Knowledge in Learning System Architecture LAB Heonkyu Park ’00. 5. 31 Knowedge in Learning
Where are we? • Focus on Machine Learning • Learning from Observation (Ch 18) • Learning in Neural Network (Ch 19) • Learning of Belief Network (Ch 19) • Reinforcement Learning (Ch 20) • Knowledge in Learning (Ch 21) Knowedge in Learning
Contents • Overview • Knowledge in Learning • Explanation-Based Learning • Learning Using Relevance Information • Inductive Logic Programming • Summary Knowedge in Learning
Overview • So far, constructing a program that has the input/output behavior observed in data. • searching a hypothesis space to find a suitable program. • Study learning methods can take advantage of prior knowledge about the environment. Knowedge in Learning
Knowledge in Learning • Entailment constraint : Hypothesis ∧Descriptions = Classifications • Cumulative Learning Knowedge in Learning
Some Simple Examples First meeting with a native speaker Toasting a sausages Observing consulting session Measuring conductance of a material Knowedge in Learning
Some general Schema • Explanation-based Learning(EBL) Hypothesis ∧ Descriptions = Classifications Background = Hypothesis • Relevance-based Learning(RBL) Hypothesis ∧ Descriptions = Classifications Background ∧ Descriptions ∧ Classifications = Hypothesis • Knowledge-based Inductive L.(KBIL) Background ∧Hypothesis ∧Descriptions = Classifications • Inductive Logic Programming(ILP) Knowedge in Learning
Explanation Based Learning • Basic Idea • Construct an explanation of the observation using prior knowledge • Establish a definition of the class of cases • Example rules Rewrite(u,v) ∧Simplify(v,w) Simplify(u,w) Primitive(u) Simplify(u,u) ArithmeticUnknown(u) Primitive(u) Rewrite( 1 x u, u) Rewrite( 0 + u, u) ……… Knowedge in Learning
EBL (cont’d) • Proof trees for the simplification problem Knowedge in Learning
EBL (cont’d) • Basic EBL process works : • Given example, construct a proof that goal predicate applies to example using available background knowledge • In parallel, construct a generalized proof tree for variabilized goal using same inference steps • Construct a new rule, left-hand side consists of the leaves of the proof tree, right-hand side is variabilized goal • Drop any conditions that are true regardless of the values of variables in the goal Knowedge in Learning
Using Relevance Information • Example : traveler in Brazil • x,y,n,l Nationality(x,n)∧ Nationality(y,n)∧ Language(x,l) • Language(y,l) Nationality(Fernando,Brazil)∧ Language(Fernando,Portuguese) • So, x Nationality(x,Brazil) Language(x,Portuguese) • Functional Dependencies(Determinations) • Nationality(x,n)Language(x,l) • Material(x,m) ∧ Temperature(x,t) Conductance(x,) Material(x,m) ∧ Temperature(x,t) Density(x,) Knowedge in Learning
RBL (cont’d) • Algorithm for finding a minimal consistent determination Knowedge in Learning
Inductive Logic Programming • Entailment Constraint Background ∧Hypothesis ∧Descriptions = Classifications • Example in ‘Family Tree’ • Term : Mother(), Father(), Married() • Properties : Male(), Female() • Target : Grandparent, BrotherInLaw, Ancestor… • Description Father(아빠, 나), Father(나, 아들) Mother(엄마, 남동생), Mother(아내, 딸) Married(아빠, 엄마), Married(누나, 자형) Male(아빠), Male(자형), Female(처제), Female(이모) ……………………………… Knowedge in Learning
ILP (Cont’d) • Classification Grandparent(할머니, 나) Grandparent(아빠,아들) Grandparent(이모부, 딸), Grandparent(할머니,처제) • Hypothesis Grandparent(x, y) [z Mother(x,z) ∧ Mother(z,y)] V [z Mother(x,z) ∧ Father(z,y)] V [z Father(x,z) ∧ Mother(z,y)] V [z Father(x,z) ∧ Father(z,y)] Knowedge in Learning
ILP (Cont’d) • Inverse resolution • Generating inverse proofs • Individual backward steps • Resolvent C resolves into C1 and C2 • Discovering new predicates & knowledge • Top-down learning methods • Start with very general rule • Gradually specialize it Knowedge in Learning
Summary of this chapter • The use of prior knowledge leads to a picture of cumulative learning • Entailment constraints helps to define a variety of learning techniques • EBL extracts general rules from single examples by explaining the examples and generalizing the explanation • RBL uses prior knowledge to identify the relevant attributes • KBIL finds inductive hypotheses that explain sets of observations with background knowledge • ILP techniques perform KBIL using knowledge expressed in first-order logic Knowedge in Learning
SUMMARY of this course • AI (Intro, Intelligent Agents) • Problem Solving • Problem solving by searching, CSP(BFS,DFS,IDS,BS) • Informed Search Method(BFS,IDA*,IIA,GA) • Artificial Life, Game playing • Uncertain Knowledge and Reasoning • Uncertainty, Probability, Bayes’ rule • Probabilistic Reasoning System(Belief Network) • Making Simple/Complex Decision(Utility Fn, Decision Network) • Learning • From observation, Neural Network, Belief Network, Reinforcement, Knowledge in Learning Knowedge in Learning
Thanks you. Knowedge in Learning
