Presentation is loading. Please wait.

Presentation is loading. Please wait.

처음 페이지로 이동 Chapter 11: Analytical Learning Inductive learning training examples n Analytical learning prior knowledge + deductive reasoning n Explanation.

Published byAshlee Wiggins Modified over 9 years ago

Similar presentations

Presentation on theme: "처음 페이지로 이동 Chapter 11: Analytical Learning Inductive learning training examples n Analytical learning prior knowledge + deductive reasoning n Explanation."— Presentation transcript:

1 처음 페이지로 이동 Chapter 11: Analytical Learning Inductive learning training examples n Analytical learning prior knowledge + deductive reasoning n Explanation based learning - prior knowledge : analyze, explain how each training example satisfies the target concept - distinguish relevant features = generalization based on logical reasoning - applied to learning search control rules

2 처음 페이지로 이동 Introduction n Inductive learning poor performance when insufficient data n Explanation based learning (1) accept explicit prior knowledge (2) generalize more accurately than inductive system (3) prior knowledge = reduce complexity of hypotheses space reduce sample complexity improve generalization accuracy

3 처음 페이지로 이동 n Task of learning play chess - target concept chessboard positions in which black will lose its queen within two moves - human explain/analyze training examples by prior knowledge - knowledge = legal rules of chess

4 처음 페이지로 이동 n Chapter summary - Learning algorithm that automatically construct/learn from explanation - Analytical learning problem definition - PROLOG-EBG algorithm - General properties, relationship to inductive learning algorithm - Application to improving performance at large state-space search problems

5 처음 페이지로 이동 Inductive Generalization Problem n Given: Instances Hypotheses Target Concept Training examples of target concept n Determine: Hypotheses consistent with the training examples

6 처음 페이지로 이동 Analytical Generalization Problem n Given: Instances Hypotheses Target Concept Training Examples Domain Theory n Determine: Hypotheses consistent with training examples and domain theory

7 처음 페이지로 이동 Example of an analytical learning problem n Instance space : describe a pair of objects Color, Volume, Owner, Material, Density, On n Hypothesis space H SafeToStack(x,y) Volume(x,vx) ^ Volume(y,vy) ^ LessThan(vx,vy) n Target concept : SafeToStack(x,y) “pairs of physical objects, such that one can be stacked safely on the other”

8 처음 페이지로 이동 n Training Examples On(Obj1, Obj2)Owner(Obj1, Fred) Type(Obj1, Box)Owner(Obj2,Louise) Type(Obj2, Endtable)Density(Obj1,0.3) Color(Obj1,Red)Material(Obj1,Cardboard) Color(Obj2,Blue)Material(Obj2,Wood) Volume(Obj1,2) n Domain Theory B SafeToStack(x,y) ~Fragile(y) SafeToStack(x,y) Lighter(x,y) Lighter(x,y) Weight(x,wx) ^ Weight(y,wy) ^ LessThan(wx,wy) Weight(x,w) Volume(x,v) ^ Density(x,d) ^ Equal(w,times(v,d)) Weight(x,5) Type(x,Endtable) Fragile(x) Material(x,Glass).......

9 처음 페이지로 이동 n Domain Theory B - explain why certain pairs of objects can be safely stacked on one another - described by a collection of Horn clause : enable system to incorporate any learned hypotheses into subsequent domain theories

10 처음 페이지로 이동 Learning with Perfect Domain Theories : PROLOG-EBG n Correct = assertions are truthful statements n Complete = covers every positive examples n Perfect domain theory is available? Yes n Why does it need to learn when perfect domain theory is given?

11 처음 페이지로 이동 PROLOG-EBG n Operation (1) Leaning a single Horn clause rule (2) Removing positive examples covered (3) Iterating this process n Given a complete/correct domain theory --> output a hypothesis (correct, cover observed positive training examples)

12 처음 페이지로 이동 PROLOG-EBG Algorithm PROLOG-EBG(Target Concept,Training Examples,Domain Theory) Learned Rules {} Pos the positive examples from Training Examples for each Positive Examples in Pos that is not covered by Learned Rules, do 1. Explain: - Explanation explanation in terms of Domain Theory that Positive Examples satisfies the Target Concept 2. Analyze: - Sufficient Conditions the most general set of features of Positive Examples sufficient to satisfy the Target Concept according to the Explanation 3. Refine: - Learned Rules Learned Rules + NewHornClause Target Concept Sufficient Conditions Return Learned Rules

13 처음 페이지로 이동

14 처음 페이지로 이동 n Weakest Preimage The weakest preimage of a conclusion C with respect to a proof P is the most general set of initial assertions A, such that A entails C according to P the most general rules SafeToStack(x,y) Volume(x,vx) ^ Density(x,dx) ^ Equal(wx,times(vx,dx)) ^ LessThan(wx,5) ^ Type(y,Endtable)

15 처음 페이지로 이동

16 처음 페이지로 이동

17 처음 페이지로 이동 Remarks on Explanation-Based Learning n Properties (1) justified general hypotheses by using prior knowledge (2) explanation determines relevant attributes (3) regressing the target concept allows deriving more general constraints (4) learned Horn clause = sufficient condition to satisfy target concept (5) implicitly assume the complete/correct domain theory (6) generality of the Horn clause depends on the formulation of the domain theory

