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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 g • Shapiro, Stuart C. and David Eckroth (ed.) “Machine Learning” Encyclopedia of Artificial Intelligence. New York: John Wiley & Sons. © 1987. •Any Questions?