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Artificial Intelligence By Dr. A.S.Alvi Artificial Intelligence Artificial + Intelligence Intelligence ? What is Intelligence ? “The ability to learn, understand and think” (Oxford dictionary) What is Intelligence ? “The capacity to learn and solve problems” (Webster dictionary) What is Intelligence ? “ The ability to acquire knowledge and use it" (Pigford and Baur) What is Intelligence ? Intelligence is a property of mind that encompasses many related mental abilities, such as the capabilities to reason think abstractly plan comprehend ideas and language solve problems learn Artificial ? Artificial: Produced by human art or effort, rather than originating naturally. Artificial: Produced by human art or effort, rather than originating naturally. Intelligence: is the ability to acquire knowledge and use it" [Pigford and Baur] So AI is defined as: AI is the study of ideas that enable computers to be intelligent. AI is the part of computer science concerned with design of computer systems that exhibit human intelligence. AI cont…… From the above two definitions, we can see that AI has two major roles: Study the intelligent part concerned with humans. Represent those actions using computers. What is Artificial Intelligence (AI)? Computational models of human behavior? • Programs that behave (externally) like humans, Computational models of human “thought” • Programs that operate (internally) the way humans do Computational systems that behave intelligently? • What does it mean to behave intelligently? Computational systems that behave rationally • More on this later What is Artificial Intelligence ? THOUGHT BEHAVIOUR Systems that think Systems that think like humans rationally Systems that act like humans HUMAN Systems that act rationally RATIONAL More Formal Definition of AI AI is a branch of computer science which is concerned with the study and creation of computer systems that exhibit some form of intelligence OR those characteristics which we associate with intelligence in human behavior More Formal Definition of AI cont…… A field that focuses on developing techniques to enable computer systems to perform activities that are considered intelligent (in humans and other animals). [Dyer] The science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. [McCarthy] More Formal Definition of AI cont…… The study of how to make computer do things which, at the moment, people do better. [Rich&Knight] The design and study of computer programs that behave intelligently. [Dean, Allen, & Aloimonos] The study of [rational] agents that exist in an environment and perceive and act. [Russell&Norvig] Major Branches of AI Perceptive system A system that approximates the way a human sees, hears, and feels objects Vision system Capture, store, and manipulate visual images and pictures Robotics Mechanical and computer devices that perform tedious tasks with high precision Expert system Stores knowledge and makes inferences Learning system Computer changes how it functions or reacts to situations based on feedback Natural language processing Computers understand and react to statements and commands made in a “natural” language, such as English Neural network Computer system that can act like or simulate the functioning of the human brain Artificial intelligence Vision systems Learning systems Robotics Expert systems Neural networks Natural language processing Agents Software that gathers information about an environment and takes actions based on that a robot a web shopping program a factory a traffic control system… Some applications of AI Game Playing Speech Recognition Computer Vision Expert Systems Diagnostic Systems Financial Decision Making Classification Systems Mathematical Theorem Proving Natural Language Understanding Scheduling and Planning Robotics and Path planning Biology and medicine Production System A production system consists of four basic components: 1. A set of rules of the form Ci → Ai where Ci is the condition part and Ai is the action part. The condition determines when a given rule is applied, and the action determines what happens when it is applied. 2. One or more knowledge databases that contain whatever information is relevant for the given problem. 