NSCET E-LEARNING PRESENTATION NOTES/unit 1/CS8691_AI.pdf · 4 What is AI? - definition •AI is the study of how to make computers make things which at the moment people do better

NSCET

E-LEARNING

PRESENTATIONLISTEN … LEARN… LEAD…

Department of Computer Science and Engineering

Mr.P.G.Siva Sharma Karthick M.E.,MBA.,

Assistant Professor

Nadar Saraswathi College of Engineering & Technology,

Vadapudupatti, Annanji (po), Theni – 625531.

CS8691 – ARTIFICIAL INTELLIGENCE

IIIrd YEAR / VIth SEMESTER

PHOTO

2Department of CSE, NSCET, Theni

UNIT 01 – INTRODUCTION

4

What is AI? - definition

• AI is the study of how to make computers make things which at the

moment people do better.

• AI is the branch of computer science that is concerned with the

automation of intelligent behaviour. A I is based upon the principles of

computer science namely data structures used in knowledge

representation, the algorithms needed to apply that knowledge and the

languages and programming techniques used in their implementation.

• Examples: Speech recognition, Smell, Face, Object, Intuition,

Inferencing, Learning new skills, Decision making, Abstract thinking

Department of CSE, NSCET, Theni

AI Definitions

What is AI ?

• A broad field which means different things to different people

Concerned with getting computers to do tasks that require

human intelligence

However

There are many tasks which we might reasonably think require intelligence which computers do without even thinking

Complex Arithmetic

There are many tasks that people do without thinking which are extremely difficult to automate

Recognizing a Face 5Department of CSE, NSCET, Theni

6

What is AI?

2. Thinking humanly 3. Thinking rationally

1. Acting humanly 4. Acting rationally

• The top row is concerned with thought processes and

reasoning

• Second row address behaviorDepartment of CSE, NSCET, Theni

Do human behavior is rational?

• A system is rational, if it does the right thing, given what it

knows

• We can distinguish human and rational behavior and say,

human are not rational, ie., Irrational (emotionally unstable)

• However, we are not perfect

• Ex: Not everyone gets same grade (A) on the exam


AI Definitions

What is AI ?

Definitions organized into four categories

Think like human

The exciting new effort to make

computers think … machines with

minds, in the full and literal sense. [Haugeland 85].

Think Rationally

The study of the computations that

make it possible to perceive, reason,

and act. [Winston, 1992]

Act humanly

The study of how to make

computers do things at which, at

the moment, people are better. [Rich & Knight, 1991]

Act rationally

The branch of computer science that

is concerned with the automation of

intelligent behavior. [Luger and

Stubblefield, 1993]


9

1. Systems that act like humans

• The overall behaviour of the system should be

human like.

• It could be achieved by observation.


10

1. Acting Humanly: The Turing Test

• Alan Turing (1912-1954)

• “Computing Machinery and Intelligence” (1950)

Human Interrogator

Human

AI System

Imitation Game


Turing Test

?• You enter a room which has a computer terminal.

• You have a fixed period of time to type what you want into the

terminal, and study the replies.

• At the other end of the line is either a human being or a computer

system.

• If it is a computer system, and at the end of the period you cannot

reliably determine whether it is a system or a human, then the

system is deemed to be intelligent. 11Department of CSE, NSCET, Theni

12

Turing Test

computer

person

tester


Recognizing AI

• The Turing test is one criterion, but it’s controversial

– It’s an imitation game, in which a human interrogator is isolated in a room, with teletype connections to an unseen human and an unseen computer

– The interrogator asks the same questions of the human and the machine and tries to determine which is which

– If the interrogator can’t tell them apart, the computer is intelligent

• Positive features of the Turing test

– It’s objective and unbiased

– It’s independent of how the machine operates

– There are no agreed upon alternative tests13Department of CSE, NSCET, Theni

Recognizing AI

• Negative features of the Turing test

– It overlooks aspects of intelligence such as perception and

mobility

– It overlooks human intelligence

– It can be gimmicky and detract from serious AI research efforts

• What is the amount of knowledge that a machine would need to pass

Turing test ?


15

What sort of Functionality Is Needed?

• To act humanly?

