Expert Systems and Artificial Intelligence

Expert Systems be calculating machine programmes that are derived from a branch of reckoner science research called factitious Intelligence (AI). AIs scientific goal is to understand intelligence by create computing machine programs that exhibit intelligent behavior. It is concerned with the concepts and methods of typic inference, or reason, by a computer, and how the cognition used to make those inferences will be stand for inside the machine. Of course, the term intelligence covers many cognitive skills, including the ability to run railway line of works, learn, and understand language AI addresses all of those.But virtually onward motion to date in AI has been made in the area of chore solving &8212 concepts and methods for building programs that reason about problems rather than calculate a solution. AI programs that achieve beneficial-level competence in solving problems in confinement areas by bringing to bear a body of experience about specific tasks are called noesis- motifd or expert systems. Often, the term expert systems is mute for programs whose knowledge motif contains the knowledge used by human experts, in contrast to knowledge gathered from textbooks or non-experts.More often than not, the ii terms, expert systems (ES) and knowledge- ground systems (KBS), are used synonymously. Taken together, they meet the most general type of AI application. The area of human intellectual endeavor to be captured in an expert system is called the task domain. Task refers to some goal-oriented, problem-solving activity. sector refers to the area within which the task is being per physical bodyed. Typical tasks are diagnosis, planning, scheduling, physique and design. An example of a task domain is aircraft crew scheduling, discussed in Chapter 2. twist an expert system is known as knowledge prepareing and its practitioners are called knowledge engineers. The knowledge engineer essential make sure that the computer has all the kno wledge needed to solve a problem. The knowledge engineer must choose one or to a greater extent forms in which to represent the required knowledge as symbol patterns in the memory of the computer &8212 that is, he (or she) must choose a knowledge representation. He must also ensure that the computer can use the knowledge efficiently by selecting from a handful of reasoning methods. The practice of knowledge engineering science is described later.We first describe the components of expert systems. The Building Blocks of Expert Systems all(prenominal) expert system consists of two principal move the knowledge base and the reasoning, or inference, engine. The knowledge base of expert systems contains both factual and heuristic rule knowledge. Factual knowledge is that knowledge of the task domain that is widely shared, typically found in textbooks or journals, and earthyly agreed upon by those breaked in the particular field. Heuristic knowledge is the less rigorous, more expe riential, more judgmental knowledge of performance.In contrast to factual knowledge, heuristic knowledge is rarely discussed, and is largely individua contestationic. It is the knowledge of good practice, good judgment, and plausible reasoning in the field. It is the knowledge that underlies the art of good guessing. Knowledge representation formalizes and organizes the knowledge. star widely used representation is the production rule, or simply rule. A rule consists of an IF part and a hence part (also called a condition and an action). The IF part lists a touch on of conditions in some logical combination.The gear up of knowledge represented by the production rule is relevant to the line of reasoning being developed if the IF part of the rule is satisfied consequently, the THEN part can be concluded, or its problem-solving action taken. Expert systems whose knowledge is represented in rule form are called rule-based systems. An opposite widely used representation, called the unit (also known as frame, schema, or list structure) is based upon a more passive view of knowledge. The unit is an assemblage of associated symbolic knowledge about an entity to be represented.Typically, a unit consists of a list of properties of the entity and associated value for those properties. Since every task domain consists of many entities that stand in various relations, the properties can also be used to specify relations, and the values of these properties are the names of other units that are linked according to the relations. whiz unit can also represent knowledge that is a additional case of another unit, or some units can be parts of another unit. The problem-solving model, or paradigm, organizes and controls the steps taken to solve the problem.One common but mightinessful paradigm involves chaining of IF-THEN rules to form a line of reasoning. If the chaining starts from a set of conditions and moves toward some conclusion, the method is called forward chainin g. If the conclusion is known (for example, a goal to be achieved) but the path to that conclusion is not known, thusly reasoning backwards is called for, and the method is backward chaining. These problem-solving methods are built into program modules called inference engines or inference procedures that manipulate and use knowledge in the knowledge base to form a line of reasoning.The knowledge base an expert uses is what he learned at school, from colleagues, and from years of experience. Presumably the more experience he has, the larger his store of knowledge. Knowledge allows him to interpret the learning in his databases to advantage in diagnosis, design, and analysis. Though an expert system consists to begin with of a knowledge base and an inference engine, a couple of other features are worth mentioning reasoning with misgiving, and explanation of the line of reasoning. Knowledge is near always incomplete and uncertain.To deal with uncertain knowledge, a rule may hav e associated with it a confidence factor or a weight. The set of methods for using uncertain knowledge in combination with uncertain data in the reasoning process is called reasoning with uncertainty. An important subclass of methods for reasoning with uncertainty is called fuzzy logic, and the systems that use them are known as fuzzy systems. Because an expert system uses uncertain or heuristic knowledge (as we humans do) its credibility is often in question (as is the case with humans).When an answer to a problem is questionable, we tend to want to know the rationale. If the rationale seems plausible, we tend to believe the answer. So it is with expert systems. Most expert systems have the ability to answer questions of the form Why is the answer X? Explanations can be generated by tracing the line of reasoning used by the inference engine (Feigenbaum, McCorduck et al. 1988). The most important ingredient in any expert system is knowledge.The power of expert systems resides in the specific, high-quality knowledge they contain about task domains. AI researchers will continue to explore and add to the current repertoire of knowledge representation and reasoning methods. But in knowledge resides the power. Because of the importance of knowledge in expert systems and because the current knowledge acquisition method is slack off and tedious, much of the future of expert systems depends on breaking the knowledge acquisition bottleneck and in codifying and representing a large knowledge infrastructure.