artificial intaligence Presentation Seminar

[born_for seminars]

Abstract:
At the beginning of the Stone Age, when people started taking shelters in caves, they made attempts to immortalize themselves by painting their images on rocks. With the gradual progress in civilization, they felt interested to see themselves in different forms. So, they started constructing
models of human being with sand, clay and stones. The size, shape,constituents and style of the model humans continued evolving but the man was not happy with the models that only looked like him. He had a strong desire to make the model ‘intelligent’, so that it could act and think as he did.
This, however, was a much more complex task than what he had done before.So, he took millions of years to construct an ‘analytical engine’ that could perform a little arithmetic mechanically. Babbage’s analytical engine was thefirst significant success in the modern era of computing. Computers of the first generation, which were realized following this revolutionary success,were made of thermo-ionic valves. They could perform the so-called ‘number crunching’ operations. The second-generation computers came up shortly after the invention of transistors and were more miniaturized in size. They were mainly used for commercial data processing and payroll creation. After morethan a decade or so, when the semiconductor industries started producing integrated circuits (IC) in bulk, the third generation computers were launched in business houses. These machines had an immense capability to performmassive computations in real time. Many electromechanical robots were also designed with these computers. Then after another decade, the fourth generation computers came up with the high-speed VLSI engines. Many electronic robots that can see through cameras to locate objects for placementat the desired locations were realized during this period. During the period of 1981-1990 the Japanese Government started to produce the fifth generation computing machines that, besides having all the capabilities of the fourth generation machines, could also be able to process intelligence. The computers of the current (fifth) generation can process natural languages, play games, recognize images of objects and prove mathematical theorems, all of which lie in the domain of Artificial Intelligence (AI). But what exactly is AI?
Defining AI:
The phrase AI, which was coined by John McCarthy three decades ago,evades a concise and formal definition to date. One representative definition is pivoted around the comparison of intelligence of computing machines with human beings]. Another definition is concerned with the performance of machines which “historically have been judged to lie within the domain of intelligence” [17], [35]. None of these definitions or the like have been universally accepted, perhaps because of their references to the word “intelligence”, which at present is an abstract and immeasurable quantity. A better definition of AI, therefore, calls for formalization of the term “intelligence”. Psychologist and Cognitive theorists are of the opinion that intelligence helps in identifying the right piece of knowledge at the appropriate instances of decision making.The phrase “AI” thus can be defined as the simulation of human intelligence on a machine, so as to make
the machine efficient to identify and use the righ piece of “Knowledge” at a given step of solving a problem. A system capable of planning and executing the right task at the right time is generally called rational. Thus, AI alternatively may be stated as a subject dealing with computational models that can think and act. A common question then naturally arises: Does rational thinking and acting include all possible characteristics of an intelligent system? If so, how does it represent behavioral intelligence such as machine learning, perception and planning? A little thinking, however, reveals that a system that can reason well must be a successful planner, as planning in many circumstances is part of a reasoning process. Further, a system can act rationally only after acquiring adequate knowledge from the real world. So, perception that stands for building up of knowledge from real world information is a prerequisite feature for rational actions. One step further thinking envisages that a machine without learning capability cannot possess perception. The rational action of an agent (actor), thus, calls for possession of all the elementary characteristics of intelligence. Relating AI with the computational models capable of thinking and acting rationally, therefore, has a pragmatic significance.
General Problem Solving Approaches in AI
To understand what exactly AI is, we illustrate some common problems. Problems dealt with in AI generally use a common term called ‘state’. A state represents a status of the solution at a given step of the problem solving procedure. The solution of a problem, thus, is a collection of the problem states. The problem solving procedure applies an operator to a state to get the next state. Then it applies another operator to the resulting state to derive anew state. The process of applying an operator to a state and its subsequent
1.The branch of computer science that is concerned with the automation of intelligent behavior.
2. The study of computations that make it possible to perceive, reason and act.
3. A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes.
4. The study of mental faculties through the use of computational models.
Such a method of solving a problem is generally referred to as statespace approach. We will first discuss the state-space approach for problem solving by a well-known problem, which most of us perhaps have solved in our childhood.
Example: Consider a 4-puzzle problem, where in a 4-cell board there are 3 cells filled with digits and 1 blank cell. The initial state of the game represents a particular orientation of the digits in the cells and the final state to be achieved is another orientation supplied to the game player. The problem of the game is to reach from the given initial state to the goal (final) state, if possible, with a minimum of moves. Let the initial and the final state be as shown in figures 1(a) and (b) respectively.