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Note for Artificial Intelligence - AI By vtu rangers

  • Artificial Intelligence - AI
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  • Visvesvaraya Technological University Regional Center - VTU
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P a g e | 1 | Module 1 MODULE 1 Artificial Intelligence Definition: • AI is the study of how to make computers do the things, which at the moment, people do better. The AI problems: • Some domains of AI 1. Mundane Tasks (Common tasks routine tasks, monotones, tiresome, exciting etc.) a. Perception i. Vision ii. Speech b. Natural Language i. Understanding ii. Generation iii. Translation c. Common Sense Reasoning i. Robot control formal tasks 2. Formal Tasks a. Games i. Chess ii. Backgammon (ludo) iii. Checkers-go b. Mathematics i. Geometry ii. Logic iii. Integral Calculus iv. Proving properties of programs 3. Expert Tables a. Engineering i. Design ii. Faull finding iii. Manufacturing planning b. Scientific Analysis c. Medical diagnosis d. Financial Analysis  What is the underlying assumption?  The heart of AI lies in the physical symbol system hypothesis (Newells simon). The physical symbol system is defined as: It consists of a set of entities call symbols which are the physical patterns which can occur as components of another type of entity called an Expression (or symbol structure).

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 A symbol structure is composed of a number of instances (or token) of symbols related in some physical way.  At any instant of time the system will contain a collection of those symbol structures, system also contains a collection of processes which operate an expressions to produce other expressions, processes of creation, modifications, processes of creation, modification, reproduction and destruction.  The physical symbol system hypothesis:  A physical symbol system has the necessary and sufficient means for general intelligent action.  What is an AI Technique?  Intelligence requires knowledge but the knowledge possess some less desirable properties such as  It is voluminous  It is hard to characterize accurately.  It is constantly changing.  It differs from data which is organised in some way corresponding to the way in which it will be used.  Thus we can define that an AI technique is a method which exploits knowledge which should be represented in such a way that:  The knowledge captures generalizations: Each individual situation need not be represented separately and the situations that share important properties are grouped together.  Knowledge can be understood by people who must provide.  It can be modified easily to correct errors and to reflect the changes in the world and in our world view.  It can be used in many situations even if it is not totally accurate or complete.  By narrowing down the range of possibilities the sheer bulk of data can be reduced, for being considered. The AI techniques must be designed keeping in view the constraints imposed by AI problems.  How to build a system to solve a particular problem? 1) Define the problem precisely a) What will be the specification of initial state and final state of acceptable solution to the problem. 2) Analyse the problems: a) Appropiate technique be selected for solving problem. 3) Isolate and represent the tasks knowledge which is necessary to solve the problem. 4) Choose the best problem solving technique and apply it to solve the particular problem.

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P a g e | 3 | Module 1  Defining the Problem as a state space search formal description of a problem: 1) Define the state space which contains all the possible configuration of the relevant objects. 2) Specify one or more states within that space which describe possible situation from which the problem solving process manages. These states are called the Initial states. 3) Specifies one or more states which would be acceptable as solutions to the problem. These states are called the goal states. 4) Specify a set of rules which describe the action (operators) available. And for doing this following issues may have to be considered. a) What unstated assumptions are present in the informal problem description? b) How generate the rule should be? c) How much of the work required to solve the problem should be precomputed and represented in the rules. • The problems can be solved using the rules in combination with an appropriate control strategy to more through the problem space until a path from initial state to final state is found. • The process of search is fundamental to the problem solving process.  Production Systems:  A set of rules each consistency of a left side(a pattern) which determines the applicability of rule and a right side which describes the operation to be performed if the rule is applied.  One or more knowledge/databases that contain whatever information is appropriate for the particular task. Some parts of the database may be permanent while other parts of it may pertain only to the solution of the current problem. The info within the databases may be structured in any appropriate way.  A control strategy which specifies the order in which the rules will be compared to the database and a way of resolving the conflicts which arise when several rules that match at once.  A rule applier.  Control Strategies:  The first requirement of a good control strategy is that it causes motion.  The second requirement of a good central strategy is that it be systematics. Ex: One systematic control strategy for the water joining the problem is the following:  Construct a dice with the initial state as its root:

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P a g e | 4 | Module 1  Generate all the off springs of the not by applying each of the applicable to rule to the initial state as its root.  The figure below shows how the tree looks at this point.  Now for each leaf node generate all the successors nodes by applying all the rules that are appropriate.  Continue this process until application some rule produces a goal state. This process is called the breadth first search.  Algorithm Breadth first search: 1) Create a variable called NODE_LIST and set it to initial state. 2) Until a goal state is found. a. Remove the first element from NODE_LIST and call it NODE_LIST was empty quit. b. For each way that each rule can match the state described in Edo: i. Apply the rule to generate a new state. ii. If the new state is a goal state, quit and returned this state. iii. Otherwise, add the new state to the end of node out.

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