– Natural language processing

– Knowledge Representation

– Automated Reasoning

– Machine Learning

– Computer Vision

– Robotics


Act Like Human

Computer would need:

1. Natural Language Processing → Communication.

2. Knowledge Representation→store info before and

during interrogation.

3. Automated Reasoning→ answer questions and draw new

conclusions.

4. Machine learning→ adapt to new circumstances.

5. Computer vision → to perceive objects

6. Robotics → to manipulate objects and move about


Turing test

• Turing test avoided physical interaction between the

interrogator and the computer

• Turing test involves first four aspects (1-4)

• Total Turing test includes a video signal so that the interrogator

can test the perceptual abilities as well as to pass physical

objects

• Total Turing test involves last two aspects (5 – 6)


2. Think Like Human

• To develop a program that think like human, the way the human think

should be known.

• Knowing the precise theory of mind ( how human think?) → expressing

the theory as a computer program.

• GPS (General Problem Solver) [ by Newell & Simon, 1961]

• Concerned with comparing the trace of its reasoning steps to traces of

human subjects solving the same problem rather than correctly solve

problems

The Cognitive Modeling approach


19

2. Thinking Humanly: Cognitive Modelling

• 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.Cognitive science:

• Computer models from AI + Experimental techniques from psychology → Construction of human mind working theories

• Requires testable theories of the workings of the human mind

• Requires experimental investigation of actual humans or animals

• Note: We will not pursue human mind theory here as we have only a computer for experimentation


20

2. Systems that think like humans

• Most of the time it is a black box where we are not clear about our

thought process.

• One has to know functioning of brain and its mechanism for

possessing information.

• It is an area of cognitive science.

– The stimuli are converted into mental representation.

– Cognitive processes manipulate representation to build new

representations that are used to generate actions.

• Neural network is a computing model for processing information

similar to brain.


21

3. Thinking Rationally: Laws of Thought

• Aristotle was one of the first to attempt to codify

“right thinking”, i.e., irrefutable reasoning processes.


3. Think Rationally

The Law of Thought Approach

Aristotle and his syllogism ( right thinking) :

always gave correct conclusions given correct premises

Ex:

• Socrates is a Man. %Fact

• All men are Mortal. % Rule if X is a Man then X is Mortal.

• Therefore Socrates is Mortal. % Inference

These laws of thoughts initiated the field of LOGIC.

Formal logic provides a precise notation and rules for representing and

reasoning with all kinds of things in the world.


3. Think Rationally

• Two main obstacles

1. Not easy to translate an informal knowledge into a formal logic.

Ex: when the knowledge is less than 100% certain

2. There is a difference between solving a problem “in principle” and

solving it “in practice”

Ex: It is usually the case that problems with few hundred facts

– Can exhaust the computational power of any computer.

– So, it is required to have some guidance as to which reasoning steps

to try first


4. Acting Rationally

The Rational Agent Approach

• Agent

agent is something that acts

• Computer agents

Operate autonomously

perceive their environments

Persist over a prolonged time period

Adapt to change, create and pursue goals

• Rational agent

Acts so as to achieve the best outcome or when there is uncertainty, the

best expected outcome



• Laws of thought approach vs. Act rationally

• Laws of thought approach

o Emphasis on correct inference

o Making correct inferences is part of being rational agent

• Act rationally = reason logically to the conclusion that a

given action will achieve one’s goals

and then act on that conclusion



• Correct inference is not always == rationality

• Ex: even if there is no correct thing to do, something must be done

• Hence, it means that there are also ways of acting rationally that

cannot be said to involve inference

• Ex: reflex actions ( acting rationally without involving inference)

• Fast action is required than careful deliberation26Department of CSE, NSCET, Theni

4. Acting RationallyTwo main advantages of rational agent approaches

1. More general than “the laws of thought” approach ( correct inference is one of

several possible mechanism to achieve rationality)

2. More amenable to scientific development than approaches based on human

behavior/thought.

Rationality vs. human behavior

• The rationality is mathematically well defined, general (many agent design have been

generated to achieve it)

• Human behavior adapted for one specific environment, defined by sum total of all

the things that humans do


28


• We focus on general principles of rational agents and on components for

constructing them

o perfect rationality

o Always doing the right thing is not feasible in complicated environments

because of high computational demands

o But a good starting point for analysis

o It simplifies the problem

• Limited rationality

o Acting appropriately when there is not enough time to do all the

computations

o Not in the syllabus


The foundations of AI

Philosophy Knowledge Rep., Logic, Foundation of AI (is AI

possible?)

