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3_learning.ppt
- 7. Components of a Learning Agent Environment Agent Reasoning & Decisions Making Sensors Effectors Model of World (being updated) List of Possible Actions Prior Knowledge about the World Goals/Utility
- 8. Components of a Learning Agent Environment Sensors Effectors Agent Reasn & Decisn World Model Actions Prior Knowldg Goals /Utility
- 9. Components of a Learning Agent Environment Sensors Effectors Performance Element
- 10. Components of a Learning Agent Environment Performance Element Learning Element Sensors Effectors PE provides knowledge to LE LE changes PE based on how it is doing
- 11. Components of a Learning Agent Environment Performance Element Learning Element Sensors Effectors Critic C provides feedback to LE on how PE is doing C compares PE with a standard of performance that’s told (via sensors)
- 12. Components of a Learning Agent Environment Performance Element Learning Element Sensors Effectors Critic Problem Generator PG suggests problems or actions to PE that will generate new examples or experiences that will aid in achieving the goals from the LE Learning Agent
- 13. Components of a Learning Agent Environment Performance Element Critic Learning Element Problem Generator Sensors Effectors Learning Agent
- 28. Inductive Concept Learning Approach: Learning Decision Trees Suit Rank - clubs hearts spades - + Size - + large small 9 jack 10 Size + - large small interior node = feature leaf node = c l a s s i f i c a t i o n arc = value - diamonds … …
Editor's Notes
- Professor of EE and CS, MIT Media Lab, MIT AI Lab, Cambridge, MA Marvin Minsky has made many contributions to AI, cognitive psychology, mathematics, computational linguistics, robotics, and optics. In recent years he has worked chiefly on imparting to machines the human capacity for commonsense reasoning. His conception of human intellectual structure and function is presented in The Society of Mind .
- Professor of Computational Sciences, Director of the Machine Learning and Inference Laboratory , George Mason University, Fairfax, VA Dr. Michalski is a cofounder of the field of machine learning, and the originator of several research subareas, such as conceptual clustering, constructive induction, variable-valued logic, natural induction, variable-precision logic (with Patrick Winston, MIT), computational theory of human plausible reasoning (with Alan Collins), two-tiered representation of imprecise concepts, multistrategy task-adaptive learning, inferential theory of learning, learnable evolution model, and, most recently, inductive databases and knowledge scouts.
- 1916-2001 , Professor of Psychology, CMU, Pittsburgh, PA Herbert Simon's main interests in computer science were in artificial intelligence, human-computer interaction, principles of the organization of humans and machines is information processing systems, the use of computers to study (by modeling) philosophical problems of the nature of intelligence and of epistemology, and the social implications of computer technology.
- RL: One-to-one mapping from inputs to stored representation. "Learning by memorization." Association-based storage and retrieval. I: Use specific examples to reach general conclusions C: A: Determine correspondence between two different representations D: Unsupervised, specific goal not given GA: R: Only feedback (positive or negative reward) given at end of a sequence of steps. Requires assigning reward to steps by solving the credit assignment problem--which steps should receive credit or blame for a final result?
- Efficiency: use to improve methods for teaching and tutoring people, as done in CAI -- Computer-aided instruction Discover: Data mining Fill in: Large, complex AI systems cannot be completely derived by hand and require dynamic updating to incorporate new information. Learning new characteristics expands the domain or expertise and lessens the "brittleness" of the system.
- e.g. feedback of success or failure
- We'll concentrate on the LE.
- card example: train-large non-face cards, test-small non-face + or -? Can prove truth? Deductive inference is truth preserving. It is a logical consequences of the information and will have the same truth-status of the original information.
- Ockham's Razor is the principle proposed by William of Ockham (England) in the fourteenth century: ``Pluralitas non est ponenda sine neccesitate'', which translates as ``entities should not be multiplied unnecessarily''. A related rule, which can be used to slice open conspiracy theories, is Hanlon's Razor: ``Never attribute to malice that which can be adequately explained by stupidity''.
- do simple concept "all big queens"
- lim x-> 0, x log 2 x, is 0 since x goes to 0 faster than log 2 x goes to –infinity l'Hopital's Rule for indeterminate forms, Guillaume Francois Antoine de l'Hopital (1661 – 1704)
- Rare base case: if (empty(atts)) return majorityClass(exs); Why? Means same set of feature values produced different classifications. Probably implies noise in data or more features are needed.
- When the number of examples available is small (less than about 100) use Leave-1-Out where N-fold cross-validation uses N = number of exs.