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Expert system , a computer program that uses artificial-intelligence methods to solve problems within a specialized domain that ordinarily requires human expertise. Dendral, as their expert system was later known, was designed to analyze chemical compounds. Expert systems now have commercial applications in fields as diverse as medical diagnosis , petroleum engineering , and financial investing. In order to accomplish feats of apparent intelligence, an expert system relies on two components: a knowledge base and an inference engine.
Expert Systems and Applied Artificial Intelligence. The field of artificial intelligence AI is concerned with methods of developing systems that display aspects of intelligent behaviour. These systems are designed to imitate the human capabilities of thinking and sensing. In AI applications, computers process symbols rather than numbers or letters.
AI applications process strings of characters that represent real-world entities or concepts. Symbols can be arranged in structures such as lists, hierarchies, or networks. These structures show how symbols relate to each other.
Computer programs outside the AI domain are programmed algorithms; that is, fully specified step-by-step procedures that define a solution to the problem.
The actions of a knowledge-based AI system depend to a far greater degree on the situation where it is used. Artificial intelligence is a science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering. The goal of AI is to develop computers that can think, see, hear, walk, talk and feel. A major thrust of AI is the development of computer functions normally associated with human intelligence, such as reasoning, learning, and problem solving.
Result: Transaction processing and decision support systems using AI. Result: Resembling the interconnected neuronal structures in the human brain. Result: Software that performs assigned tasks on the users behalf. The most important applied area of AI is the field of expert systems. An expert system ES is a knowledge-based system that employs knowledge about its application domain and uses an inferencing reason procedure to solve problems that would otherwise require human competence or expertise.
The power of expert systems stems primarily from the specific knowledge about a narrow domain stored in the expert system's knowledge base. It is important to stress to students that expert systems are assistants to decision makers and not substitutes for them. Expert systems do not have human capabilities. They use a knowledge base of a particular domain and bring that knowledge to bear on the facts of the particular situation at hand.
The knowledge base of an ES also contains heuristic knowledge - rules of thumb used by human experts who work in the domain. The test outlines some illustrative minicases of expert systems applications. These include areas such as high-risk credit decisions, advertising decision making, and manufacturing decisions. Generic Categories of Expert System Applications. Table Application areas include classification, diagnosis, monitoring, process control, design, scheduling and planning, and generation of options.
Classification - identify an object based on stated characteristics. Diagnosis Systems - infer malfunction or disease from observable data. Monitoring - compare data from a continually observed system to prescribe behaviour. Process Control - control a physical process based on monitoring. Design - configure a system according to specifications.
Generation of Options - generate alternative solutions to a problem. The strength of an ES derives from its knowledge base - an organized collection of facts and heuristics about the system's domain. An ES is built in a process known as knowledge engineering , during which knowledge about the domain is acquired from human experts and other sources by knowledge engineers. The accumulation of knowledge in knowledge bases, from which conclusions are to be drawn by the inference engine, is the hallmark of an expert system.
Knowledge Representation and the Knowledge Base. The knowledge base of an ES contains both factual and heuristic knowledge. Knowledge representation is the method used to organize the knowledge in the knowledge base.
Knowledge bases must represent notions as actions to be taken under circumstances, causality, time, dependencies, goals, and other higher-level concepts. Several methods of knowledge representation can be drawn upon. Two of these methods include:. A frame specifies the attributes of a complex object and frames for various object types have specified relationships. Rule-based expert systems are expert systems in which the knowledge is represented by production rules.
A production rule, or simply a rule, consists of an IF part a condition or premise and a THEN part an action or conclusion. The explanation facility explains how the system arrived at the recommendation. Depending on the tool used to implement the expert system, the explanation may be either in a natural language or simply a listing of rule numbers.
Combines the facts of a specific case with the knowledge contained in the knowledge base to come up with a recommendation. In a rule-based expert system, the inference engine controls the order in which production rules are applied A fired and resolves conflicts if more than one rule is applicable at a given time. This is what A reasoning amounts to in rule-based systems. Directs the user interface to query the user for any information it needs for further inferencing.
The facts of the given case are entered into the working memory , which acts as a blackboard, accumulating the knowledge about the case at hand.
The inference engine repeatedly applies the rules to the working memory, adding new information obtained from the rules conclusions to it, until a goal state is produced or confirmed. Figure Inferencing engines for rule-based systems generally work by either forward or backward chaining of rules. Two strategies are:. The inferencing process moves from the facts of the case to a goal conclusion. The strategy is thus driven by the facts available in the working memory and by the premises that can be satisfied.
The inference engine attempts to match the condition IF part of each rule in the knowledge base with the facts currently available in the working memory. If several rules match, a conflict resolution procedure is invoked; for example, the lowest-numbered rule that adds new information to the working memory is fired.
The conclusion of the firing rule is added to the working memory. Forward-chaining systems are commonly used to solve more open-ended problems of a design or planning nature, such as, for example, establishing the configuration of a complex product.
If such a rule is found, its premise becomes the new subgoal. In an ES with few possible goal states, this is a good strategy to pursue. If a hypothesized goal state cannot be supported by the premises, the system will attempt to prove another goal state. Thus, possible conclusions are review until a goal state that can be supported by the premises is encountered.
Backward chaining is best suited for applications in which the possible conclusions are limited in number and well defined. Classification or diagnosis type systems, in which each of several possible conclusions can be checked to see if it is supported by the data, are typical applications.
