AI Problem

Al Problem

Much of the early working AI focused on formal tasks, such as game playing and theorem proving. For example chess playing, logic theorist was an early attempt to prove mathematical theorems. Game playing and theorem proving share the property that people who do them well are considered to be displaying intelligence.

Despite this it appeared that computers could perform well at those tasks by being fast at exploring a large number of solution paths and then selecting the best one. But no computer is fast enough to overcome the combinatorial explosion generated by most problems.

AI focusing on the sort of problem solving we do every day for instance, when we decide to get to work in the morning, often called commonsense reasoning. In investigating this sort of reasoning Newell, Shaw, and Simon built the General Problem Solver (GPS), which they applied to several commonsense tasks as well performing symbolic manipulations of logical expression. However no attempt was made to create a program with a large amount of knowledge about a particular problem domain. Only quite simple tasks were selected.

As AI research progressed and techniques for handling larger amounts of world knowledge were developed in dealing with problem solving in specialized domains such as medical diagnosis and chemical analysis.

Perception (vision and speech) is another area for AI problems. Natural language understanding and problem solving in specialized domain are other areas related to AI problems. The problem of understanding spoken language is perceptual problem and is hard to solve from the fact that it is more analog related than digital related. Many people can perform one or may be more specialized tasks in which carefully, acquired expertise is necessary. Examples of such as tasks include engineering design, scientific discovery, medical diagnosis, and financial planning. Programs that can solve problems in these domains also fall under the aegis of Artificial Intelligence.

The tasks that are targets of works in AI can be categorized as follows:

1. Mundane tasks - Perception (Vision and Speech), Natural language

(Understanding, Generation, Translation, Commonsense reasoning, Robot control)

2. Formal tasks - Games (Chess, etc.), Mathematics (Geometry, Logic, Integral calculus, etc.)

3. Expert tasks - Engineering (Design, Fault finding, Manufacturing planning), Scientific analysis, Medical diagnosis, Financial analysis

A person who knows how to perform tasks from several of the categories shown in above list learn the necessary skills in a standard order. First perceptual, linguistic, and commonsense skills are learned. Later expert skills such as engineering, medicine, or finance are acquired. Earlier skills are easier and thus more amenable to computerized duplication than the later, more specialized one. For this reason much of the initial work in Al work was concentrated in those early areas.

The problems areas where now AI is flourishing most as a practical discipline are primarily the domains that require only specialized expertise without the assistance of commonsense knowledge. Expert systems (AI programs) now are up for day-to-day tasks that aim at solving part, or perhaps all, of practical, significant problem that previously required high human expertise.

When one is building a expert system, following questions need to be considered before one can progress further:

• What are the underlying assumptions about intelligence?

• What kinds of techniques will be useful for solving AI problems?

• At what level if at all can human intelligence be modelled?

• When will it be realised when an intelligent program has been built?