3. A control strategy that determines the order in which the rules are applied to the database, and provides a way of resolving any conflicts that can arise when several rules match at once. 4. A rule applier which is the computational system that implements the control strategy and applies the rules State Space Search To provide a formal description of a problem: Define a state space that contains all the possible configurations of the relevant objects. Specify the initial states. Specify the goal states. Specify a set of rules: Solving a problem = search in the state space by using the rules + control strategy Control Strategies Requirements of a good control strategy: It causes motion. Otherwise, it will never lead to a solution. It is systematic. Otherwise, it may use more steps than necessary. It is efficient. Find a good, but not necessarily the best, answer. Control Strategies Example 1: Water Jug Problem Tree Structure Example 2: Traveling Salesman Problem Criteria for success. One of the most important questions to answer in any scientific or engineering research project is “How will we know if we have succeeded?” So how in AI we have to ask ourselves how will we know if we have constructed a machine that is intelligent? The question is hard as unanswerable question “What is Intelligence?” Criteria for success To measure the progress we use proposed method known as Turing Test. Alan Turing suggested this method to determine whether the machine can think. To conduct this test, we need two people and the machine to be evaluated. One person act as interrogator, who is in a separate room from the computer and the other person. Criteria for success The interrogator can ask questions to either the person or computer by typing questions and received typed responses. However the interrogator knows them only as A and B and aims to determine which is the person and which is the machine. The goal of the machine is to fool the interrogator into believing that it is the person. If the machine succeeds at this, then we will conclude that the machine can think. Production Rules for Water Jug Problem 1. (x,y) (4,y) Fill the 4-gallon jug (x,3) Fill the 3-gallon jug if x<4 2. (x,y) if y<3 Production Rules for Water Jug Problem 3. (x,y) if x>0 (x-d , y) Pour some water out the 4-gallon jug 4. (x,y) if y>0 (x , y-d) Pour some water out the 3-gallon jug Production Rules for Water Jug Problem 5. (x,y) if x>0 (0,y) Empty the 4-gallon jug on the ground 6. (x,y) if y>0 (x,0) Empty the 3-gallon jug on the ground Production Rules for Water Jug Problem 7. (x,y) if x+y4 and y>0 8. (x,y) if x+y3 and x>0 (4,y-(4-x)) (4-(3-y) ,3) Pour water from the 3-gallon jug into the 4-gallon jug until the 4-gallon jug is full Pour water from the 4-gallon jug into the 3-gallon jug until the 3-gallon jug is full Production Rules for Water Jug Problem 9. (x,y) if x+y4 and y>0 10. (x,y) if x+y4 and x>0 (x+y,0) Pour all water from the 3-gallon jug into 4-gallon jug (0,x+y) Pour all water from the 4-gallon jug into 3-gallon jug Production Rules for Water Jug Problem 11. (0,2) (2,0) 12. (2,y) (0,y) Pour the 2-gallon from the 3-gallon jug into 4-gallon jug Empty the 2-gallon in the 4-gallon jug on the ground Search Strategies: Blind Search (Water Jug Problem Level1, level2) Search Strategies: Heuristic Search The Travelling Salesman Problem “A salesman has a list of cities, each of which he must visit exactly once. There are direct roads between each pair of cities on the list. Find the route the salesman should follow for the shortest possible round trip that both starts and finishes at any one of the cities.” Heuristic is a technique that improves the efficiency of a search process, possibly by sacrificing claims of completeness or optimality. Example 1: Traveling Salesman Problem Given n cities with known distances between each pair, find the shortest tour that passes through all the cities exactly once before returning to the starting city Alternatively: Find shortest Hamiltonian circuit in a weighted connected graph Example: 2 a b 5 3 4 8 c 7 d TSP by Exhaustive Search Tour Cost a→b→c→d→a 2+3+7+5 = 17 a→b→d→c→a 2+4+7+8 = 21 a→c→b→d→a 8+3+4+5 = 20 a→c→d→b→a 8+7+4+2 = 21 a→d→b→c→a 5+4+3+8 = 20 a→d→c→b→a 5+7+3+2 = 17 Algorithm 2: The Nearest-Neighbor Algorithm Step 1. Start at the designated starting vertex. If there is no designated starting vertex, pick any vertex. Step 2. From the starting vertex go to its nearest neighbor (the vertex for which the corresponding edge has the smallest weight. Steps 3. From each vertex go to its nearest neighbor, choosing only among the vertices that haven’t been yet visited. (If there is more than