert systems so as to cater the needs of growing population. The development process may be incremental but the overall concept requires a paradigm shift in the way we think about modernization of production that is based more on needs and novel ways of meeting them rather than modifying existing techniques.
Author: Ravi Bandakkanavar A Techie, Blogger, Web Designer, Programmer by passion who aspires to learn new Technologies every day. It has been 8 years since I have been publishing articles and enjoying every bit of it. I want to share the knowledge and build a great community with people like you.
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