Expert Systems
Introduction
Expert systems are computer programs that leverage artificial intelligence methods to solve complex problems within specific domains, typically requiring human expertise. The first expert system was developed in 1965 by Edward Feigenbaum and Joshua Lederberg at Stanford University in California, USA. Dendral, as their expert system was later known, was designed to analyze chemical compounds. Today, expert systems have commercial applications in fields such as medical diagnosis, petroleum engineering, and financial investing.
To accomplish tasks that require intelligence, expert systems rely on two key components: a knowledge base and an inference engine. The knowledge base is an organized collection of facts about the system's domain, while the inference engine interprets and evaluates these facts to provide answers. Typical tasks for expert systems include classification, diagnosis, monitoring, design, scheduling, and planning for specialized activities. With their ability to analyze complex information and provide expert-level solutions, expert systems have revolutionized numerous industries and continue to play a vital role in the field of artificial intelligence. [1]
An introduction to knowledge representation via rules and expert systems:
Components of Expert Systems
Knowledge Base
The knowledge base is the foundation of an expert system, containing a set of rules, facts, and heuristics that represent the knowledge of a human expert in a specific domain. The domain expert with the required expertise and experience creates the knowledge base, which is then encoded in a formal language that the expert system can understand. This comprehensive collection of knowledge serves as the basis for the expert system's decision-making and problem-solving abilities, making it an essential component of the system's functionality.
Inference/rules engine
The inference engine is responsible for reasoning and decision-making within an expert system. It applies the rules and procedures defined in the knowledge base to input data, generating the required output through a process of deduction and reasoning. The inference engine utilizes various techniques, such as backward and forward chaining, to draw conclusions from the knowledge base and provide expert-level solutions to complex problems.
User Interface
The user interface serves as the point of interaction between the user and the expert system, presenting the questions, options, and recommendations generated by the inference engine in a user-friendly format. The user interface can be either text-based or graphical, depending on the specific application, and is designed to facilitate effective communication and understanding between the user and the expert system.
How it works
Following the analysis of input data, the inference engine utilizes the rules and heuristics stored in the knowledge base to generate conclusions and recommendations. To enhance user understanding, the explanation facility provides a detailed explanation of the reasoning process, outlining how the expert system arrived at its conclusion.
Expert systems gather and organize information and facts within a knowledge base, integrating them with an inference engine. The inference engine uses two methods to acquire information from the knowledge base: forward chaining and backward chaining.
In forward chaining, the expert system reads and processes a set of facts to logically predict what will happen next, such as predicting stock market movements.
In backward chaining, the system evaluates a set of information backward to reach a logical conclusion about a goal, such as in a medical diagnosis system.
Types of Expert Systems
Rule-based Expert Systems
Rule-based expert systems are the most prevalent type of expert system, utilizing a set of rules to reason about a problem and provide solutions or recommendations. These rules are created by human experts and are organized within a knowledge base.
Fuzzy Logic Expert Systems
Fuzzy logic expert systems utilize fuzzy logic to handle uncertainty and imprecision within data, allowing for degrees of truth rather than the traditional binary (true or false) approach. These systems are applied in product recommendation systems and image recognition applications.
Knowledge-based Expert Systems
Knowledge-based expert systems rely on a knowledge base that contains facts and rules about a specific domain, designed to mimic the problem-solving capabilities of human experts. These systems utilize a knowledge inference engine to explain the problem and provide solutions.
Utilization of Expert Systems
Expert systems have numerous applications across various industries, including healthcare, finance, engineering, and many others. Some examples include:
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Medical Diagnosis: Expert systems can assist medical professionals in diagnosing diseases, analyzing symptoms, and providing treatment options based on medical history, symptoms, and test results
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Financial Planning: Expert systems can help with financial planning, including investment strategy, retirement planning, and tax planning, based on a user's financial situation and goals
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Quality Control: Expert systems can identify defects or quality concerns by analyzing production data or testing results
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Customer Support: Expert systems can help customers troubleshoot technical issues or provide product-related advice based on a user's queries or inputs
Use Case
BalanceAI Risk - Comprehensive toolkit tailored to handle various aspects of risk assessment, decision-making, and strategy optimization.
Built as a Python module enriched by CLIPS expert systems programming language, it can empower traders and financial professionals to design robust risk management strategies, optimize their trading performance, and navigate the complexities of financial markets with greater confidence and efficiency.
More information here: https://medium.com/@balancedao/ai-expert-system-to-boost-trading-bd0f871f603e