Maths Search, Analysis of search algos, logic

Economics Expert Systems, Decision Theory, Principles of

Rational Behavior

Psychology Behavioristic insights into AI programs

Neuroscience (Brain Science) Learning, Neural Nets

Control theory and

Cybernetics

Information Theory & AI, Entropy, Robotics

Computer Sc. & Engg. Systems for AI


30

The Foundations of AI

1. Philosophy (423 BC - present):

2. Can formal rules be used to draw valid conclusions?

• How does the mind arise from a physical brain?

• Where does knowledge come from?

• How does knowledge lead to action?

- Logic, methods of reasoning.

- Mind as a physical system.

- Foundations of learning, language, and rationality.


31


2. Mathematics (c.800 - present):

• What are the formal rules to draw valid conclusions?

• What can be computed?

• How do we reason with uncertain information?

• Three areas developed– Logic, Computation and Probability


32


2. Mathematics (c.800 - present):

• A) Formal representation and proof

• Development of Formal logic

– 1) Propositional or Boolean logic

– 2) Development of First-Order logic by extending the Boolean logic to

include objects and relations

• B) Algorithms, computation, decidability, tractability

• First algorithm was developed - Euclid’s algorithm for computing GCD

• C) Probability – Baye’s rule is the underlying approach for uncertain

reasoning in AI systems.


33


3. Economics :

• How should we make decisions so as to maximize payoff?

• How should we do this when others may not go along?

• How should we do this when the pay off may be far in the

future ?

• Decision theory

• Game theory

• Operations Research

• Markov Decision Processes


34


4. Neuroscience:

• The study of nervous system, particularly the brain

• The exact way in which the brain enables thought is unknown

• However, it does enable thought has the evidence

• “A strong blow to the head can lead to mental incapacitation”

• Neurons – Brain consists of nerve cells or neurons

• There is no theory on how an individual memory is stored

• A collection of simple cells can lead to thought, action,

consciousness that “brains cause minds”


35


4. Neuroscience:

The parts of a nerve cell or neuron.


36

The Foundations of AI4. Neuroscience:

A comparison of resources available at IBM BLUE GENE

Supercomputer , a PC of 2008 and the human brain


37

The Foundations of AI5. Psychology (1879 - present):

• How do humans and animals think and act?

- Adaptation.

- Phenomena of perception and motor control.

- Experimental techniques.


38

The Foundations of AI6. Computer Engineering:

• How can we build an efficient computer?

• For artificial intelligence to succeed, we need two things:

• intelligence and an artifact.

• The computer has been the artifact of choice

• Each generation of computer hardware has brought an increase

in speed and capacity and a decrease in price


39

The Foundations of AI6. Computer Engineering:

• In 2005 power dissipation problems led to multiply the number of CPU

cores rather than the clock speed.

• Current expectations are that future increases in power will come from

massive parallelism

– Convergence with the properties of the brain.

• The software side of computer science has supplied AI with:

– The operating systems, programming languages, and tools needed to

write modem programs


40


7. Control theory and cybernetics:

• How can artifacts operate under their own control?

– Previous Assumption: Only living things could modify their behavior in

response to changes in the environment

– Machines can modify their behavior in response to the environment

• Ex: Water-flow regulator, steam engine governor, thermostat

• The theory of stable feedback systems (1894)

• Build systems that transition from initial

state to goal state with minimum energy

• In 1950, control theory could only describe

linear systems

• AI largely rose as a response to this shortcomingDepartment of CSE, NSCET, Theni

41


8. Linguistics (1957 - present):

- How does language relate to thought?

• Knowledge representation: the study of how to put knowledge into a form

that a computer can reason with

• Grammar

• Speech demonstrates human intelligence

– Analysis of human language reveals thought taking place in ways not

understood in other settings

• Language and thought are believed to be tightly intertwined


42

Evolution 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.

• Why was it necessary for AI to become a separate field ?

• Why couldn't all the work done in AI have taken place under the name of control theory or operations research or decision theory, which have objectives similar to those of AI?

