AI Unit I: Introduction to Artificial Intelligence

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Introduction: Artificial Intelligence:

Introduction: Artificial Intelligence BE Computer Engineering 1

What is an Intelligent system? :

What is an Intelligent system? What is intelligence? Hard to define unless you list characteristics eg, Reasoning Learning/ Adaptivity A truly intelligent system adapts itself to deal with changes in problems (automatic learning). Machine intelligence has a computer follow problem solving processes something like that in humans. Intelligent systems display machine-level intelligence, reasoning, often learning, not necessarily self-adapting. 2

Intelligent systems in business:

Intelligent systems in business Intelligent systems in business utilise one or more intelligence tools, usually to aid decision making Provides business intelligence to Increase productivity Examples of business intelligence – information on Customer behaviour patterns Market trend Examples of successful intelligent systems applications in business: Customer service (Customer Relations Modelling) Scheduling (eg Mine Operations) Data mining Financial market prediction Quality control 3

Intelligent systems in business – examples:

Intelligent systems in business – examples HNC (now Fair Isaac) software’s credit card fraud detector Falcon offers 30-70% improvement over existing methods (an example of a neural network). MetLife insurance uses automated extraction of information from applications in MITA (an example of language technology use) Personalized, Internet-based TV listings (an intelligent agent) Hyundai’s development apartment construction plans FASTrak-Apt (a Case Based Reasoning project) US Occupational Safety and Health Administration (OSHA uses "expert advisors" to help identify fire and other safety hazards at work sites (an expert system). Source: http://www.newsfactor.com/perl/story/16430.html 4

Characteristics of intelligent systems:

Characteristics of intelligent systems Possess one or more of these: Capability to extract and store knowledge Human like reasoning process Learning from experience (or training) Finding solutions through processes similar to natural evolution Recent trend More sophisticated Interaction with the user through natural language understanding speech recognition and synthesis image analysis Most current intelligent systems are based on rule based expert systems one or more of the methodologies belonging to soft computing 5

Artificial Intelligence (AI) :

Artificial Intelligence (AI) Primary goal: Development of software aimed at enabling machines to solve Problems through human-like reasoning Attempts to build systems based on a model of knowledge representation and processing in the human mind Encompasses study of the brain to understand its structure and functions Expert systems – an AI success story of the 80s 6

The Soft Computing (SC) paradigm:

The Soft Computing (SC) paradigm Also known as Computational Intelligence Unlike conventional computing, SC techniques can be tolerant of imprecise, incomplete or corrupt input data solve problems without explicit solution steps learn the solution through repeated observation and adaptation can handle information expressed in vague linguistic terms arrive at an acceptable solution through evolution 7

The Soft Computing (SC) paradigm (cont’d):

The Soft Computing (SC) paradigm (cont’d) Few characteristics are common in problem solving by individual humans Eevolution is common in nature The predominant SC methodologies found in current intelligent systems are: Artificial Neural Networks (ANN) Fuzzy Systems Genetic Algorithms (GA) 8

Introduction:

Introduction What is AI? The foundations of AI A brief history of AI The state of the art Introductory problems 9

What is AI?:

What is AI? Intelligence : “ability to learn, understand and think”. AI is the study of how to make computers make things which at the moment people do better. Examples: Speech recognition, Smell, Face, Object, Intuition, Inferencing, Learning new skills, Decision making. 10

What is AI?:

What is AI? 11 Thinking humanly -> m/c with mind. Thinking rationally -> Study of computation that makes it possible to act, reason. Acting humanly -> study to make comps make things better than people do. Acting rationally -> concerned with intelligent behaviour in artifacts.

Acting Humanly: The Turing Test:

Acting Humanly: The Turing Test Alan Turing (1912-1954) “Computing Machinery and Intelligence” (1950) 12 Human Interrogator Human AI System Imitation Game

Acting Humanly: The Turing Test:

Acting Humanly: The Turing Test The Turing Test, proposed by Alen turing in 1950. was designed to provide a satisfatory operational defination of intelligence. the computer would need to possess following capabilities, natural language processing : to enable it to communicate successfully knowledge representation: to store what it knows automate d reasoning: to use the stored information to answer question and to draw new conclusion. machine learning : to adapt to new circumstances and detect patterns. computer vision: to perceive object and robotics: to manipulate object and move about . AI compose of above disciplines. 13

Thinking Humanly: Cognitive Modelling:

Thinking Humanly: Cognitive Modelling when we say that a given program thinks like a human, we must have some way to figure out how human thinks. 14

