Expert systems in Artificial Intelligence are a prominent domain for research in AI. It was initially introduced by researchers at Stanford University and was developed to solve complex problems in a particular domain. This blog on Expert Systems in Artificial Intelligence will cover the following topics. Show
Introduction to Expert Systems in Artificial IntelligenceAn Expert system is a domain in which Artificial Intelligence stimulates the behavior and judgment of a human or an organization containing experts. It acquires relevant knowledge from its knowledge base and interprets it as per the user’s problem. The data in the knowledge base is essentially added by humans who are experts in a particular domain. However, the software is used by non-experts to gain information. It is used in various medical diagnoses, accounting, coding, gaming, and more areas. Breaking down an expert system essentially is an AI software that uses knowledge stored in a knowledge base to solve problems. This usually requires a human expert; thus, it aims at preserving human expert knowledge in its knowledge base. Hence, expert systems are computer applications developed to solve complex problems in a particular domain at an extraordinary level of human intelligence and expertise. The Three C’s of ESCharacteristics of Expert Systems
Capabilities of Expert Systems The expert systems are capable of a number of actions, including:
Components/ Architecture of Expert Systems There are 5 Components of expert systems:
Strategies Used By The Inference EngineThe Inference Engine uses the following strategies to recommend solutions:
Forward ChainingWith this strategy, an expert system is able to answer the question, “What can happen next?” By following a chain of conditions and derivations, the expert system deduces the outcome after considering all facts and rules. It then sorts them before arriving at a conclusion in terms of a suitable solution. This strategy is followed while working on the conclusion, result, or effect. For example, predicting how the share market prediction of share market will react to the changes in the interest rates. Backward ChainingAn expert system uses backward chaining to answer the question, “Why did this happen?” Depending upon what has already occurred, the inference engine tries to identify the conditions that could have happened in the past to trigger the final result. This strategy is used to find the cause or the reason behind something happening. For example, the diagnosis of different types of cancer in humans. Types of Expert System TechnologyExpert systems can be classified into five categories. There are several types of expert systems, including rule-based, frame-based, fuzzy, neural, and neuro-fuzzy. Simple expert systems that describe knowledge as a collection of rules are called rule-based expert systems. Multi-valued logic is another name for fuzzy logic expert systems, which distinguish between class members and non-members when solving problems. Frames are used in a frame-based expert system to store and represent knowledge. By storing neural knowledge as weights in neurons, a neural expert system replaces a conventional knowledge base with neural knowledge. The last method is a neuro-fuzzy system, which combines parallel computation, learning, knowledge representation, and explanatory skills. ES technologies come in various levels, they are:
Steps to Develop an Expert SystemThere are 6 steps involved in the development of an expert system. Expert Systems ExamplesThere are numerous examples of expert systems. Some of them are:
Traditional Systems versus Expert SystemsA key distinction between the traditional system as opposed to the expert system is the way in which the problem-related expertise is coded. Essentially, in conventional applications, the problem expertise is encoded in both programs as well as data structures. On the other hand, in expert systems, the approach of the problem-related expertise is encoded in data structures only. Moreover, the use of knowledge in expert systems is vital. However, traditional systems use data more efficiently than expert systems. One of the biggest limitations of conventional systems is that they cannot explain a problem’s conclusion. That is because these systems try to solve problems in a straightforward manner. However, expert systems can provide explanations and simplify the understanding of a particular conclusion. Generally, an expert system uses symbolic representations to perform computations. On the contrary, conventional systems are incapable of expressing these terms. They only simplify the problems without being able to answer the “how” and “why” questions. Moreover, problem-solving tools are present in expert systems as opposed to traditional ones; hence, various problems are often entirely solved by the system’s experts. Human System Vs. Expert SystemHuman ExpertsExpert SystemsPerishable and unpredictable in nature Permanent and consistent in natureDifficult to transfer and document data Easy to transfer and document dataHuman expert resources are expensiveExpert Systems are cost-effective SystemsApplications of Expert SystemsApplications RoleDesign DomainCamera lens design automobile designMedical DomainDiagnosis Systems to deduce the cause of disease from observed dataConduction medical operations on humans.Monitoring systemsComparing data continuously with observed systems Process Control SystemsControlling physical processes based on the monitoring.Knowledge DomainFinding faults in vehicles or computers.Commerce Detection of possible fraud Suspicious transactions Stock market trading Airline scheduling, Cargo scheduling.Advantages of Expert Systems
Limitations of Expert SystemsIt is evident that no technology is entirely perfect for offering easy and complete solutions. Larger systems are not only expensive but also require a significant amount of development time and computer resources. Limitations of ES include:
Expert systems have managed to evolve to the extent that they have stirred various debates about the fate of humanity in the face of such intelligence. Considering that Expert systems were among the first truly successful forms of artificial intelligence (AI) software, it might just be the future of technology. If your interests are intrigued by the Expert systems in AI, do check out Great Learning’s AI courses here. Frequently Asked QuestionsWhat are the 5 components of an expert system? The five components of an expert system are: Where is an expert system used in AI? An expert system is used in many areas of AI, such as the healthcare industry for medical diagnosis, programming, games, and much more. An expert system stores most of its knowledge in a knowledge base to address issues that mostly is a human job What are the types of expert systems? There are five primary types of expert systems: rule-based expert systems, frame-based expert systems, fuzzy expert systems, neural expert systems, and neuro-fuzzy expert systems. What are the limitations of expert systems?Limitations of Expert Systems. Difficult knowledge acquisition.. Maintenance costs.. Development costs.. Adheres only to specific domains.. Requires constant manual updates; it cannot learn by itself.. It is incapable of providing logic behind the decisions.. Can expert systems work with incomplete knowledge?An expert system can work with the available information whether it is incomplete or uncertain. In this sense, an expert system can provide some results even under the constraints of limited or uncertain information. A conventional program would be severely limited under such constraints.
Where does an expert system acquire knowledge?Experts. Although an ES knowledge base can be developed from a range of sources such as textbooks, manuals and simulation models, the knowledge at the core of a well developed ES comes from human experts.
Why are expert systems important for companies to capture knowledge?Expert systems capture scarce expert knowledge and render it archival. This is an advantage when losing the expert would be a significant loss to the organization. Distributing the expert knowledge enhances employee productivity by offering necessary assistance to make the best decision.
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