The mainstream development of the field of artificial intelligence is from “Heuristic Programs” (Heuristic program) to “Expert Systems” (Expert System).
1. Heuristic Programs
Heuristic programs are computer programs that simulate human thinking methods and rules, used to simulate and explore human thinking methods and intelligent activities in various problem-solving processes, aiming to improve the level of artificial intelligence in computer applications and assist, extend or enhance people’s problem-solving abilities.
The first famous heuristic program is the “Logic Theory Machine” (Logic Theory Machine), abbreviated as “LT”. Developed by Newell (A.Newell), Simon, and Shaw (J.C.Shaw), it was successfully created in 1956. This is a product of the combination of psychologists and computer experts.
The heuristic program simulates certain thinking methods and rules used by mathematicians in the process of proving mathematical theorems, applying psychological methods to design a psychological experiment called “thinking aloud” to record and analyze the thinking process and intelligent activities, studying and exploring methods and rules such as: problem decomposition, variable substitution, symbol replacement, etc. These rules and relevant mathematical axioms were compiled into a computer program, allowing computers to prove mathematical theorems. Using heuristic programs, 38 mathematical theorems in the second chapter of Whitehead and Russell’s famous work “Principia Mathematica” (1910-1913) were proven, pioneering the use of computers to simulate human high-level intelligent activities and achieving automation of complex mental labor, which is regarded as the true beginning of artificial intelligence.
In 1956, another significant achievement of heuristic programs was the development of a self-learning “checkers program” by Samuel, which simulated Samuel himself and other skilled players’ strategies and methods, able to estimate several moves in advance, accumulating experiences and lessons from games, learning from skilled opponents or through game records, continuously improving the level of artificial intelligence in checkers. Initially, this checkers program could only play as well as Samuel himself. Later, after learning from other masters, in 1959, it defeated its creator. In 1962, it defeated a state champion in checkers in the United States. This research result opened up and promoted research in the field of artificial intelligence regarding “machine games” and “machine learning”.
The representative work of the further development of heuristic programs is the “General Problem Solver” (General Problem Solver), abbreviated as GPS, which was developed by Newell, Simon, and Shaw in 1960. They believed that there are common methods and rules in people’s thinking processes when solving various different problems, which were discovered and confirmed through psychological experiments. Among them, the most active intelligent activities are the analysis of “goals and methods”, that is, to achieve a certain problem-solving goal, what methods need to be employed. Common thinking methods include:
1. Transformation and Decomposition
Transformation is converting one problem into another isomorphic or homomorphic problem, transforming a certain goal into another equivalent or equivalent goal; decomposition is breaking down a complex large problem into simpler small problems, decomposing the overall goal into local sub-goals.
2. Eliminating Differences
This means using appropriate methods to eliminate the differences between the current state and the target state, as well as the differences between interim goals and final goals, such as eliminating the differences on both sides of an expression’s equality.
3. Applying Operators
The method is a collection of various operators, where operators are various operations, calculations, reasoning, actions, tools, means, etc. used to eliminate goal differences.
4. Selection and Matching
In the process of using operators to eliminate differences in problem-solving, for various goals, sub-goals, and relevant methods and operators, selection and matching must be performed to effectively solve problems using appropriate methods.
Based on the above analysis, the heuristic program GPS was compiled, capable of solving 11 different types of problems, enhancing the generality of heuristic programs and expanding the application range of automating mental labor using computers.
In 1960, Chinese-American mathematician Wang Hao proposed a new algorithm for machine theorem proving in propositional logic, utilizing computers to prove over 300 theorems in set theory.
In 1965, Robinson (J.A.Robinson) proposed the “Resolution Principle” of first-order predicate logic, simplifying the decision-making steps and promoting the progress of machine theorem proving based on predicate logic.
In 1977, Chinese mathematician Wu Wenjun proposed a machine theorem proving method for elementary geometry problems, further extending it to elementary differential geometry and non-Euclidean geometry.
2. Expert Systems
In the field of artificial intelligence, the significant shift in scientific research methods and system development strategies regarding how to use machines to simulate human intelligence, especially how to use computers to simulate human thinking, is the emergence of “Expert Systems” (Expert System).
Expert systems are computer systems based on the professional knowledge and work experience of experts, used to solve specialized problems, for example, medical expert systems based on doctors’ knowledge and experience for diagnosing and treating diseases. Expert systems simulate the thinking processes of experts, possessing a level of professional knowledge similar to that of experts and the ability to solve specialized problems. Artificial intelligence expert systems can simulate, extend, and enhance the intelligence of the experts themselves, preserving, disseminating, and aggregating the professional knowledge and valuable experiences of experts from various fields, facilitating mutual communication and widespread application.
