Introduction To Expert Systems

Introduction

An expert is a person who is well versed in a particular domain such as robotics, linguistics, medicine, finance, etc. The knowledge required for solving a given problem is called knowledge domain. A typical expert must be in a position to advise on a particular matter related to his area of expertise. He should be able to answer queries from his clients. For example, a physician should be able to answer queries such as which medicine should be given to a diseased person, what is the dosage of medicine for a particular disease, what kind of food a person suffering from diabetics should take, and so on. He need not answer queries on linguistics, robotics, or finance as these are not related to his domain. Why not a computer system does his job? The answer lies in expert system.

What are expert systems?

Expert systems are artificial intelligence programs capable of doing the functions of a typical human expert. A medical expert system, for example, should have knowledge on various kinds of patients, disorders, symptoms, medicines, etc. Knowledge base and inference engine are the two major components of an expert system. A knowledge engineer is employed to develop the required knowledge by talking to a human expert and gather as much knowledge as possible and then store it in a knowledge base. Knowledge can also be learned automatically from other sources using machine learning techniques. A user interface is used to get queries from the user. Later these queries are given to the inference engine which then makes inferences with the available rules and the knowledge base and sends the response or advice. Expert system uses an explanation facility to explain the user how it arrives at the conclusion. There are many products available in the market. MYCIN, for example, is a medical expert system and ELIZA for human-computer interaction.

Advantages and disadvantages

Computers, naturally have many advantages over human beings. For example, computers do not get exhausted, do not get fed up, work faster, do not cost more, and can work efficiently. So an expert system can obviously take these advantages for granted. On the other hand they struggle to match with humans in intelligence and commonsense. Making knowledge base on par with that of humans is a tedious job.

Overview on machine learning

What is machine learning

As the name implies machine learning means making the computer able to learn on
its own. It is one of the most active research area within the purview of artificial intelligence.

Technically it is defined as computational methods using the past information available to improve performance with practice and to acquire knowledge automatically.

Machine learning involves designing efficient and accurate prediction algorithms. It uses induction as a way of thinking. In fact it relies on induction process to a great extent. The algorithm designed for this purpose gets labelled training examples and produces the result in the form of prediction rule.

As the complexity, variety and size of machine learning models increases it is required to make use of optimization approaches having great availability and theoretical properties.

Need for machine learning

Learning is a central feature in intelligent systems. It is not possible to build an intelligent system without having a learning module. It also contributes to developing mechanisms for cognition, perception and action.

Types of machine learning

There are two kinds of learning: Supervised and unsupervised. Supervised approach aims to deduct input-output relations based on input-output samples. Once this relation is learned, it is easy to predict output values for unknown input points. Unsupervised approach on the other hand does not accept output values as training samples. It actually depends on the situation and aims to extract relevant information from the given data.

Goals

The main goal is to design general purpose algorithms. These algorithms should be efficient and should consider amount of data required. Moreover they should be applicable to wide variety of problems.

The result of machine learning process should be a prediction rule that makes predictions as accurate as possible. Human experts should be able to understand these predictions easily.

Applications

Applications of machine learning include classifying text, language processing, speech recognition, computational biology, games, diagnosis in medical field, computer vision, data mining, robot control and so on.