O-decision-tree

O-decision-tree - Inductive Learning (1/2) Decision Tree...

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1 1 Inductive Learning (1/2) Decision Tree Method (If it’s not simple, it’s not worth learning it) R&N: Chap. 18, Sect. 18.1–3 2 Motivation ± An AI agent operating in a complex world requires an awful lot of knowledge: state representations, state axioms, constraints, action descriptions, heuristics, probabilities, . .. ± More and more, AI agents are designed to acquire knowledge through learning 3 What is Learning? ± Mostly generalization from experience: “Our experience of the world is specific, yet we are able to formulate general theories that account for the past and predict the future” M.R. Genesereth and N.J. Nilsson, in Logical Foundations of AI , 1987 ± Æ Concepts, heuristics, policies ± Supervised vs. un-supervised learning 4 Contents ± Introduction to inductive learning ± Logic-based inductive learning: Decision-tree induction ± Function-based inductive learning Neural nets Logic-Based Inductive Learning ± Background knowledge KB ± Training set D ( observed knowledge) that is not logically implied by KB ± Inductive inference : Find h such that KB and h imply D h = D is a trivial, but un-interesting solution (data caching) 6 Rewarded Card Example ± Deck of cards, with each card designated by [r,s], its rank and suit, and some cards “rewarded” ± Background knowledge KB: ((r=1) v … v (r=10)) NUM(r) ((r=J) v (r=Q) v (r=K)) FACE(r) ((s=S) v (s=C)) BLACK(s) ((s=D) v (s=H)) RED(s) ± Training set D: REWARD([4,C]) REWARD([7,C]) REWARD([2,S]) ¬ REWARD([5,H]) ∧¬ REWARD([J,S])
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2 7 Rewarded Card Example ± Deck of cards, with each card designated by [r,s], its rank and suit, and some cards “rewarded” ± Background knowledge KB: ((r=1) v … v (r=10)) NUM(r) ((r=J) v (r=Q) v (r=K)) FACE(r) ((s=S) v (s=C)) BLACK(s) ((s=D) v (s=H)) RED(s) ± Training set D: REWARD([4,C]) REWARD([7,C]) REWARD([2,S]) ¬ REWARD([5,H]) ∧¬ REWARD([J,S]) ± Possible inductive hypothesis: h (NUM(r) BLACK(s) REWARD([r,s])) There are several possible inductive hypotheses 8 Learning a Predicate (Concept Classifier) ± Set E of objects (e.g., cards) ± Goal predicate CONCEPT(x), where x is an object in E, that takes the value True or False (e.g., REWARD) Example: CONCEPT describes the precondition of an action, e.g., Unstack(C,A) E is the set of states CONCEPT(x) HANDEMPTY x, BLOCK(C) x, BLOCK(A) x, CLEAR(C) x, ON(C,A) x Learning CONCEPT is a step toward learning an action description 9 Learning a Predicate (Concept Classifier) ± Set E of objects (e.g., cards) ± Goal predicate CONCEPT(x), where x is an object in E, that takes the value True or False (e.g., REWARD) ± Observable predicates A(x), B(X), … (e.g., NUM, RED) ± Training set : values of CONCEPT for some combinations of values of the observable predicates 10 Example of Training Set Example of Training Set Note that the training set does not say whether an observable predicate is pertinent or not Ternary attributes Goal predicate is PLAY-TENNIS 12 Learning a Predicate (Concept Classifier) ± Set E of objects (e.g., cards) ± Goal predicate CONCEPT(x), where x is an object in E, that takes the value True or False (e.g., REWARD)
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O-decision-tree - Inductive Learning (1/2) Decision Tree...

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