AI PPT

LEARNING IN ARTIFICIAL INTELLIGENCE

LEARNING DEFINITION

Changes in the system that are adaptive

in the sense that they enable the system

to do the same task or tasks drawn from

the same population more efficiently and

more effectively the next time

WHAT IS LEARNING? Machines cannot be called intelligent until they are able to learn to do new

things and adapt to new situations, rather than simply doing as they are told

to do.

Adapting to new surroundings and to solve new problems is an important

characteristics of intelligent entities

Learning covers a wide range of phenomenon:

-Skill refinement : Practice makes skills improve. More you play Cricket,

better you get

-Knowledge acquisition: Knowledge is generally acquired through

experience

VARIOUS LEARNING MECHANISMS

Simple storing of computed information or rote learning, is the most basic

learning activity

Many computer programs i.e. database systems can be said to learn in this

sense

Advice Taking

Own problem-solving experience Learning from examples : we often learn to classify things in the world

without being given explicit rules

Learning from examples usually involves a teacher who helps us classify things by correcting us when we are wrong

ROTE LEARNING

ROTE LEARNING Rote learning is the basic learning activity. It is also called

memorization because the knowledge, without any modification is,

simply coped into the knowledge base

In case of data caching, we store computed values so that we do not

have to recomputed them later

When computation is more expensive than recall, this strategy can

save a significant amount of time

Caching in AI programs can be used to produce some surprising

performance improvements

Such Caching is known as rote learning

LEARNING BY TAKING ADVICE

LEARNING BY TAKING ADVICE This is a simple form of learning

A programmer writes a set of instructions to instruct the computer

what to do, the programmer is a teacher and the computer is a

student. Once learned (i.e. programmed), the system will be in a

position to do new things

In chess, the advice “fight for control of the center of the board” is

useless unless the player can translate the advice into concrete

moves and plans. A computer program might make use of the

advice by adjusting its static evaluation function to include a factor

based on the number of center squares attacked by its own pieces

LEARNING BY TAKING ADVICE( CONTD…)

FOO (First Operational Operationaliser), for example, is

a learning system which is used to learn the game of

Hearts. It converts the advice which is in the form of

principles, problems, and methods into effective

executable (LISP) procedures (or knowledge). Now this

knowledge is ready to use.

LEARNING IN PROBLEM SOLVING

LEARNING IN PROBLEM SOLVING

Can program get better without the aid of a teacher?

It can be by generalizing from its own experiences

Learning in problem solving we have:

-Learning by parameter adjustment

-Learning by Macro-Operators

-Learning by Chunking

-The Utility Program

-Learning from Example : INDUCTION

LEARNING BY PARAMETER ADJUSTMENT

Many programs rely on an evaluation procedure that combines information from

several sources into a single summary statistic

For Example: Game Playing reflecting the desirability of a particular board

position and Pattern classification programs to determine the correct category into

which a given stimulus should be placed

In designing such programs, it is often difficult to know a priori how much weight

should be attached to each feature being used

One way of finding the correct weights is to begin with some estimate of the correct

settings and then to let the program modify the settings on the basis of its experience

LEARNING BY MACRO-OPERATORS Sequences of actions that can be treated as a whole are called macro-operators

Example: suppose you are faced with the problem of getting to the downtown post

office. Your solution may involve getting in your car, starting it, and driving along a

certain route. Substantial planning may go into choosing the appropriate route, but

you need not plan about how to about starting the car. You are free to treat START-

CAR as an atomic action, even though it really consists of several actions: sitting

down, adjusting the mirror, inserting the key, and turning the key

After each problem solving episode, the learning component takes the computed

plan and stores it away as a macro-operator, or MACROP

MACROP is just like a regular operator

LEARNING BY CHUNKING Chunking is similar to learning with macro-operators

 it is used by problem solver systems that make use of production systems

When a system detects useful sequence of production firings, it creates chunk,

which is essentially a large production that does the work of an entire sequence of

smaller ones

SOAR is an example production system which uses chunking

Chunks learned during the initial stages of solving a problem are applicable in the

later stages of the same problem-solving episode

After a solution is found, the chunks remain in memory, ready for use in the next

problem

THE UTILITY PROBLEM While new search control knowledge can be of great benefit in

solving future problems efficiently, there are also some drawbacks:

large amounts of memory Required Time Consuming If we only want to minimize the number of node expansions in

the search space, then the more control rules we learn, the better

If we want to minimize the total CPU time required to solve a problem, we must consider this trade off

LEARNING FROM EXAMPLES: INDUCTION

The idea of producing a classification program that can evolve its own class

definitions is called concept learning or induction

Classification is the process of assigning, to a particular input, the name of a

class to which it belongs

The classes from which the classification procedure can choose can be

described in a variety of ways

Classification is an important component of many problem solving tasks

Before classification can be done, the classes it will use must be defined:

-Isolate a set of features that are relevant to the task domain

-Isolate a set of features that are relevant to the task domain

DISCOVERY

DISCOVERYo Learning is the process by which one entity acquires knowledge.

Usually that knowledge is already possessed by some number of

other entities who may serve as teachers

o Discovery is a restricted form of learning in which one entity

acquires knowledge without the help of a teacher

- Theory-Driven Discovery

- Data Driven Discovery

- Clustering

AM : THEORY-DRIVEN DISCOVERY

o AM is a program that discovers concepts in elementary mathematics

and set theory

oAM is written by Lenat and it worked from a few basic concepts of set

theory to discover a good deal of standard number theoryoAM has 2 inputs: - A description of some concepts of set theory (in LISP form),E.g. set union

-Information on how to perform mathematics. E.g. functions

oAM exploited a variety of general-purpose AI techniques. It used a

frame system to represent mathematical concepts. One of the major

activities of AM is to create new concepts and fill in their slots

HOW DOES AM WORKS?

BACON: DATA DRIVEN DISCOVERY

o Many discoveries are made from observing data obtained from the

world and making sense of it -- E.g. Astrophysics - discovery of planets,

Quantum mechanics , discovery of sub-atomic particles

o BACON is an attempt at provided such an AI system

o BACON begins with a set of variables for a problem

For example in the study of the behavior of gases, some

variables are p, the pressure on the gas, V, the volume of the gas, n, the

amount of gas in moles, and T the temperature of the gas

o Physicists have long known a law, called ideal gas law, that relates these

variables

HOW DOES BACON WORKS?o First, BACON holds the variables n and T constant, performing

experiments at different pressures p1, p2 and p3

o BACON notices that as the pressure increases, the volume V decreases

o For all values, n, p, V and T, pV/nT = 8.32 which is ideal gas law as

shown by BACON

o BACON has been used to discover wide variety of scientifc laws such

as Kepler’s third law, Ohm’s law, the conservation of momentum and

Joule’s law

o BACON’s discovery procedure is state-space search

CLUSTERINGo A cluster is a collection of objects which are similar in some way. Clustering

groups data items into similarity classes. The properties of these classes can then

be used to understand problem characteristics or to find similar groups of data

items. Clustering can be defined as the process of reducing a large set of unlabeled

data to manageable piles consisting of similar items. The similarity measures

depend on the assumptions and desired usage one brings to the data

oClustering begins by doing feature extraction on data items and measure the

values of the chosen feature set. Then the clustering model selects and compares

two sets of data items and outputs the similarity measure between them

ANY QUESTION