Transcript Document

1. Introduction
Prof. Gheorghe Tecuci
Learning Agents Laboratory
Computer Science Department
George Mason University
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Overview
What is Artificial Intelligence
What is Machine Learning
History of Machine Learning
Basic bibliography and reading
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What is Artificial Intelligence
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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
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What is an intelligent agent
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.
input/
sensors
user/
environment
output/
effectors
Intelligent
Agent
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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
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Overview
What is Artificial Intelligence
What is Machine Learning
History of Machine Learning
Basic bibliography and reading
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What is Machine Learning
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The architecture of a learning agent
Implements a general problem solving method that uses
the knowledge from the knowledge base to interpret the
input and provide an appropriate output.
Learning Agent
Input/
Sensors
User/
Environment
Problem Solving
Engine
Learning
Engine
Output/
Effectors
Knowledge Base
Ontology
Rules/Cases/Methods
Implements
learning
methods
for extending
and refining
the knowledge
base to
improve
agent’s
competence
and/or
efficiency in
problem
solving.
Data structures that represent the objects from the application domain,
general laws governing them, actions that can be performed with them, etc.
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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?
Learning is a very general term denoting the way in
which people and computers:
(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.
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Two complementary dimensions for learning
Competence
A system is improving its competence if it learns to solve a
broader class of problems, and to make fewer mistakes in
problem solving.
Efficiency
A system is improving its efficiency, if it learns to solve the
problems from its area of competence faster or by using
fewer resources.
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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
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Learning strategies
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.).
• Rote learning
• Instance-based learning
• Learning from instruction
• Reinforcement learning
• Learning from examples
• Neural networks
• Explanation-based learning
• Genetic algorithms and
evolutionary computation
• Conceptual clustering
• Quantitative discovery
• Abductive learning
• Learning by analogy
• Reinforcement learning
• Bayesian learning
• Multistrategy learning
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Overview
What is Artificial Intelligence
What is Machine Learning
History of Machine Learning
Basic bibliography and reading
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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).
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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).
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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.
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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.
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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.
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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.
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Recommended reading
Mitchell T.M., Machine Learning, Chapter 1: Introduction, pp. 1-19, McGraw
Hill, 1997.
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