Thinking Humanly: Cognitive Modelling:

Thinking Humanly: Cognitive Modelling GPS was designed. Not content to have a program correctly solving a problem. More concerned with comparing its reasoning steps to traces of human solving the same problem. Requires testable theories of the workings of the human mind: cognitive science . 15

Thinking Rationally: Laws of Thought:

Thinking Rationally: Laws of Thought Aristotle was one of the first to attempt to codify “right thinking”, i.e., irrefutable reasoning processes. Formal logic provides a precise notation and rules for representing and reasoning with all kinds of things in the world. Obstacles: - Informal knowledge representation. - Computational complexity and resources. 16

Acting Rationally:

Acting Rationally Acting so as to achieve one’s goals, given one’s beliefs. Does not necessarily involve thinking. Advantages: - More general than the “laws of thought” approach. - More amenable to scientific development than human- based approaches. 17

The Foundations of AI:

Philosophy (423 BC - present): - Logic, methods of reasoning. - Mind as a physical system. - Foundations of learning, language, and rationality. Mathematics (c.800 - present): - Formal representation and proof. - Algorithms, computation, decidability, tractability. - Probability. 18 The Foundations of AI

The Foundations of AI:

The Foundations of AI Psychology (1879 - present): - Adaptation. - Phenomena of perception. - Experimental techniques. Linguistics (1957 - present): - Knowledge representation. - Grammar. 19

A Brief History of AI:

A Brief History of AI The gestation of AI (1943 - 1956): - 1943: McCulloch & Pitts: Boolean circuit model of brain. - 1950: Turing’s “Computing Machinery and Intelligence”. - 1956: McCarthy’s name “Artificial Intelligence” adopted. Early enthusiasm, great expectations (1952 - 1969): - Early successful AI programs: Samuel’s checkers, Newell & Simon’s Logic Theorist, Gelernter’s Geometry Theorem Prover. - Robinson’s complete algorithm for logical reasoning. 20

A Brief History of AI:

A Brief History of AI A dose of reality (1966 - 1974): - AI discovered computational complexity. - Neural network research almost disappeared after Minsky & Papert’s book in 1969. Knowledge-based systems (1969 - 1979): - 1969: DENDRAL by Buchanan et al.. - 1976: MYCIN by Shortliffle. - 1979: PROSPECTOR by Duda et al.. 21

A Brief History of AI:

A Brief History of AI AI becomes an industry (1980 - 1988): - Expert systems industry booms. - 1981: Japan’s 10-year Fifth Generation project. The return of NNs and novel AI (1986 - present): - Mid 80’s: Back-propagation learning algorithm reinvented. - Expert systems industry busts. - 1988: Resurgence of probability. - 1988: Novel AI ( ALife , GAs, Soft Computing, …). - 1995: Agents everywhere. - 2003: Human-level AI back on the agenda. 22

Task Domains of AI:

Task Domains of AI Mundane Tasks: Perception Vision Speech Natural Languages Understanding Generation Translation Common sense reasoning Robot Control Formal Tasks Games : chess, checkers etc Mathematics: Geometry, logic, Proving properties of programs Expert Tasks: Engineering ( Design, Fault finding, Manufacturing planning) Scientific Analysis Medical Diagnosis Financial Analysis 23

AI Technique:

AI Technique Intelligence requires Knowledge Knowledge possesses less desirable properties such as: Voluminous Hard to characterize accurately Constantly changing Differs from data that can be used AI technique is a method that exploits knowledge that should be represented in such a way that: Knowledge captures generalization It can be understood by people who must provide it It can be easily modified to correct errors. It can be used in variety of situations 24

The State of the Art:

The State of the Art Computer beats human in a chess game. Computer-human conversation using speech recognition. Expert system controls a spacecraft. Robot can walk on stairs and hold a cup of water. Language translation for webpages. Home appliances use fuzzy logic. 25

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Artificial Intelligence Intelligent Agents

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Intelligent Agent Agent : entity in a program or environment capable of generating action. An agent uses perception of the environment to make decisions about actions to take. The perception capability is usually called a sensor . The actions can depend on the most recent perception or on the entire history (percept sequence).

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Agent Function The agent function is a mathematical function that maps a sequence of perceptions into action. The function is implemented as the agent program . The part of the agent taking an action is called an actuator . environment  sensors  agent function  actuators  environment

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Sensors Percept (Observations) Actuator Action Environment Environment Environment Environment Agent Agent Function

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Rational Agent A rational agent is one that can take the right decision in every situation. Performance measure : a set of criteria/test bed for the success of the agent's behavior. The performance measures should be based on the desired effect of the agent on the environment.

