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Machine Learning
Wilma Bainbridge
Tencia Lee
Kendra Leigh
What is Machine Learning?
Machine learning is the process in which a machine changes its
structure, program, or data in response to external information
in such a way that its expected future performance improves.
Learning by machines can overlap with simpler processes, such
as the addition of records to a database, but other cases are
clear examples of what is called “learning,” such as a speech
recognition program improving after hearing samples of a
person’s speech.
Components of a Learning Agent
•
Curiosity Element – problem
generator; knows what the agent
wants to achieve, takes risks
(makes problems) to learn from
• Learning Element – changes the
future actions (the performance
element) in accordance with the
results from the performance
analyzer
• Performance Element – choosing
actions based on percepts
• Performance Analyzer – judges
the effectiveness of the action,
passes info to the learning element
Why is machine learning important?
Or, why not just program a computer to know everything it
needs to know already?
Many programs or computer-controlled robots must be prepared
to deal with things that the creator would not know about, such
as game-playing programs, speech programs, electronic
“learning” pets, and robotic explorers. Here, they would have
access to a range of unpredictable knowledge and thus would
benefit from being able to draw conclusions independently.
Relevance to AI
•
Helps programs handle new situations based on the input and
output from old ones
• Programs designed to adapt to humans will learn how to
better interact
• Could potentially save bulky programming and attempts to
make a program “foolproof”
• Makes nearly all programs more dynamic and more powerful
while improving the efficiency of programming.
Approaches to Machine Learning
• Boolean logic and resolution
• Evolutionary machine learning – many algorithms / neural
networks are generated to solve a problem, the best ones
survive
• Statistical learning
•
Unsupervised learning – algorithm that models outputs from
the input, knows nothing about the expected results
• Supervised learning – algorithm that models outputs from the
input and expected output
• Reinforcement learning – algorithm that models outputs from
observations
Current Machine Learning Research
Almost all types of AI are developing machine learning, since it
makes programs dynamic.
Examples:
• Facial recognition – machines learn through many trials what
objects are and aren’t faces
•
Language processing – machines learn the rules of English
through example; some AI chatterbots start with little
linguistic knowledge but can be taught almost any language
through extensive conversation with humans
Future of Machine Learning
• Gaming – opponents will be able to learn from the player’s
strategies and adapt to combat them
•
Personalized gadgets – devices that adapt to their owner as he
changes (gets older, gets different tastes, changes his modes)
•
Exploration – machines will be able to explore environments
unsuitable for humans and quickly adapt to strange properties
Problems in Machine Learning
•
Learning by Example:
• Noise in example classification
• Correct knowledge representation
•
Heuristic Learning
• Incomplete knowledge base
• Continuous situations in which there is no absolute answer
•
Case-based Reasoning
• Human knowledge to computer representation
•Problems in Machine Learning
• Grammar – meaning pairs
• new rules must be relearned a number of times to
gain “strength”
• Conceptual Clustering
• Definitions can be very complicated
• Not much predictive power
Successes in Research
• ARCH by P.H. Winston in which positive and negative
examples are used to explain the concept
•
D. B. Lenat’s pioneering work in heuristics with incomplete
knowledge base: RLL language and EURISKO system
•
LAS by Anderson (1977) & AMBER by Langley (1982)
simulate aspects of grammar learning
•Successes continued…
• Aspects of daily life using machine learning
• Optical character recognition
• Handwriting recognition
• Speech recognition
• Automated steering
• Assess credit card risk
• Filter news articles
• Refine information retrieval
• Data mining
Bibliography
•
•
•
•
•
http://robotics.stanford.edu/people/nilsson/mlbook.html
http://www.mlnet.org/
http://ai-depot.com/GameAI/Learning.html
http://web.engr.oregonstate.edu/~tgd/experimental-research/
http://encyclopedia.thefreedictionary.com/machine%20learnin
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• Shapiro, Stuart C. and David Eckroth (ed.) “Machine
Learning” Encyclopedia of Artificial
Intelligence. New York: John Wiley & Sons. © 1987.
•Any Questions?