CS 782 Machine Learning

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CS 782 Machine Learning
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
Demo Disciple learning agent
Basic bibliography and reading
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What is Artificial Intelligence
Artificial Intelligence is the Science and
Engineering that is concerned with the theory and
practice of developing systems that exhibit the
characteristics we associate with intelligence in
human behavior perception, natural language
processing, reasoning, planning and problem
solving, learning and adaptation, etc.
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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
reasoningin 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
IntelligentAgent
output/
user/ environment
effectors
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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
Demo Disciple learning agent
Basic bibliography and reading
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What is Machine Learning
Machine Learning is the domain of Artificial
Intelligence which is concerned with building
adaptive computer systems that are able to
improve their competence and/or efficiency
through learning from input data or from their
own problem solving experience.
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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.
Implements learning methods for extending and
refining the knowledge base to improve agents
competence and/or efficiency in problem solving.
Learning Agent
Problem Solving Engine
Input/
Sensors
Learning Engine
User/ Environment
Output/
Ontology Rules/Cases/Methods
Knowledge Base
Effectors
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

• Acquire, discover, and organize knowledge (by
  building, modifying and organizing internal
  representations of some external reality)
• 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 constructionof knowledge-based
systems
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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 (e.g. deduction, induction or
analogy), a certain type of computational or
representational mechanism (e.g. rules, trees,
neural networks, etc.), and a certain type of
learning goal (e.g. learn a concept, discover a
formula, acquire new knowledge about an entity,
refine an entity).

• Instance-based learning
• Reinforcement learning
• Neural networks
• Genetic algorithms and evolutionary
  computation
• Reinforcement learning
• Bayesian learning
• Multistrategy learning

• Rote learning
• Learning from instruction
• Learning from examples
• Explanation-based learning
• Conceptual clustering
• Quantitative discovery
• Abductive learning
• Learning by analogy

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Overview
What is Artificial Intelligence
What is Machine Learning
History of Machine Learning
Demo Disciple learning agent
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 Checkers 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 (Winstons 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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Overview
What is Artificial Intelligence
What is Machine Learning
History of Machine Learning
Demo Disciple learning agent
Basic bibliography and reading
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Disciple approach to agent development
Disciple is a theory, methodology and agent shell
for rapid development of end to end knowledge
bases and agents, by subject matter experts, with
limited assistance from knowledge engineers
The expert teaches Disciple in a way that
resembles how the expert would teach a person.
Disciple learns from the expert, building,
verifying and improving its knowledge base
DISCIPLE RKF/COG
Problem Solving
Ontology Rules
Interface
Learning
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Vision on the evolution of the software
development process
Mainframe Computers
Software systems developed and used by computer
experts
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Vision on the use of Disciple in Education
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An intelligent agent for Center of Gravity
analysis
The center of gravity of an entity (state,
alliance, coalition, or group) is the foundation
of capability, the hub of all power and movement,
upon which everything depends, the point against
which all the energies should be directed. Carl
Von Clausewitz, On War, 1832.
If a combatant eliminates or influences the
enemys strategic center of gravity, then the
enemy will lose control of its power and
resources and will eventually fall to defeat. If
the combatant fails to adequately protect his own
strategic center of gravity, he invites
disaster. (Giles and Galvin, USAWC 1996).
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Approach to Center of Gravity (COG) analysis

• Based on the concepts of critical capabilities,
  critical requirements and critical
  vulnerabilities, which have been recently adopted
  into the joint military doctrine of USA (Strange
  , 1996).
• Applied to current war scenarios (e.g. War on
  terror 2003, Iraq 2003) with state and non-state
  actors (e.g. Al Qaeda).

Identification of COG candidates
Testing of COG candidates
Identify potential primary sources of moral or
physical strength, power and resistance from
Test each identified COG candidate to determine
whether it has all the necessary critical
capabilities
Which are the critical capabilities? Are the
critical requirements of these capabilities
satisfied? If not, eliminate the candidate. If
yes, do these capabilities have any vulnerability?
Government Military People Economy Alliances Etc.
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Critical capabilities needed to be a COG
people
leader
military
receive communication from the highest level
leadership
be protected
be deployable
stay informed
exert power
communicate desires to the highest level
leadership
communicate
be indispensable
industrial capacity
be influential
support the goal
financial capacity
be a driving force
support the highest level leadership
external support
have support
will of multi member force
have a positive impact
be irreplaceable
be influential
ideology
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Leader who is a COG
Critical capability to
Corresponding critical requirement
be protected
Have means to be protected from all threats
stay informed
Have means to receive essential intelligence
Have means to communicate with the government,
the military and the people
communicate
Have means to influence the government, the
military and the people
be influential
Have reasons and determination for pursuing the
goal
be a driving force
Have means to secure continuous support from the
government, the military and the people
have support
be irreplaceable
Be the only leader to maintain the goal
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Illustration Saddam Hussein (Iraq 2003)
Critical capability to
be protected
Corresponding critical requirement
Have means to be protected from all threats
?
Means
Vulnerabilities
Republican Guard Protection Unit ? loyalty not
based on conviction and can be influenced by
US-led coalition
Iraqi Military ? loyalty can be influenced by
US-led coalition ? can be destroyed by US-led
coalition
Complex of Iraqi Bunkers ? location known to US
led coalition ? design known to US led
coalition ? can be destroyed by US-led coalition

• System of Saddam Doubles
• ? loyalty of Saddam Doubles to Saddam can be
  influenced by US-led coalition

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Demonstration
Teaching Disciple how to determine whether a
strategic leader has the critical capability to
be protected.
DiscipleDemo
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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. Tecuci G., Boicu M., Marcu D., Stanescu
B., Boicu C., Comello J., Training and Using
Disciple Agents A Case Study in the Military
Center of Gravity Analysis Domain, in AI
Magazine, 24, 4, 2002, pp.51-68, AAAI Press,
Menlo Park, California, 2002, http//lalab.gmu.edu
/publications/default.htm