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Rationality The agent's rational behavior depends on: the performance measure that defines success the agent's knowledge of the environment the action that it is capable of performing the current sequence of perceptions. Definition : for every possible percept sequence, the agent is expected to take an action that will maximize its performance measure.

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Agent Autonomy An agent is omniscient if it knows the actual outcome of its actions. Not possible in practice. An environment can sometimes be completely known in advance. Exploration : sometimes an agent must perform an action to gather information (to increase perception). Autonomy : the capacity to compensate for partial or incorrect prior knowledge (usually by learning).

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Environment Observable -fully or partially A fully observable environment needs less representation.

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Environment Episodic or sequential Sequential – future actions depend on the previous ones. Episodic – individual unrelated tasks for the agent to solve. Static – dynamic Discrete – continuous Single agent – multi agent Multiple agents can be competitive or cooperative.

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More Definitions of Agents " An agent is a persistent software entity dedicated to a specific purpose . " (Smith, Cypher , and Spohrer 94 ) "Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user's goals or desires." (IBM) "Intelligent agents continuously perform three functions: perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions. "(Hayes-Roth 94)

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Agent vs. Program Size – an agent is usually smaller than a program. Purpose – an agent has a specific purpose while programs are multi-functional. Persistence – an agent's life span is not entirely dependent on a user launching and quitting it. Autonomy – an agent doesn't need the user's input to function.

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Simple Agents Table-driven agents : the function consists of a lookup table of actions to be taken for every possible state of the environment. If the environment has n variables, each with t possible states, then the table size is t n . Only works for a small number of possible states for the environment. Simple reflex agents : deciding on the action to take based only on the current perception and not on the history of perceptions. Based on the condition-action rule: (if (condition) action) Works if the environment is fully observable

PowerPoint Presentation:

( defun table_agent (percept) (let ((action t)) (push percept percepts) ( setq action (lookup percepts table)) action)) ( defun reflex_agent (percept) (let ((rule t) (state t) (action t)) ( setq state (interpret percept)) ( setq rule (match state)) ( setq action (decision rule)) action))

PowerPoint Presentation:

def table_agent (percept): action = True percepts.append (percept) action = lookup(percepts, table) return action def reflex_agent (percept): state = interpret(percept) rule = match(state) action = decision(rule) return action

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Model-Based Reflex Agents If the world is not fully observable, the agent must remember observations about the parts of the environment it cannot currently observe. This usually requires an internal representation of the world (or internal state). Since this representation is a model of the world, we call this model-based agent.

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( setq state t) #the world model ( setq action nil) #latest action ( defun model_reflex_agent (percept) (let ((rule t)) ( setq state ( update_state state action percept)) ( setq rule (match state)) ( setq action (decision rule)) action))

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state = True # the world model action = False # latest action def model_reflex_agent (percept) state = update_state (state, action, percept) rule = match(state) action = decision(rule) return action

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Goal-Driven Agents The agent has a purpose and The action to be taken depends on the current state and on what it tries to accomplish (the goal). In some cases the goal is easy to achieve. In others it involves planning , sifting through a search space for possible solutions, developing a strategy . Utility-based agents : the agent is aware of a utility function that estimates how close the current state is to the agent's goal.

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Learning Agents Agents capable of acquiring new competence through observations and actions. Components: learning element (modifies the performance element) performance element (selects actions) feedback element (critic) exploration element (problem generator).

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Other Types of Agents Temporarily continuous – a continuously running process, Communicative agent – exchanging information with other agents to complete its task. Mobile agent – capable of moving from one machine to another one (or from one environment to another). Flexible agent – whose actions are not scripted. Character – an agent with conversation skills, personality, and even emotional state.

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Agent Classification

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Agent Example A file manager agent. Sensors: commands like ls , du, pwd . Actuators: commands like tar, gzip , cd , rm , cp, etc. Purpose: compress and archive files that have not been used in a while. Environment: fully observable (but partially observed), deterministic (strategic), episodic, dynamic, discrete.