• Why isn't AI a branch of mathematics?

• The first answer is that AI from the start embraced the idea of duplicating human faculties such as creativity, self-improvement, and language use.

• None of the other fields were addressing these issues.

• The second answer is methodology. AI is the only one of these fields that is clearly a branch of computer science (USING COMPUTER SIMULATIONS)

• AI is the only field to attempt to build machines that will function autonomously in complex, changing environments


43

Evolution of AI

• Early enthusiasm, great expectations (1952 - 1969):

• Early successful AI programs:

– Newell & Simon’s Logic Theorist

– Newell & Simon’s General Problem Solver (GPS) to imitate human problem solving

• Solved puzzles and considered sub goals and possible actions similar to which humans approached the same problems.

• Thus, GPS was probably the first program to embody the "thinking humanly" approach

– Newell & Simon’s Physical symbol system hypothesis: • which states that "a physical symbol system has the necessary and sufficient means for general intelligent action."

• Any system (human or machine) exhibiting intelligence must operate by manipulating data structures composed of symbols.


44

Evolution of AI

• Early enthusiasm, great expectations (1952 - 1969):

• Early successful AI programs: – Samuel’s Checkers– Gelernter’s Geometry Theorem Prover– Robinson’s complete algorithm for logical reasoning. – At 1958 in MIT AI Lab McCarthy defined the high-level

language Lisp• which was the dominant AI programming language for the

next 30 years.


45

Evolution of AI• A dose of reality (1966 - 1973):

• The first kind of difficulty arose because most early programs knew

nothing of their subject matter

• They succeeded by means of simple syntactic manipulations

• Early machine translation efforts to speed up the translation of Russian

scientific papers in the wake of the Sputnik launch in 1957.

• It was thought initially that simple syntactic transformations based on the

grammars of Russian and English, and word replacement from an

electronic dictionary, would suffice to preserve the exact meanings of

sentences.

• The fact is that accurate translation requires background knowledge in

order to resolve ambiguity and establish the content of the sentence.Department of CSE, NSCET, Theni

46

Evolution of AI

• Knowledge-based systems (1969 - 1979):

• Uses more powerful, domain-specific knowledge

• Allows larger reasoning steps and

• Can more easily handle typical expertise.

• Ex1 :1969: DENDRAL by Buchanan et al.

– generated all possible structures consistent with the formula of the molecule

– The significance of DENDRAL was that it was the first successful knowledge-intensive system:

– its expertise derived from large numbers of special-purpose rules.


47

Evolution of AI

• Knowledge-based systems (1969 - 1979):

• Ex2: 1976: MYCIN by Shortliffle• Expert systems in medical diagnosis • With about 450 rules, MYCIN was better than junior doctors.

• Two major differences from DENDRAL. – First, unlike the DENDRAL rules, no general theoretical model existed from which the

MYCIN rules could be deduced.

– They had to be acquired from extensive interviewing of experts, who in turn acquired them from textbooks, other experts, and direct experience of cases.

– Second, the rules had to reflect the uncertainty associated with medical knowledge.

– MYCIN incorporated a calculus of uncertainty called certainty factors which seemed (at the time) to fit well with how doctors assessed the impact of evidence on the diagnosis.


48

Evolution of AI

• AI becomes an industry (1980 - present):

- Expert systems industry booms.

1981: Japan’s 10-year Fifth Generation project to build

intelligent computers.

• The return of NNs (1986 - present):

- Mid 80’s: Back-propagation learning algorithm

reinvented.

- 1988: Resurgence of probability.


49

Evolution of AI

• AI adopts the scientific method (1987 - present):

• In terms of methodology, AI has finally come firmly under the scientific method.

• To be accepted, hypotheses must be subjected to rigorous empirical experiments

– and the results must be analyzed statistically for their importance

– It is now possible to replicate experiments by using shared repositories of test data and code.


50

Evolution of AI

• The emergence of intelligent agents (1995- present)

• One of the most important environments for intelligent agents is the Internet.

• AI systems have become so common in Web-based applications that the "-bot'' suffix has entered everyday language.

• Internet tools, such as search engines, recommender systems, and Web site aggregators use AI technologies


51

Evolution of AI

• The availability of very large data sets( 2001-present)

• In the 60-year history of computer science, the emphasis has been on the algorithm as the main subject of study.

