Expert Systems are knowledge-based intelligent reasoning systems that involve research into knowledge acquisition, knowledge bases, reasoning control mechanisms, and intelligent human-computer interfaces. They integrate artificial intelligence and domain knowledge, marking a milestone as expert systems have rapidly developed and been widely applied in various fields, significantly advancing the trend towards intelligence in practical applications.
Expert systems apply artificial intelligence technology to reason based on specialized domain knowledge provided by one or more human experts, simulating the decision-making process of human experts to solve complex problems that require expert knowledge. Currently, expert systems are not limited to scientific issues but also have important applications in engineering and business.
During the research on expert systems, it was discovered that expert capabilities have two aspects: first, an expert possesses a large amount of specialized knowledge; second, experts can flexibly apply knowledge based on the environment and objects, and can draw good conclusions from imprecise and incomplete evidence. Through extensive research, expert systems DENDRAL and MYCIN were successively introduced in the 1960s. The famous MYCIN system is a computer system for diagnosing and treating patients with bacterial infections, composed of over 600 rules, with uncertain reasoning rules, and MYCIN is capable of explaining its reasoning process. This early expert system already had practical working capabilities, requiring years of training for humans to be competent. Although MYCIN’s scope of use is limited, it marked a new stage in the development of expert systems.
Later, machine learning systems addressed the learning mechanism of expert systems, allowing them to continuously enrich their knowledge bases, greatly enhancing the applicability of expert systems. With the future development of theoretical foundations and computer hardware for expert systems, their applicability is bound to become stronger. Currently, expert systems are mainly applied in medicine, fault diagnosis, chemistry, computer software and hardware, mathematics, and engineering.
The development of expert systems is also a programming process at this stage. Traditional programming lacks flexibility, and more importantly, it lacks imprecise inference and suitable algorithms. The expression of expert systems is: Knowledge + Reasoning = System, whereas the traditional expression is: Data + Algorithm = Program. Therefore, the structure of expert systems is different from traditional programs. A complete expert system consists of four components: the knowledge base, inference engine, knowledge acquisition component, and explanation interface. The knowledge base stores specialized knowledge provided by experts in relevant fields. The function of the inference engine is to select relevant knowledge from the knowledge base according to certain reasoning strategies and to reason based on the evidence provided by the user until a corresponding conclusion is reached. The inference engine includes two parts: reasoning methods and control strategies. The knowledge acquisition process can be seen as the transfer of a type of specialized knowledge to the knowledge base. The human-computer interface completes the input and output in a user-friendly manner.
Structure of Expert Systems
In a mature expert system, several technologies are extremely critical. First, to facilitate the storage, retrieval, use, and modification of knowledge in computers for reasoning and searching, knowledge representation technology must be highly efficient. Currently, there are mainly production rules, semantic networks, frame representation, predicate logic representation, and new technologies are still being developed; secondly, because it is necessary to simulate human thinking in expert systems, imprecise reasoning methods are essential. Probabilistic algorithms once became the most important method for practical needs, and in recent years, the introduction of fuzzy mathematics has opened new prospects for this field; finally, related to knowledge representation technology and reasoning methods, the quality of search strategies, which simulate human cognitive processes, is also significant for the success of the system. Technologies currently utilized include state-space methods, problem recursion methods, and best-first methods.
In summary, the uniqueness of artificial intelligence systems determines that it is a vibrant field that spans multiple disciplines and is highly dependent on fundamental research. Its development will undoubtedly showcase more charm in the realm of science and technology, making our lives better.
Source: China Science Popularization Expo
