Artificial Intelligence - What Are Expert Systems?


Expert systems are used to solve issues that would normally be addressed by humans.

In the early decades of artificial intelligence research, they emerged as one of the most promising application strategies.

The core concept is to convert an expert's knowledge into a computer-based knowledge system.

Dan Patterson, a statistician and computer scientist at the University of Texas in El Paso, differentiates various properties of expert systems:

• They make decisions based on knowledge rather than facts.

• The task of representing heuristic knowledge in expert systems is daunting.

• Knowledge and the program are generally separated so that the same program can operate on different knowledge bases.

• Expert systems should be able to explain their decisions, represent knowledge symbolically, and have and use meta knowledge, that is, knowledge about knowledge.

(Patterson, et al., 2008) Expert systems generally often reflect domain-specific knowledge.

The subject of medical research was a frequent test application for expert systems.

Expert systems were created as a tool to assist medical doctors in their work.

Symptoms were usually communicated by the patient in the form of replies to inquiries.

Based on its knowledge base, the system would next attempt to identify the ailment and, in certain cases, recommend relevant remedies.

MYCIN, a Stanford University-developed expert system for detecting bacterial infections and blood disorders, is one example.

Another well-known application in the realm of engineering and engineering design tries to capture the heuristic knowledge of the design process in the design of motors and generators.

The expert system assists in the initial design phase, when choices like as the number of poles, whether to use AC or DC, and so on are made (Hoole et al. 2003).

The knowledge base and the inference engine are the two components that make up the core framework of expert systems.

The inference engine utilizes the knowledge base to make choices, whereas the knowledge base holds the expert's expertise.

In this way, the knowledge is isolated from the software that manipulates it.

Knowledge must first be gathered, then comprehended, categorized, and stored in order to create expert systems.

It is retrieved to answer issues depending on predetermined criteria.

The four main processes in the design of an expert system, according to Thomson Reuters chief scientist Peter Jackson, are obtaining information, representing that knowledge, directing reasoning via an inference engine, and explaining the expert system's answer (Jackson 1999).

The expert system's largest issue was acquiring domain knowledge.

Human specialists may be challenging to obtain information from.

Many variables contribute to the difficulty of acquiring knowledge, but the complexity of encoding heuristic and experienced information is perhaps the most important.

The knowledge acquisition process is divided into five phases, according to Hayes-Roth et al. (1983).

Identification, or recognizing the problem and the data that must be used to arrive at a solution; conceptualization, or comprehending the key concepts and relationships between the data; formalization, or comprehending the relevant search space; implementation, or converting formalized knowledge into a software program; and testing the rules for completeness and accuracy are among them.

  • Production (rule-based) or non-production systems may be used to represent domain knowledge.
  • In rule-based systems, knowledge is represented by rules in the form of IF THEN-ELSE expressions.

The inference process is carried out by iteratively going over the rules, either through a forward or backward chaining technique.

  • Forward chaining asks what would happen next if the condition and rules were known to be true. Going from a goal to the rules we know to be true, backward chaining asks why this occurred.
  • Forward chaining is defined as when the left side of the rule is assessed first, that is, when the conditions are verified first and the rules are performed left to right (also known as data-driven inference).
  • Backward chaining occurs when the rules are evaluated from the right side, that is, when the outcomes are verified first (also known as goal-driven inference).
  • CLIPS, a public domain example of an expert system tool that implements the forward chaining method, was created at NASA's Johnson Space Center. MYCIN is an expert system that works backwards.

Associative/semantic networks, frame representations, decision trees, and neural networks may be used in expert system designs based on nonproduction architectures.

Nodes make form an associative/semantic network, which may be used to represent hierarchical knowledge. 

  • An example of a system based on an associative network is CASNET.
  • The most well-known use of CASNET was the development of an expert system for glaucoma diagnosis and therapy.

Frames are structured sets of closely related knowledge in frame architectures.

  • A frame-based architecture is an example of PIP (Present Illness Program).
  • MIT and Tufts-New England Clinical Center developed PIP to generate hypotheses regarding renal illness.

Top-down knowledge is represented via decision tree structures.

Blackboard system designs are complex systems in which the inference process's direction may be changed during runtime.

A blackboard system architecture may be seen in DARPA's HEARSAY domain independent expert system.

  • Knowledge is spread throughout a neural network in the form of nodes in neural network topologies.
  • Case-based reasoning is attempting to examine and find answers for a problem using previously solved examples.
  • A loose connection may be formed between case-based reasoning and judicial law, in which the decision of a comparable but previous case is used to solve a current legal matter.
  • Case-based reasoning is often implemented as a frame, which necessitates a more involved matching and retrieval procedure.