• But recent work in AI suggests that for many problems, it makes more sense to worry about the data

• This is true because of the increasing availability of very large data sources

Examples:

• trillions of words of English, billions of images from the Web, or billions of base pairs of genomic sequences


52

Evolution of AI

• The availability of very large data sets( 2001-present)

• Work like this suggests the "knowledge bottleneck'' in AI

• The problem of how to express all the knowledge that a

system needs may be solved in many applications by

learning methods rather than hand-coded knowledge

engineering


What can AI do today?1. Robotic vehicles:

• A driverless robotic car named STANLEY sped through the rough terrain

of the Mojave dessert at 22 mph, finishing the 132-mile course first to win

the 2005 DARPA Grand Challenge.

• STANLEY is fitted with cameras, radar, and laser range finders to sense the

environment and on board software to command the steering braking, and

acceleration (Thrun, 2006).

• The following year CMU's Boss won the Urban Challenge, safely driving

in traffic through the streets of a closed Air Force base, obeying traffic rules

and avoiding pedestrians and other vehicles


What can AI do today?

2. Speech recognition:

• A traveler calling United Airlines to book a flight can have the

entire conversation guided by an automated speech recognition

and dialog management system.

3. Autonomous planning and scheduling:

• A hundred million miles from Earth, NASA's Remote Agent

program became the first on-board autonomous planning

program to control the scheduling of operations for a

spacecraft54Department of CSE, NSCET, Theni


4. Game playing:

• IBM’s Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997

• Human champions studied Kasparov's loss ,were able to draw

a few matches in subsequent years but the most recent human-

computer matches have been won convincingly by the

computer.



5. Spam fighting:

• Each day, learning algorithms classify over a billion messages

as spam saving the recipient from having to waste time

deleting , for many users it could comprise 80% or 90% of all

messages, if not classified away by algorithms.

• Because the spammers are continually updating their tactics

it is difficult for a static programmed approach to keep up and

learning algorithms work best



6. Logistics planning:

• During the 1991 Gulf War, US forces deployed an AI logistics

planning and scheduling program

• that involved up to 50,000 vehicles, cargo, and people

• Parameters are starting points, destinations, routes, and

conflict resolution

7. Robotics:

• The iRobot Corporation has sold over two million Roomba

robotic vacuum cleaners for home use. 57Department of CSE, NSCET, Theni

What can AI do today?• 8. Machine Translation:

• A computer program automatically translates from Arabic to English

• The program uses a statistical model built from examples of Arabic-to-

English translations and from examples of English text

• There are two trillion words (Brants et at., 2007).

• None of the computer scientists on the team speak Arabic

• 9. No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego)

• 10. Proverb solves crossword puzzles better than most humans


Applications of AIFinance

• Banks use artificial intelligence systems to organize operations,

invest in stocks, and manage properties

• Loan investigation, ATM design, safe and fast banking etc. also

uses AI.

• In August 2001, robots beat humans in a simulated financial

trading competition

Department of CSE, NSCET, Theni 59

Hospitals and medicine

• A medical clinic can use artificial intelligence systems to

organize bed schedules, make a staff rotation, and provide

medical information and other important tasks.

• AI has also applications in field of cardiology (CRG),

Neurology (MRI), Embryology (sonography), complex

operations of internal organs etc.


Heavy industry

• Huge machines involves risks in manual maintenance and

working .

• Robots are safe and efficient agent for it.

• They are often given jobs that are considered dangerous to

humans.

• Robots have proven effective in jobs that are very repetitive

which may lead to mistakes or accidents due to a lapse in

concentration and other jobs which humans may find

degradingDepartment of CSE, NSCET, Theni 61

Telecommunications maintenance and Gaming

• Many telecommunications companies make use of heuristic

search in the management of their workforces,

• for example BT Group has deployed heuristic search in a

scheduling application that provides the work schedules of

20,000 engineers.

• AI has also been applied to video games, for example video

game bots, which are designed to stand in as opponents where

humans aren't available or desired


Music

• Scientists are trying to make the computer emulate the

activities of the skillful musician.

• Composition, performance, music theory, sound processing are

some of the major areas on which research in Music and

Artificial Intelligence are focusing.