Tic Tac Toe:

Tic Tac Toe Three programs are presented : Series increase Their complexity Use of generalization Clarity of their knowledge Extensability of their approach 48

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe 49 X X o

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe Program 1: Data Structures: Board: 9 element vector representing the board, with 1-9 for each square. An element contains the value 0 if it is blank, 1 if it is filled by X, or 2 if it is filled with a O Movetable: A large vector of 19,683 elements ( 3^9), each element is 9-element vector. Algorithm: 1. View the vector as a ternary number. Convert it to a decimal number. 2. Use the computed number as an index into Move-Table and access the vector stored there. 3. Set the new board to that vector. 50

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe Comments: This program is very efficient in time. 1. A lot of space to store the Move-Table. 2. A lot of work to specify all the entries in the Move-Table. 3. Difficult to extend. 51

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe 52 1 2 3 4 5 6 7 8 9

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe Program 2: Data Structure: A nine element vector representing the board. But instead of using 0,1 and 2 in each element, we store 2 for blank, 3 for X and 5 for O Functions: Make2: returns 5 if the center sqaure is blank. Else any other balnk sq Posswin(p): Returns 0 if the player p cannot win on his next move; otherwise it returns the number of the square that constitutes a winning move. If the product is 18 (3x3x2), then X can win. If the product is 50 ( 5x5x2) then O can win. Go(n): Makes a move in the square n Strategy: Turn = 1 Go(1) Turn = 2 If Board[5] is blank, Go(5), else Go(1) Turn = 3 If Board[9] is blank, Go(9), else Go(3) Turn = 4 If Posswin(X)  0 , then Go(Posswin(X)) ....... 53

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe Comments: 1. Not efficient in time, as it has to check several conditions before making each move. 2. Easier to understand the program’s strategy. 3. Hard to generalize. 54

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe 55 8 3 4 1 5 9 6 7 2 15 - ( 8 + 5 )

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe Comments: 1. Checking for a possible win is quicker. 2. Human finds the row-scan approach easier, while computer finds the number-counting approach more efficient. 56

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe Program 3: 1. If it is a win, give it the highest rating. 2. Otherwise, consider all the moves the opponent could make next. Assume the opponent will make the move that is worst for us. Assign the rating of that move to the current node. 3. The best node is then the one with the highest rating. 57

Introductory Problem: Tic-Tac-Toe:

Introductory Problem: Tic-Tac-Toe Comments: 1. Require much more time to consider all possible moves. 2. Could be extended to handle more complicated games. 58

Introductory Problem: Question Answering:

Introductory Problem: Question Answering “Mary went shopping for a new coat. She found a red one she really liked. When she got it home, she discovered that it went perfectly with her favourite dress”. Q1 : What did Mary go shopping for? Q2 : What did Mary find that she liked? Q3 : Did Mary buy anything? 59

Introductory Problem: Question Answering:

Introductory Problem: Question Answering Program 1: 1. Match predefined templates to questions to generate text patterns. 2. Match text patterns to input texts to get answers. “What did X Y” “What did Mary go shopping for?” “Mary go shopping for Z” Z = a new coat 60

Introductory Problem: Question Answering:

Introductory Problem: Question Answering Program 2: Structured representation of sentences: Event2: Thing1: instance: Finding instance: Coat tense: Past colour: Red agent: Mary object: Thing 1 61

Introductory Problem: Question Answering:

Introductory Problem: Question Answering Program 3: Background world knowledge: C finds M C leaves L C buys M C leaves L C takes M 62

Exercises:

Exercises 1. Characterize the definitions of AI: "The exciting new effort to make computers think ... machines with minds, in the full and literal senses" (Haugeland, 1985) "[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning ..." (Bellman, 1978) 63

Exercises:

Exercises "The study of mental faculties, through the use of computational models" (Charniak and McDermott, 1985) "The study of the computations that make it possible to perceive, reason, and act" (Winston, 1992) "The art of creating machines that perform functions that require intelligence when performed by people" (Kurzweil, 1990) 64

Exercises:

Exercises "The study of how to make computers do things at which, at the moment, people are better" (Rich and Knight, 1991) "A field of study that seeks to explain and emulate intelligent behavior in terms of computationl processes" (Schalkoff, 1990) "The branch of computer science that is concerned with the automation of intelligent behaviour" (Luger and Stubblefield, 1993) 65

Exercises:

Exercises "A collection of algorithms that are computationally tractable, adequate approximations of intractabiliy specified problems" (Partridge, 1991) "The enterprise of constructing a physical symbol system that can reliably pass the Turing test" (Ginsberge, 1993) "The f ield of computer science that studies how machines can be made to act intelligently" (Jackson, 1986) 66

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