The first expert system DENDRAL is a chemical analysis expert system proposed by American scientist Feigenbaum (E.A.Feigenbaum) in 1965 and successfully developed in 1968. It can analyze experimental data from mass spectrometers to infer the molecular structure of unknown compounds, possessing a level of knowledge and analytical ability similar to that of a chemistry expert. The emergence of the chemical analysis expert system DENDRAL marked the birth of a new branch of artificial intelligence – “Expert Systems”.
The medical expert system MYCIN was started in 1971 by Stanford University (Stanford University) Shortliffe (E.H.Shortliffe, 1947-?) and others, was basically completed in 1974, and published in 1976. It has the knowledge and experience of internists and can be used for diagnosing, treating, and consulting services for blood infections. The MYCIN system adopts a system structure of “Knowledge Base” and “Inference Engine”, introducing the concept of “certainty” for non-deterministic knowledge reasoning, able to respond to user inquiries and provide explanations for answers along with estimates of their certainty. MYCIN is a relatively comprehensive and well-structured expert system. Its successful development has provided examples and experiences for the research and development of many other expert systems.
A significant achievement in application is the geological exploration expert system (PROSPECTOR). It was developed by Duda (R.O.Duda) et al. at the Stanford Research Institute (SRI) and can be used for geological survey data analysis, exploring types of ore deposits, their locations, and distributions. It began development in 1976 and was basically completed in 1981, characterized by having knowledge and experience from multiple experts and multiple disciplines. Due to PROSPECTOR’s successful application in aluminum ore exploration, it achieved significant economic and social benefits, attracting widespread attention from the business community and stimulating a “gold rush” in artificial intelligence, leading to investments in the establishment of intelligent industry companies and research on the application of expert system technologies, promoting the transition of artificial intelligence from academic research to technological market development.
In 1977, Feigenbaum proposed the concept of “Knowledge Engineering”, further promoting the development of knowledge-based expert systems and other knowledge engineering systems. For example, in 1979, at the World Conference of the International Joint Conference on Artificial Intelligence, 80% of the papers were related to expert systems.
To improve the design and development efficiency of expert systems and shorten development cycles, various types of expert system development tools were developed. For example, skeleton-type expert system development tools EMYCIN, KAS, etc.; knowledge acquisition assistance tools TEIRESIES, SEEK, etc.; general knowledge representation languages LISP, PROLOG, KRL, OPS5, etc.; and combinatorial development tools AGE, etc.
China’s first expert system was developed by the Control Theory Group of the Institute of Automation, Chinese Academy of Sciences, led by Tu Xuyan and Guo Rongjiang, starting in 1977 and successfully developed in 1979, named “Traditional Chinese Medicine Diagnosis and Treatment Program for Hepatitis”. This is also the world’s first Traditional Chinese Medicine expert system.
Since the 1980s, research and development of expert systems have evolved towards large, multidisciplinary, and comprehensive knowledge engineering systems. For example, the HPP’–80 system was jointly developed by Stanford University and Stanford Research Institute as a large knowledge engineering system. It includes application expert systems in multiple disciplines such as chemical analysis, medical diagnosis, molecular biology, structural mechanics, fault diagnosis, circuit design, etc., along with knowledge engineering tools such as EMYCIN and AGE.
The extensive research, development, and many successful applications of expert systems have, on one hand, promoted the development of methods and technologies in knowledge representation, knowledge reasoning, knowledge acquisition, and knowledge utilization. For instance, in 1980, Nilsson (N.J.Nilsson) published the monograph “Principles of Artificial Intelligence”, discussing the design principles of artificial intelligence systems based on knowledge representation and reasoning using production systems as the basic structure and predicate calculus methods. As a typical knowledge engineering system, expert systems have become one of the most active and productive subfields in artificial intelligence. On the other hand, it has also facilitated the popularization of artificial intelligence, shifting from the exploration of general thinking rules to the utilization of specialized knowledge, transitioning from academic theoretical research to technological market application development, allowing the public to gain a better understanding of the significance and value of artificial intelligence. However, expert systems and knowledge engineering still face challenges such as the “bottleneck” in knowledge acquisition and uncertain common sense reasoning, which need further development.