Antiviruses

• AI has played increasingly played important role in Antivirus

detection.

• At present, some principle AI techniques applied in antivirus

detection.

• It improves the performance of antivirus detection systems and

promotes the production of new AI algorithms and application

in antivirus detection to integrate antivirus detection with AI.


INTELLIGENT AGENTS

• An agent is anything that can be viewed as perceiving its

environment through sensors andacting upon that environment

through actuators.

• Different types of agents

• 1. A human agent has eyes, ears, and other organs for sensors

and hands, legs, mouth, and other body parts for actuators.


INTELLIGENT AGENTS

• 2. A robotic agent might have cameras and infrared range

finders for sensors and various motors for actuators.

• 3. A software agent receives keystrokes, file contents, and

network packets as sensory inputs and acts on the environment

by displaying on the screen, writing files, and sending network

packets.

• 4. Generic agent – A general structure of an agent who

interacts with the environment.


INTELLIGENT AGENTS

• The term percept is to refer to the agent's perceptual inputs at

any given instant.

• PERCEPT SEQUENCE: Agent's percept sequence is the

complete history of everything the agent has ever perceived.

• An agent's behavior is described by the agent function that

maps any given percept sequence to an action.

• AGENT PROGRAM : The agent function for an artificial

agent will be implemented by an agent program.


• Definition: An Intelligent Agent perceives it environment via sensors and

acts rationally upon that environment with its effectors.

• Hence, an agent gets percepts one at a time, and maps this percept sequence

to actions.

• Properties

– Autonomous

– Interacts with other agents

plus the environment

– Reactive to the environment

– Pro-active (goal- directed) future problems

Intelligent Agents


sensors/percepts and effectors/actions

• Humans

– Sensors: Eyes (vision), ears (hearing), skin (touch), tongue (gustation), nose (olfaction).

– Percepts:

• At the lowest level – electrical signals from these sensors

• After preprocessing – objects in the visual field (location, textures, colors, …), auditory streams (pitch, loudness, direction), …

– Effectors: limbs, digits, eyes, tongue, …

– Actions: lift a finger, turn left, walk, run, carry an object, …

• The Point: percepts and actions need to be carefully defined, possibly at different levels of abstraction


A more specific example:

Automated taxi driving system

• Percepts: Video, sonar, speedometer, odometer, engine

sensors, keyboard input, microphone, GPS, …

• Actions: Steer, accelerate, brake, horn, speak/display, …

• Goals: Maintain safety, reach destination, maximize profits

(fuel, tire wear), obey laws, provide passenger comfort, …

• Environment: U.S. urban streets, freeways, traffic,

pedestrians, weather, customers, …

• Different aspects of driving may require different types of

agent programs! 70Department of CSE, NSCET, Theni

Rationality

• An ideal rational agent should, for each possible percept

sequence, do whatever actions that will maximize its

performance measure based on

(1) the percept sequence, and

(2) its built-in and acquired knowledge.

• Hence it includes information gathering, not "rational

ignorance."

• Rationality => Need a performance measure to say how well a

task has been achieved.


Autonomy

• A system is autonomous to the extent that its own behavior is

determined by its own experience and knowledge.

• Therefore, a system is not autonomous if it is guided by its

designer according to a priori decisions.

• To survive agents must have:

– Enough built- in knowledge to survive.

– Ability to learn.


Examples of Agent Types and their

Descriptions


Some Agent Types

• Table-driven agents

– use a percept sequence/ action table in memory to find the

next action. They are implemented by a (large) lookup table.

• Simple reflex agents

– are based on condition-action rules and implemented with an

appropriate production (rule-based) system. They are stateless

devices which do not have memory of past world states.

• Agents with memory

– have internal state which is used to keep track of past states of

the world.74Department of CSE, NSCET, Theni

Some Agent Types

• Agents with goals

– are agents which in addition to state information have a

kind of goal information which describes desirable

situations. Agents of this kind take future events into

consideration.

• Utility-based agents

– base their decision on classic axiomatic utility-theory in

order to act rationally.


