The study of AI itself serves as means of understanding human intelligence. The reasons stated are as follows:
- The use of computers demands a clear statement of the problem and clear strategy for solutions and this requires a clear understanding about the human thinking process.
- Computer models force precision. Implementing a theory uncovers conceptual mistakes and oversights that ordinarily escape the notice of even the most meticulous researcher.
- Computer implementations qualify task requirements. Once a program performs a task, upper bound statements can be made about how much information processing the task requires.
- It is usually simple to deprive a computer program of some piece of knowledge in order to test how important that information really is.
2) What are the components of Expert Systems?
In general, expert systems are composed of basic components such as:
i) a user interface: To facilitate user interaction
ii) a knowledge base: The facts or knowledge based upon which the ES makes decisions
iii) an inference mechanism: the reasoning engines built according to heuristics reasoning or facts.
3) What are the two types of knowledge contained in Expert Systems?
The knowledge base of expert systems contains both factual and heuristic knowledge:
Factual knowledge is that knowledge of the task domain that is widely shared, typically found in textbooks or journals, and commonly agreed upon by those knowledgeable in the particular field.
Heuristic knowledge is the less rigorous, more experimental, more judgmental knowledge of performance. In contrast to factual knowledge, heuristic knowledge is largely based upon past experiences and knowledge gained. For instance the knowledge that, if the same pattern of events
take place then the same conclusion could be expected. It is the knowledge built from ‘thumb rules’.
4) What is Knowledge Engineering?
For computer systems to behave as an ‘expert system’, expertise has to be built into the system. Knowledge Engineering refers to techniques of building knowledge into expert systems. It involves understanding knowledge in specific domains and representing it in computer in a syntax that is computer process-able.
5) What is DSS?
Decision support systems (DSS) are human–computer decision-making systems to support managerial judgments, and intuitions to solve managerial problems by providing necessary information, generating, evaluating and suggesting decision alternatives. Most organisational problems need a combination of quantitative and qualitative data processing. Decision support systems (DSS) are a subset of computer-based information systems (CBIS). DSS maybe part Management support systems or also expert systems and executive information systems.
AI : Artificial Intelligence – deals with techniques of making intelligent systems
CBIS : Computer Based Information Systems
Inference Engine : A component of expert systems that makes decisions based upon reasoning built into the systems through factual or ruled based methods
KBS : Knowledge Based Systems, also known as Expert System and Knowledge Based
Computer Systems (KBCS)
Knowledge Base : Facts or knowledge of a specific domain that is built into an expert system based on which the system can make decisions
Knowledge Engineering : Techniques of building knowledge into expert systems. That is understanding knowledge in specific domains and representing it in computer in a syntax that is computer processible