Presentation on Artificial Intelligence Presenters are – Ravinder kaur Mandeep kaur Deepak sedha Akash panchal

WHAT IS ARTIFICIAL INTELLIGENCE ? Artificial Intelligence began in earnest with the emergence of the modern computer during 1940s and 1950s. It was the ability of these new electronic machines to store large amounts of information It’s a branch of computer science concerned with the study and creation of computer systems that can show some form of intelligence

What AI requires ? AI requires an understanding of related terms such as intelligence, knowledge, reasoning, thought, learning and a number of computer-related terms. Intelligence is the ability to acquire, understand and apply knowledge or the ability to exercise thought and reason. Food for this intelligence is knowledge.

What is not AI ? Study and creation of conventional computer systems. Study of the mind. Study of the body. Study of Languages.

Application Areas of Artificial Intelligence AI in business Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties AI in fiction In science fiction AI — almost always strong AI — is commonly portrayed as an upcoming power trying to overthrow human authority AI in Philosophy The strong AI vs. weak AI debate is still a hot topic amongst AI philosophers

Turing Test Turing in 1950s, published an article in the Mind Magazine, which triggered a controversial topic “Can a Machine Think?” In that article he proposed a game named imitation game which was later called TURING TEST

Criticism of Turing Test Other researchers said that the test determines the intelligence of a programmer, who programs it as compared to machine. The machine can manipulate the formal symbols

Importance and Advantages of AI AI can replace humans being in some more specific jobs for some business store and household day to day activities and will help to sort out the manpower AI machines can help in hospitals, providing food and medicines where human being feared to be attacked by such disease. Robotics is good example It is estimated that AI will help human being in aeronautics to know the universe.

AI Problems The AI problems can be broadly divided into : Ordinary Tasks Formal Tasks Expert Tasks

Ordinary Tasks Commonsense Reasoning: Commonsense reasoning i.e. developing computer systems which has some commonsense like if we let fall any thing on the floor it may break Perception: Perception includes two basic properties what humans generally posses the i.e. Vision and Speech Natural Language understanding: Communicating various ideas is perhaps the most important thing that differentiates humans from animals

Formal Tasks Game Playing: Making computers playing games seems to be very interesting that is why many researchers have extensively contributed for computer game playing methods Mathematics: Finding a proof for a theorem in mathematics is certainly is an intelligent task. The study of theorem proving play a significant part in development of Artificial Intelligence Methods.

Expert Tasks Expert Systems: This area of AI deals in creation of computer systems which can perform those tasks which now a days is performed by experts. Expert systems are the expert programs that manipulate encoded knowledge to solve problem in a particular domain e.g. Medical, Military

Characteristics of AI Problems The AI programs manipulate symbols where as conventional programs deal with numeric processing. The basis of AI program is that it must be able to manipulate the knowledge and it has to be represented in a way that in which it can be easily manipulated. The problems of AI deal with have a combinational explosion of various solution paths. E.g. Chess problem or in general game playing.

Underlying Assumption This assumption is made because this is the only way by which knowledge can be manipulated to arrive at new results. This symbol system has necessary and sufficient means for general intelligent action The assumption is only an assumption there is no way to prove or disapprove it on some logical grounds

AI Technique The knowledge should be general i.e. When ever we talk about the solution to a given problem we must reach a general solution which can be applicable to other problems as well It can be used in many situations even if it is not perfect or complete. e.g. Chess playing It should be easy to modify It should be able to overcome its own volume by reducing the range of overall possibilities

Types of AI Techniques Three AI techniques:- Use of Knowledge Search Generalization

There are two ways in which the AI problem can be represented: State Space Representation Problem Reduction

State Space Representation State: AI problem can be represented as a well formed set of possible states. State can be Initial State i.e. starting point, Goal State i.e. destination point and various other possible states between them which are formed by applying certain set of rules Space: In an AI problem the exhaustive set of all possible states is called space Search: In simple words search is a technique which takes the initial state to goal state by applying certain set of valid rules while moving through space of all possible states

Problem Reduction Production systems provide such structures, which helps the search procedure to perform efficiently. The process of solving the problem can be modeled as a production system. A production system consists of the following: A set of Rules Control Strategy Knowledge Data Base Rule Applier

Working of Production Rules Let us consider that the Initial state of a problem to be BADCCB and Goal State is ABBCCD. The set of rules are as follows (notice they are written as Left Hand side (IF) and Right Hand Side (Then Condition) 1) BA AB 2) DC CD 3) CB BC 4) DB BD Starting form Initial State BADCCB, we can see that the rules 1,2,3 can be applied at this state to move on to next one. As BA can be changed in to AB, DC can be changed in to CD and CB can be changed in to BC. This conflict will be resolved by choosing a control strategy. Here “Apply First applicable rule” strategy has been chosen. Control Strategy also take care of the fact that whether the rule pointer will stay on the position after applying the rule or will it move to first rule again.

Table representing working of production rules

Properties of Control Strategy: Control strategy should cause movement: The control strategy should be chosen in such a way that it should cause movement other wise same stated will be repeated again and the search will be able to move ahead in space of a given problem Control strategy should be systematic

Factors influencing the direction of the search: Nature of states Branching factor

State Space Search Types of Search Techniques:   The searching process in AI can be divided in two parts based on the amount of the knowledge it is carrying Un-Informed Search Techniques Informed Search Techniques

Un-Informed Search Technique The only information these kinds of search procedures have is about Initial State, Goal State and Set of rules that means they don’t have domain specific knowledge There are two types of search blind searches Depth-First Search Breadth-First Search

Breadth First Search

Depth First Search

Heuristic Functions Heuristic Search It can be noted that both above said search methods are systematic and force mobility, which are primary conditions of any good search process. In the perspective of AI, many times one may not get the best solution. In such cases it is required to obtain a very good solution Heuristic Functions The heuristics which are required for solving problems are generally represented as heuristic functions. Heuristic functions convert the problems states in to quantitative form

Following are the search algorithms which use the heuristic functions:   Hill Climbing Best First Search A* Algorithm AO* Algorithm Beam Search Constraint Satisfaction

best-first search

Conclusion

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