Simple Reflex Agent

• Table lookup of percept- action pairs defining all possible

condition- action rules necessary to interact in an environment

• Problems

– Too big to generate and to store (Chess has about 10^120

states, for example)

– Not adaptive to changes in the environment; requires entire

table to be updated if changes occur

• Use condition-action rules to summarize portions of the table

If Car-in-front –is –braking then initiate-braking


A Simple Reflex Agent: Schema En

vir

on

me

nt

Agent

What the world is like now

What action I should do now

Condition-action rules

Sensors

Effectors


Reflex Agent with Internal State

• Encode "internal state" of the world to remember the past as

contained in earlier percepts

• Needed because sensors do not usually give the entire state of the

world at each input, so perception of the environment is captured

over time. "State" used to encode different "world states" that

generate the same immediate percept.

• Requires ability to represent change in the world; one possibility

is to represent just the latest state, but then can't reason about

hypothetical courses of action78Department of CSE, NSCET,

Theni

Agents that Keep Track of the World


Goal- Based Agent

• Choose actions so as to achieve a (given or computed) goal.

• A goal is a description of a desirable situation

• Keeping track of the current state is often not enough -- need

to add goals to decide which situations are good

• May have to consider long sequences of possible actions

before deciding if goal is achieved -- involves consideration of

the future, “what will happen if I do...?”


Agents with Explicit Goals


Utility- Based Agent

• When there are multiple possible alternatives, how to decide which one is

best?

• A goal specifies a crude distinction between a happy and unhappy state, but

often need a more general performance measure that describes "degree of

happiness"

• Utility function U: States --> Reals indicating a measure of success or

happiness when at a given state

• Allows decisions comparing choice between conflicting goals, and choice

between likelihood of success and importance of goal (if achievement is

uncertain)82Department of CSE, NSCET, Theni

A Complete Utility- Based Agent

En

vir

on

me

nt

Sensors

Effectors

What the world is like now

What it will be like if I do

action A

Utility

State

How the world evolves

What my actions do

What action I should do now

How happy I will be in such a state


Properties of Environments• Accessible/ Inaccessible.

– If an agent's sensors give it access to the complete state of the environment needed

to choose an action, the environment is accessible.

– Such environments are convenient, since the agent is freed from the task of keeping

track of the changes in the environment.

• Deterministic/ Non-deterministic.

– An environment is deterministic if the next state of the environment is completely

determined by the current state of the environment and the action of the agent.

– In an accessible and deterministic environment the agent need not deal with

uncertainty.

• Episodic/ Nonepisodic.

– An episodic environment means that subsequent episodes do not depend on what

actions occurred in previous episodes.

– Such environments do not require the agent to plan ahead.


Properties of Environments• Static/ Dynamic.

– A static environment does not change while the agent is thinking.

– In a static environment the agent need not worry about the passage of

time while he is thinking, nor does he have to observe the world while

he is thinking.

– In static environments the time it takes to compute a good strategy does

not matter.

• Discrete/ Continuous.

– If the number of distinct percepts and actions is limited the environment

is discrete, otherwise it is continuous.


Characteristics of environments

Accessible Deterministic Episodic Static Discrete

Solitaire No Yes Yes Yes Yes

Backgammon Yes No No Yes Yes

Taxi driving No No No No No

Internet shopping No No No No No

Medical diagnosis No No No No No



88


Giurgiu

UrziceniHirsova

Eforie

Neamt

Oradea

Zerind

Arad

Timisoara

Lugoj

Mehadia

Dobreta

Craiova

Sibiu Fagaras

Pitesti

Vaslui

Iasi

Rimnicu Vilcea

Bucharest


90


1 2

3 4

5 6

7 8

91




Start State Goal State

2

45

6

7

8

1 2 3

8 4

7 6 5

1

23

45

6

7

8

5 4

6 1 8

7 3 2

94


Start State Goal State

2

45

6

7

8

1 2 3

8 4

7 6 5

1

23

45

6

7

8

5 4

6 1 8

7 3 2


R

L

S S

S S

R

L

R

L

R

L

S

SS

S

L

L

LL R

R

R

R


R

L

S S

S S

R

L

R

L

R

L

S

SS

S

L

L

LL R

R

R

R

97


R

RRP

R R


Documents

NSCET E-LEARNING PRESENTATION NOTES/unit 1/CS8691_AI.pdf · 4 What is AI? - definition •AI is the study of how to make computers make things which at the moment people do better