Machine Learning

Wilma Bainbridge

Tencia Lee

Kendra Leigh

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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.

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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

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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.

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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.

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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

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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

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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

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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

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•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

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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

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•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

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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%20learning • Shapiro, Stuart C. and David Eckroth (ed.) “Machine

Learning” Encyclopedia of Artificial Intelligence. New York: John Wiley & Sons. © 1987.

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•Any Questions?