Concept of AI

UNIT I: PROBLEM SOLVING

Syllabus

Introduction to AI - AI Applications Problem solving agents- search algorithms uninformed search strategies - Heuristic search strategies - Local search and optimization problems - adversarial search - constraint satisfaction problems (CSP). (Chapters - 1, 2, 3, 4, 5, 6)

Chapter 1: Introduction to AI

Concept of Al

Many human mental activities such as developing computer programs, working out mathematics, engaging in common sense reasoning, understanding languages and interpreting it, even driving an automobile are said to demand "intelligence". Several computer systems have been built that can perform tasks such as these. Also there are specially developed computers systems that can diagnose disease, solve quadratic equations, understand human speech and natural language text.

We can say that all such systems possess certain degree of artificial intelligence.

The central point of all such activities and systems is that "How to think" OR rather "How to make system think". The process of thinking has various steps like preceive, understand, predict and manipulate a world that is made up of tiny complex things or situations.

The field of AI not just attempts to understand but also it builds intelligent entities.

Various Definitions of AI

1. AI may be defined as the branch of computer science that is concerned with the automation of intelligent behaviour. (Luger-1993)

2. Systems that thinks like human.

3. The exciting new effort to make computers think ... machines with minds, in the full and literal sense. (Hallgeland-1985)

4. "The automation of activities that we associate with human thinking, activities such as devision making, problem solving, learning ..." (Bellman-1978)

5. Systems that act like humans.

6. "The art of creating machines that perform functions that require intelligence, when performed by people". (Kurzweil - 1990)

7. "The study of how to make computers do things at which, at the moment, people are better". (Rich and Knight - 1991)

8. Systems that think rationally.

9. The study of mental faculties through the use of computational models. (Charniak and McDermott - 1985)

10. "The study of the computations that make it possible to perceive, reason and act". (Winston - 1992)

11. Systems that act rationally

12. "Computational intelligence is the study of the design of intelligent agents"."(Poole et al - 1998)

13. "AI is concerned with intelligent behaviour in artifacts". (Nilsson - 1998)

These definitions vary along two main dimensions. First dimension is the thought process and reasoning and second dimension is the behaviour of the machine.

The first seven definitions are based on comparisons to human performance where as remaining definitions measure success against an ideal concept of intelligence, which we call rationality. A system is rational if it does the "right thing" given what it knows. Historically, there are four approaches that are followed in AI. These four approaches are Acting Humanly, Thinking Humanly, Thinking Rationally and Acting Rationally. Let us consider four approaches in detail.

1) Acting Humanly

For testing intelligence Alan Turing (1950) proposed a test called as Turing test. He suggested a test based on common features that can match with the most intelligent entity - human beings.

Computer would need to possess following capabilities:

a) Natural language processing - To enable it to communicate successfully in English.

b) Knowledge representation to store what it knows, what it hears.

c) Automated reasoning to make use of stored information to answer questions being asked and to draw conclusions.

d) Machine learning to adapt to new circumstances and to detect and make new predictions by finding patterns.

Turing also suggested to have physical interaction between interrogater and computers. Turing test avoids this but Total Turing Test includes video signal so that the interrogator can test the subject's perceptual abilities, as well as the opportunity for the interrogator to pass the physical objects "through the hatch".

To pass total turing test in addition, computer will need following capabilities.

e) Computer vision to perceive objects.

f) Robotics to manipulate objects.

2) Thinking Humanly

As we are saying that the given program thinks like human it we should know that how human thinks. For that, the theory of human minds needs to be explored. There are two ways to do this: through introspection i.e. trying to catch our own thoughts as they go by and through psychological experiments.

If computer programs, I/O and timing behaviours matches corresponding human behaviours, that is, we can say that some of the program's mechanisms could also be operating in human. The interdesciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology that try to construct precise and testable theories of the workings of human mind.

3) Thinking Rationally - the "laws of thought approach"

The concept of "Right thinking" was proposed by Aristotle. This idea provided patterns for argument structures that always yielded correct conclusions when given correct premises.

For example,

"Ram is man",

"All men are mortal",

"Ram is mortal".

These laws of thought were supposed to govern the operation in the mind; their study initiated the field called logic which can be implemented to create intelligent systems.

4) Acting Rationally

An agent (Latin agre-to do) is something that acts. But computer agents are expected to have more other attributes that distinguish them from just the "programs", because they need to operate under autonomous control, perceiving their environment, persisting prolonged time period, adapting to change and being capable of taking on another goals. A rational agent is expected to act so as to achieve the best outcome or when there is uncertairuty to acheive best expected outcome.

The laws of thought emphasis on correct inference which should be incorported in rational agent.

Foundation of AI

Now we discuss the various disciplines that contributed ideas, viewpoints and techniques to AI.

Philosophy provides base to AI by providing theories of relationship between physical brain and mental mind, rules for drawing valid conclusions. It also provides information about knowledge origins and the knowledge leads to action.

Mathematics gives strong base to AI to develop concrete and formal rules for drawing valid conclusions, various methods for date computation and techniques to deal with uncertain information.

Economics support AI to make decisions so as to maximize payoff and make decisions under uncertain circumstances.

Neuroscience gives information which is related to brain processing which helps AI to develope date processing theories.

Phychology provides strong concepts of how humans and animals think and act which helps AI for developing process of thinking and actions.

Strong and Weak AI

After taking brief look at various disciplines that contribute towards AI, now let us look at the concept of strong and weak AI which also gives basic foundation for developing automated systems.

1. Strong AI

This concept was put forward by John Searle in 1980 in his article, "Minds, Brains and Programs". Strong form AI provides theories for developing some form of computer based AI that can truly reason and solve problems. A strong form of AI is said to be sentient or self aware.

Strong AI can be categorized as,

Human-like AI - In which the computer program thinks and reasons much like a human-mind.

Non-human-like AI - In which the computer program develops a totally non-human sentience; and a non-human way of thinking and reasoning.

2. Weak AI

Weak artificial intelligence research deals with the creation of some form of computer based AI that cannot truly reason and solve problems. They can reason and solve problems only in a limited domain, such a machine would, in some ways, act as if it were intelligent, but it would not possess true intelligence.

There are several fields of weak AI, one of which is natural language. Much of the work in this field has been done with computer simulations of intelligence based on predefined sets of rules. Very little progress has been made in strong AI. Depending on how one defines one's goals, a moderate amount of progress has been made in weak AI.