There are three options for manually constructing the knowledge base.

  • Knowledge may be elicited via an interview with a computer using interactive tools. This technique is shown by the computer-graphics-based OPAL software, which enabled clinicians with no prior medical training to construct expert medical knowledge bases for the care of cancer patients.
  • Text scanning algorithms that read books into memory are a second alternative to human knowledge base creation.
  • Machine learning algorithms that build competence on their own, with or without supervision from a human expert, are a third alternative still under development.

DENDRAL, a project started at Stanford University in 1965, is an early example of a machine learning architecture project.

DENDRAL was created in order to study the molecular structure of organic molecules.

  • While DENDRAL followed a set of rules to complete its work, META-DENDRAL created its own rules.
  • META-DENDRAL chose the important data points to observe with the aid of a human chemist.

Expert systems may be created in a variety of methods.

  • User-friendly graphical user interfaces are used in interactive development environments to assist programmers as they code.
  • Special languages may be used in the construction of expert systems.
  • Prolog (Logic Programming) and LISP are two of the most common options (List Programming).
  • Because Prolog is built on predicate logic, it belongs to the logic programming paradigm.
  • One of the first programming languages for artificial intelligence applications was LISP.

Expert system shells are often used by programmers.

A shell provides a platform for knowledge to be programmed into the system.

  • The shell is a layer without a knowledge basis, as the name indicates.
  • The Java Expert System Shell (JESS) is a strong expert shell built in Java.

Many efforts have been made to blend disparate paradigms to create hybrid systems.

  • Object-oriented programming seeks to combine logic-based and object-oriented systems.
  • Object orientation, despite its lack of a rigorous mathematical basis, is very useful in modeling real-world circumstances.

  • Knowledge is represented as objects that encompass both the data and the ways for working with it.
  • Object-oriented systems are more accurate models of real-world things than procedural programming.
  • The Object Inference Knowledge Specification Language (OI-KSL) is one way (Mascrenghe et al. 2002).

Although other languages, such as Visual Prolog, have merged object-oriented programming, OI-KSL takes a different approach.

Backtracking in Visual Prolog occurs inside the objects; that is, the methods backtracked.

Backtracking is taken to a whole new level in OI KSL, with the item itself being backtracked.

To cope with uncertainties in the given data, probability theory, heuristics, and fuzzy logic are sometimes utilized.

A fuzzy electric lighting system was one example of a Prolog implementation of fuzzy logic, in which the quantity of natural light influenced the voltage that flowed to the electric bulb (Mascrenghe 2002).

This allowed the system to reason in the face of uncertainty and with little data.

Interest in expert systems started to wane in the late 1990s, owing in part to unrealistic expectations for the technology and the expensive cost of upkeep.

Expert systems were unable to deliver on their promises.

Even today, technology generated in expert systems research is used in various fields like data science, chatbots, and machine intelligence.

  • Expert systems are designed to capture the collective knowledge that mankind has accumulated through millennia of learning, experience, and practice.

Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Clinical Decision Support Systems; Computer-Assisted Diagnosis; DENDRAL; Expert Systems.

Further Reading:

Hayes-Roth, Frederick, Donald A. Waterman, and Douglas B. Lenat, eds. 1983. Building Expert Systems. Teknowledge Series in Knowledge Engineering, vol. 1. Reading, MA: Addison Wesley.

Hoole, S. R. H., A. Mascrenghe, K. Navukkarasu, and K. Sivasubramaniam. 2003. “An Expert Design Environment for Electrical Devices and Its Engineering Assistant.” IEEE Transactions on Magnetics 39, no. 3 (May): 1693–96.

Jackson, Peter. 1999. Introduction to Expert Systems. Third edition. Reading, MA: Addison-Wesley.

Mascrenghe, A. 2002. “The Fuzzy Electric Bulb: An Introduction to Fuzzy Logic with Sample Implementation.” PC AI 16, no. 4 (July–August): 33–37.

Mascrenghe, A., S. R. H. Hoole, and K. Navukkarasu. 2002. “Prototype for a New Electromagnetic Knowledge Specification Language.” In CEFC Digest. Perugia, Italy: IEEE.

Patterson, Dan W. 2008. Introduction to Artificial Intelligence and Expert Systems. New Delhi, India: PHI Learning.

Rich, Elaine, Kevin Knight, and Shivashankar B. Nair. 2009. Artificial Intelligence. New Delhi, India: Tata McGraw-Hill.

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