Fuzzy logic is a method of reasoning that resembles human reasoning since it allows for approximate values and inferences and incomplete or ambiguous data fuzzy data. Fuzzy logic is a method of choice for handling uncertainty in some expert systems.
Expert systems with fuzzy-logic capabilities thus allow for more flexible and creative handling of problems. These systems are used, for example, to control manufacturing processes. There are several levels of ES technologies available. Two important things to keep in mind when selecting ES tools include:.
The tool selected for the project has to match the capability and sophistication of the projected ES, in particular, the need to integrate it with other subsystems such as databases and other components of a larger information system.
The tool also has to match the qualifications of the project team. A shell is an expert system without a knowledge base. A shell furnishes the ES developer with the inference engine, user interface, and the explanation and knowledge acquisition facilities. Domain-specific shells are actually incomplete specific expert systems, which require much less effort in order to field an actual system. Expert system development environments.
They run on engineering workstations, minicomputers, or mainframes; offer tight integration with large databases; and support the building of large expert systems. High-level programming languages. ESs are now rarely developed in a programming language. Three fundamental roles in building expert systems are:. Expert - Successful ES systems depend on the experience and application of knowledge that the people can bring to it during its development.
Large systems generally require multiple experts. Knowledge engineer - The knowledge engineer has a dual task. This person should be able to elicit knowledge from the expert, gradually gaining an understanding of an area of expertise.
Intelligence, tact, empathy, and proficiency in specific techniques of knowledge acquisition are all required of a knowledge engineer. Knowledge-acquisition techniques include conducting interviews with varying degrees of structure, protocol analysis, observation of experts at work, and analysis of cases.
On the other hand, the knowledge engineer must also select a tool appropriate for the project and use it to represent the knowledge with the application of the knowledge acquisition facility.
User - A system developed by an end user with a simple shell, is built rather quickly an inexpensively. Larger systems are built in an organized development effort.
A prototype-oriented iterative development strategy is commonly used. ESs lends themselves particularly well to prototyping. Steps in the methodology for the iterative process of ES development and maintenance include:. Problem Identification and Feasibility Analysis:. The needed degree of integration with other subsystems and databases is established. Testing and Refinement of Prototype:.
Expert Systems and Applied Artificial Intelligence. The field of artificial intelligence AI is concerned with methods of developing systems that display aspects of intelligent behaviour. These systems are designed to imitate the human capabilities of thinking and sensing. In AI applications, computers process symbols rather than numbers or letters. AI applications process strings of characters that represent real-world entities or concepts. Symbols can be arranged in structures such as lists, hierarchies, or networks.
Expert System is an interactive and reliable computer-based decision-making system which uses both facts and heuristics to solve complex decision-making problems. It is considered at the highest level of human intelligence and expertise. The purpose of an expert system is to solve the most complex issues in a specific domain. The Expert System in AI can resolve many issues which generally would require a human expert. It is based on knowledge acquired from an expert. Artificial Intelligence and Expert Systems are capable of expressing and reasoning about some domain of knowledge. Expert systems were the predecessor of the current day artificial intelligence, deep learning and machine learning systems.
and institutionalizing expert system development and usage. Download Design and Development of Expert Systems and Neural Networks free book PDF.
Expert systems ES are one of the prominent research domains of AI. The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise. Knowledge is required to exhibit intelligence.
Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. Expert Systems With Applications is a refereed international journal whose focus is on exchanging information relating to expert and intelligent systems applied in industry , government , and universities worldwide.
Expert System is an interactive and reliable computer-based decision-making system which uses both facts and heuristics to solve complex decision-making problems. It is considered at the highest level of human intelligence and expertise. The purpose of an expert system is to solve the most complex issues in a specific domain.
Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. Expert Systems With Applications is a refereed international journal whose focus is on exchanging information relating to expert and intelligent systems applied in industry , government , and universities worldwide. The thrust of the journal is to publish papers dealing with the design, development, testing The journal will publish papers in expert and intelligent systems technology and application in the areas of, but not limited to: finance, accounting, engineering, marketing, auditing, law, procurement and contracting, project management, risk assessment, information management, information retrieval, crisis management, stock trading, strategic management, network management, telecommunications, space education, intelligent front ends, intelligent database management systems, medicine, chemistry, human resources management, human capital, business, production management, archaeology, economics, energy, and defense. Papers in multi-agent systems, knowledge management, neural networks, knowledge discovery, data and text mining, multimedia mining, and genetic algorithms will also be published in the journal.
The history of artificial neural networks ANN began with Warren McCulloch and Walter Pitts  who created a computational model for neural networks based on algorithms called threshold logic. This model paved the way for research to split into two approaches. One approach focused on biological processes while the other focused on the application of neural networks to artificial intelligence.
Expert System is an interactive and reliable computer-based decision-making system which uses both facts and heuristics to solve complex decision-making problems. It is considered at the highest level of human intelligence and expertise. The purpose of an expert system is to solve the most complex issues in a specific domain. The Expert System in AI can resolve many issues which generally would require a human expert. It is based on knowledge acquired from an expert.
Collective intelligence Collective action Self-organized criticality Herd mentality Phase transition Agent-based modelling Synchronization Ant colony optimization Particle swarm optimization Swarm behaviour. Evolutionary computation Genetic algorithms Genetic programming Artificial life Machine learning Evolutionary developmental biology Artificial intelligence Evolutionary robotics. Reaction—diffusion systems Partial differential equations Dissipative structures Percolation Cellular automata Spatial ecology Self-replication.
and store the outputs into the EMR systems [7,18]. Knowledge acquisition: before designing an expert system,. the experts must be identif.