What are Expert Systems?
The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.
Characteristics of Expert Systems
- High performance
- Highly responsive
Capabilities of Expert Systems
The expert systems are capable of
- Instructing and assisting human in decision making
- Deriving a solution
- Interpreting input
- Predicting results
- Justifying the conclusion
- Suggesting alternative options to a problem
Components of Expert Systems
The components of ES include −
- Knowledge Base
- Inference Engine
- User Interface
Let us see them one by one briefly −
It contains domain-specific and high-quality knowledge. Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon the collection of highly accurate knowledge.
What is Knowledge?
The data is collection of facts. The information is organized as data and facts about the task domain. Data, information, and past experience combined together are termed as knowledge.
Components of Knowledge Base
The knowledge base of an ES is a store of both, factual and heuristic knowledge.
Factual Knowledge − It is the information widely accepted by the Knowledge Engineers and scholars in the task domain.
Heuristic Knowledge − It is about practice, accurate judgement, one’s ability of evaluation and guessing.
It is the method used to organize and formalize the knowledge in the knowledge base. It is in the form of IF-THEN-ELSE rules.
The success of any expert system majorly depends on the quality, completeness and accuracy of the information stored in the knowledge base.
The knowledge base is formed by readings from various experts, scholars and the Knowledge Engineers. The knowledge engineer is a person with the qualities of empathy, quick learning and case analyzing skills.
He acquires information from subject expert by recording, interviewing and observing at work.
Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless solution.
In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.
In case of rule based ES, it −
- Applies rules repeatedly to the facts, which are obtained from earlier rule application.
- Adds new knowledge into the knowledge base if required.
- Resolves rules when multiple rules are applicable to a particular case.
To recommend a solution, the Inference Engine uses the following strategies −
- Forward Chaining -What can happen next?
- Backward Chaining - Why this happened?
User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert in Artificial Intelligence. It explains how the ES has arrived at a particular recommendation. The explanation may appear in the following forms −
- Natural language displayed on screen.
- Verbal narrations in natural language.
- Listing of rule numbers displayed on the screen.
The user interface makes it easy to trace the credibility of the deductions.
Expert Systems Limitations
No technology can offer easy and complete solution. Large systems are costly, require significant development time and computer resources. ESs have their limitations which include −
- Limitations of the technology
- Difficult knowledge acquisition
- ES are difficult to maintain
- High development costs
Prominent Expert Systems
- MYCIN – used to diagnose infectious blood diseases and recommend antibiotics.
- DENDRAL – embedded a chemist’s knowledge of mass spectrometry rules to use in analysis.
- CADUCEUS – used to analyze blood-borne infectious bacteria
- CLIPS and Prolog programming languages are both used in expert systems.
- The Age of Empire game uses CLIPS to control its AI.
A frame based production system. Uses a database of over 1,000,000 facts and rules, surround all fields of human knowledge.
CYC can answer questions about all kinds of knowledge in its database and can even understand analogies and other complex relations.