Introduction - Learning Agents Center - George Mason University

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15 Οκτ 2013 (πριν από 4 χρόνια και 2 μήνες)

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1

Learning Agents Laboratory

Computer Science Department

George Mason University

Prof. Gheorghe Tecuci

1. Introduction

2

Overview

What is Machine Learning

History of Machine Learning

Basic bibliography and reading

What is Artificial Intelligence

3

What is Artificial Intelligence

4

Central goals of Artificial Intelligence

Understand the principles that make intelligence possible

(in humans, animals, and artificial agents)

Developing intelligent machines or agents

(no matter whether they operate as humans or not)

Formalizing knowledge and mechanizing reasoning

in all areas of human endeavor

Making the working with computers


as easy as working with people

Developing human
-
machine systems that exploit the

complementariness of human and automated reasoning

5

What is an intelligent agent

Intelligent

Agent

user/

environment

output/

sensors

effectors

input/

An
intelligent agent

is a system that:




perceives its environment (which may be the physical


world, a user via a graphical user interface, a collection of


other agents, the Internet, or other complex environment);




reasons to interpret perceptions, draw inferences, solve


problems, and determine actions; and




acts upon that environment to realize a set of goals or


tasks for which it was designed.

6

Characteristic features of intelligent agents

Knowledge representation and reasoning

Transparency and explanations

Ability to communicate

Use of huge amounts of knowledge

Exploration of huge search spaces

Use of heuristics

Reasoning with incomplete or conflicting data

Ability to learn and adapt

7

Overview

What is Machine Learning

History of Machine Learning

What is Artificial Intelligence

Basic bibliography and reading

8

What is Machine Learning

9

The architecture of a learning agent

Ontology


Rules/Cases/Methods

Problem Solving

Engine

Learning Agent

User/

Environment

Output/

Sensors

Effectors

Input/

Knowledge Base

Learning

Engine

Implements
learning
methods

for extending
and refining

the knowledge

base to
improve
agent’s
competence
and/or
efficiency in
problem
solving.

Implements a general problem solving method that uses
the knowledge from the knowledge base to interpret the
input and provide an appropriate output.

Data structures that represent the objects from the application domain,

general laws governing them, actions that can be performed with them, etc.


10

What is Learning?

Learning denotes 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
effectively the next time (Simon, 1983).

Learning is making useful changes in our minds (Minsky,
1985).

Learning is constructing or modifying representations of
what is being experienced (Michalski, 1986).

A computer program learns if it improves its performance at
some task through experience (Mitchell, 1997).

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So what is Learning?


(1)
acquire and organize knowledge (by building,
modifying and organizing internal representations of
some external reality);


(2)
discover new knowledge and theories (by creating
hypotheses that explain some data or phenomena);


(3)
acquire skills (by gradually improving their motor or
cognitive skills through repeated practice, sometimes
involving little or no conscious thought).

Learning results in changes in the agent (or mind) that
improve its competence and/or efficiency.

Learning is a very general term denoting the way in
which people and computers:

12

Two complementary dimensions for learning

A system is improving its competence if it learns to solve a
broader class of problems, and to make fewer mistakes in
problem solving.

A system is improving its efficiency, if it learns to solve the
problems from its area of competence faster or by using
fewer resources.

Competence

Efficiency

13

Main directions of research in Machine Learning

Discovery of general principles, methods,

and algorithms of learning

Automation of the construction

of knowledge
-
based systems

Modeling human learning mechanisms

14

Learning strategies




Rote learning




Learning from instruction



Learning from examples




Explanation
-
based learning




Conceptual clustering




Quantitative discovery




Abductive learning




Learning by analogy



Instance
-
based learning



Reinforcement learning



Neural networks



Genetic algorithms and


evolutionary computation



Reinforcement learning



Bayesian learning



Multistrategy learning

A Learning Strategy is a basic form of learning characterized
by the employment of a certain type of inference (like
deduction, induction or analogy) and a certain type of
computational or representational mechanism (like rules,
trees, neural networks, etc.).

15

Overview

What is Machine Learning

History of Machine Learning

What is Artificial Intelligence

Basic bibliography and reading

16

History of Machine Learning

Early enthusiasm (1955
-

1965)



Learning without knowledge;


Neural modeling (self
-
organizing systems and decision
space techniques);


Evolutionary learning;


Rote learning (Samuel Checker’s player).

17

History of Machine Learning (cont.)

Dark ages (1962
-

1976)



To acquire knowledge one needs knowledge;


Realization of the difficulty of the learning process and of
the limitations of the explored methods (e.g. the
perceptron cannot learn the XOR function);


Symbolic concept learning (Winston’s influential thesis,
1972).

18

History of Machine Learning (cont.)

Renaissance (1976
-

1988)



Exploration of different strategies (EBL, CBR, GA, NN,
Abduction, Analogy, etc.);


Knowledge
-
intensive learning;


Successful applications;


Machine Learning conferences/workshops worldwide.

19

History of Machine Learning (cont.)

Maturity (1988
-

present)



Experimental comparisons;


Revival of non
-
symbolic methods;


Computational learning theory;


Multistrategy learning;


Integration of machine learning and knowledge
acquisition;


Emphasis on practical applications.

20

Successful applications of Machine Learning


Learning to recognize spoken words (all of the most
successful systems use machine learning);


Learning to drive an autonomous vehicle on public
highway;


Learning to classify new astronomical structures (by
learning regularities in a very large data base of image
data);


Learning to play games;


Automation of knowledge acquisition from domain
experts;


Learning agents.

21

Basic bibliography

Mitchell T.M.,
Machine Learning,

McGraw Hill, 1997.

Shavlik J.W. and Dietterich T. (Eds.),
Readings in Machine Learning
, Morgan Kaufmann,
1990.

Buchanan B., Wilkins D. (Eds.),
Readings in Knowledge Acquisition and Learning:
Automating the Construction and the Improvement of Programs
, Morgan Kaufmann, 1992.

Langley P.,
Elements of

Machine Learning,

Morgan Kaufmann, 1996.

Michalski R.S., Carbonell J.G., Mitchell T.M. (Eds),
Machine Learning: An Artificial
Intelligence Approach,

Morgan Kaufmann, 1983 (Vol. 1), 1986 (Vol. 2).

Kodratoff Y. and Michalski R.S. (Eds.)
Machine Learning: An Artificial Intelligence
Approach

(Vol. 3), Morgan Kaufmann Publishers, Inc., 1990.

Michalski R.S. and Tecuci G. (Eds.),
Machine Learning: A Multistrategy Approach

(Vol. 4),
Morgan Kaufmann Publishers, San Mateo, CA, 1994.

Tecuci G. and Kodratoff Y. (Eds.),
Machine Learning and Knowledge Acquisition:
Integrated Approaches,

Academic Press, 1995.

Tecuci G.,
Building Intelligent Agents: An Apprenticeship Multistrategy Learning

Theory, Methodology, Tool and Case Studies
, Academic Press, 1998.

22

Recommended reading

Mitchell T.M.,
Machine Learning,

Chapter 1: Introduction, pp. 1
-
19, McGraw
Hill, 1997.