one, choose at random). Keep doing this until all the vertices have been visited. Tower of Hanoi There are three towers The disks, with decreasing sizes, placed on the first tower You need to move all of the disks from the first tower to the second tower .Only one disk can be move at a time. Larger disks can not be placed on top of smaller disks The third tower can be used to temporarily hold disks a b c Tower of Hanoi a b c State Space Representations The state space is simply the space of all possible states, or configurations, that our system may be in. Generally, of course, we prefer to work with some convenient representation of that search space. There are two components to the representation of state spaces: Static States Transitions between States State Space Graphs: If the number of possible states of the system is small enough, we can represent all of them, along with the transitions between them, in a state space graph, e.g. Inference Rules Modus Ponens From P and P → Q , infer Q. P P→Q -----------Q For ex: Given : (John is a student) And : (John is a student) → (John is a Engg student) Conclude : (John is a Engg student) Inference Rules Modus tollens: From Q and P → Q , infer P. Q P→Q -----------P For ex: Given : (John is a Engg student) And : (John is a student) → (John is a Engg student) Conclude: (John is a student) Inference Rules Chain rule : From P → Q , Q → R infer P → R. P→Q Q→R -----------P→R Tic-Tac-Toe X O Initial State: Board position of 3x3 matrix with 0 and X. Operators: Putting 0’s or X’s in vacant positions alternatively Terminal test: Which determines game is over Utility function: e(p) = (No. of complete rows, columns or diagonals are still open for player ) – (No. of complete rows, columns or diagonals are still open for opponent ) Game tree for Tic-Tac-Toe Depth-first searching A B C D E F H L M I N O G J P K Q 49 A depth-first search (DFS) explores a path all the way to a leaf before backtracking and exploring another path For example, after searching A, then B, then D, the search backtracks and tries another path from B Node are explored in the order A B D E H L M N I O P CFGJKQ N will be found before J Breadth-first searching A B C D E F G H L M I N O J P K Q A breadth-first search (BFS) explores nodes nearest the root before exploring nodes further away For example, after searching A, then B, then C, the search proceeds with D, E, F, G Node are explored in the order A B C D E F G H I J K LMNOPQ J will be found before N Criterion Time Breadth-First Depth-First bd bm Space bd bm Optimal? Yes No Complete? Yes No b: branching factor d: solution depth m: maximum depth Problem Characteristics Is the problem decomposable? Can solution steps be ignored or undone? Is the universe predictable? Is a good solution absolute or relative? Is the solution a state or a path? What is the role of knowledge? Does the task require human-interaction? Is the problem decomposable? Can the problem be broken down to smaller problems to be solved independently? Decomposable problem can be solved easily. 2 (x2 + 3x + sin2x.cos x) dx 2 x dx x3/3 3x dx 3x dx 2 2 sin x.cos x dx 2 (1− cos x)cos2xdx 3x2/2 cos2xdx − cos4xdx Can solution steps be ignored or undone? Ignorable (e.g. Theorem Proving), in which solution steps can be ignored. Recoverable (e.g. 8-puzzle), in which solution steps can be undone. Irrecoverable (e.g. Chess), in which solution steps can be undone. Is the universe predictable? The 8-Puzzle: Every time we make a move, we know exactly what will happen. Certain outcome! Playing Bridge: We cannot know exactly where all the cards are or what the other players will do on their turns. Uncertain outcome! Is a good solution absolute or relative? 1. Marcus was a man 2. Marcus was a Pompeian 3. Marcus was born in 40 A.D 4. All men are mortal. 5. All Pompeians died when the volcano erupted in 79 A.D 6. No mortal lives longer than 150 years. 7. It is now 2008 A.D Question: Is Marcus alive? What is the role of knowledge? Playing Chess Knowledge is important only to constrain the search for a solution. Reading Newspaper Knowledge is required even to be able to recognize a solution. Does the task require human-interaction? Is the solution a state or a path? The Water Jug Problem • The path that leads to the goal must be reported. A path-solution problem can be reformulated as a state- solution problem by describing a state as a partial path to a solution. The question is whether that is natural or not.