04-04-2012, 11:17 AM
ARTIFICIAL INTELLIGENCE
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abstract
Artificial intelligence is the area of computer science focusing on creating machines that engage on behaviours that human s consider intelligent.The ability to create intelligent machines has intrigued humans since ancient times,and today with the advent of the computer and 50years of research into AI programming techniques,the dream of start machines is becoming a reality.Researchers are creating systems which can mimic human thoughts.understand speech,beat the best human chess player,and countless other feats never before possible.Find out how the military is applying AI logic to its hi-tech systems,and how in the near future Artificial Intelligence may impact our lives.Moreover we can say that there are now machines in this world that can think,react and take decisions on their own and the possibility of such machines will increase in near future.
introduction
The phrase Artificial Intelligence calls for formalization of the term intelligence.Thus it is defined as the simulation of human intelligenceon a machine so make the machine efficient to identify and use the right place of knowledge at a given step of solving a problem.Such systems are called rationals,a question is that oes rational thinking and acting include all possible charactersistics of an intelligent systems?If so,how does it represents behavioural intelligence such as machine learning,perception and planning?Asystem can act rationally only after acquiring real world knowledge,a little thinking can make a system a successful planner.Perception is a prerequisite feature of rational systems.Relating artificial intelligence with computational modals capable of thinking and acting rationally therefore has a pragmatic significance.
Technical tools
To understand what exactly artificial intelleigence is,we illustrate some common problems.Problems of AI generally use a common term called state.A state represents a status of the solution at agiven step of the problem solving procedure.And this approach is termed as state-space approach. 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 isto 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.
Fig.: The initial and the final states of the Number Puzzle game, where B denotes the blank space.
We now define two operations, blank-up (BU) / blank-down (BD) and blank-left (BL) / blank-right (BR), and the state-space (tree) for the problem is presented below using these operators. The algorithm for the above kind of problems is straightforward. It consists of three steps, described by steps 1, 2(a) and 2(b) below.
Algorithm for solving state-space problems
Begin
1. state: = initial-state; existing-state:=state;
2. While state ≠ final state do
Begin
o a. Apply operations from the set {BL, BR, BU, BD} to each state so as to generate new-states;
o b. If new-states ∩ the existing-states ≠ φ
Then do
• Begin state := new-states - existing-states;
o Existing-states := existing-states - {states}
o End;
• End while;
End.
Fig.: The state-space for the Four-Puzzle problem.
It is thus clear that the main trick in solving problems by the state-space approach is to determine the set of operators and to use it at appropriate states of the problem.
Researchers in artificial intelligence have segregated the AI problems from the non-AI problems. Generally, problems, for which straightforward mathematical / logical algorithms are not readily available and which can be solved by intuitive approach only, are called AI problems. The 4-puzzle problem, for instance, is an ideal AI Problem. There is no formal algorithm for its realization, i.e., given a starting and a goal state, one cannot say prior to execution of the tasks the sequence of steps required to get the goal from the starting state. Such problems are called the ideal AI problems. The wellknown water-jug problem, the Travelling Salesperson Problem (TSP) , and the n-Queen problem are typical examples of the classical AI problems. Among the non-classical AI problems, the diagnosis problems and the pattern classification problem need special mention. For solving an AI problem, one may employ both artificial intelligence and non-AI algorithms. An obvious question is: what is an AI algorithm? Formally speaking, an artificial intelligence algorithm generally means a non-conventional intuitive approach for problem solving. The key to artificial intelligence approach is intelligent search and matching. In an intelligent search problem / sub-problem, given a goal (or starting) state, one has to reach that state from one or more known starting (or goal) states.