18 처음 페이지로 이동 n Perspectives on example based learning (1) EBL as theory-guided generalization (2) EBL as example-guided reformation of theories (3) EBL as “just” restating what the learner already “knows” n Knowledge compilation - reformulate the domain theory to produce general rules that classify examples in a single inference step - transformation = efficiency improving task without altering correctness of system’s knowledge

19 처음 페이지로 이동 Characteristics n Discovering New Features - learned feature : feature by hidden units of neural networks n Deductive Learning - background knowledge of ILP : enlarge the set of hypotheses - domain theory : reduce the set of acceptable hypotheses n Inductive Bias - inductive bias of PROLOG-EBG = domain theory B - Approximate inductive bias of PROLOG-EBG = domain theory B + preference for small sets of maximally general Horn clauses

20 처음 페이지로 이동 n LEMMA-ENUMERATOR algorithm - enumerate all proof trees - for each proof tree, calculate the weakest preimage and construct a Horn clause - ignore the training data - output a superset of Horn clauses output by PROLOG-EBG n Role of training data focus algorithm on generating rules that cover the distribution of instances that occur in practice n Observed positive example allow generalizing deductively to other unseen instances IF ((PlayTennis = YES) (Humidity=x)) THEN ((PlayTennis = YES) (Humidity <= x)

21 처음 페이지로 이동 n Knowledge-level learning - the learned hypothesis entails predictions that go beyond those entailed by the domain theory - deductive closure : set of all predictions entailed by a set of assertions n Determinations - some attribute of the instance is fully determined by certain other attributes, without specifying the exact nature of the dependency - example target concept : “people who speak Portuguese” domain theory : “ the language spoken by a person is determined by their nationality” training example : “Joe, 23-year-old Brazilian, speaks Portuguese” conclusion : “all Brazilians speak Portuguese”

22 처음 페이지로 이동 Explanation-based Learning of Search Control Knowledge n Speed up complex search programs n Complete/Correct domain theory for learning search control knowledge = definition of legal search operator + definition of the search objective n Problem find a sequence of operators that will transform an arbitrary initial state S to some final state F that satisfies the goal predicate G

23 처음 페이지로 이동 PRODIGY n Domain-independent planning system n find a sequence of operators that leads from S to O n means-ends planner decompose problems into subgoals solve them combine their solution into a solution for the full problem

24 처음 페이지로 이동 SORA System n Support a broad variety of problem-solving strategies n Learned by explaining situations in which its current strategy leads to inefficiencies

25 처음 페이지로 이동 Practical Problems applying EBL to learning search control n The number of control rules that must be learned is very large (1) efficient algorithms for matching rules (2) utility analysis : estimating the computational cost and benefit of each rule (3) identify types of rules that will be costly to match re-expressing such rules in more efficient forms optimizing rule-matching algorithm

26 처음 페이지로 이동 n Constructing the explanations for the desired target concept is intractable (1) example - states for which operator A leads toward the optimal solution (2) “lazy” or “incremental” explanation - heuristics are used to produce partial/approximate and tractable explanation - learned rules may be imperfect - monitoring performance of the rule on subsequent cases - when error, original explanation is elaborated to cover new case, - more refined rules is extracted

Download ppt "처음 페이지로 이동 Chapter 11: Analytical Learning Inductive learning training examples n Analytical learning prior knowledge + deductive reasoning n Explanation."

Similar presentations

Explanation-Based Learning (borrowed from mooney et al)

Explanation-Based Learning (borrowed from mooney et al)
Analytical Learning.

Analytical Learning.
Concept Learning and the General-to-Specific Ordering

Concept Learning and the General-to-Specific Ordering
Combining Inductive and Analytical Learning

Combining Inductive and Analytical Learning
Naïve Bayes. Bayesian Reasoning Bayesian reasoning provides a probabilistic approach to inference. It is based on the assumption that the quantities of.

Naïve Bayes. Bayesian Reasoning Bayesian reasoning provides a probabilistic approach to inference. It is based on the assumption that the quantities of.
 2002, G.Tecuci, Learning Agents Laboratory 1 Learning Agents Laboratory Computer Science Department George Mason University Prof. Gheorghe Tecuci Deductive.

 2002, G.Tecuci, Learning Agents Laboratory 1 Learning Agents Laboratory Computer Science Department George Mason University Prof. Gheorghe Tecuci Deductive.
Knowledge Representation and Reasoning Learning Sets of Rules and Analytical Learning Harris Georgiou – 4.

Knowledge Representation and Reasoning Learning Sets of Rules and Analytical Learning Harris Georgiou – 4.
Combining Inductive and Analytical Learning Ch 12. in Machine Learning Tom M. Mitchell 고려대학교 자연어처리 연구실 한 경 수 1999. 7. 9.

Combining Inductive and Analytical Learning Ch 12. in Machine Learning Tom M. Mitchell 고려대학교 자연어처리 연구실 한 경 수
Knowledge in Learning Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 19 Spring 2004.

Knowledge in Learning Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 19 Spring 2004.
Learning control knowledge and case-based planning Jim Blythe, with additional slides from presentations by Manuela Veloso.

Learning control knowledge and case-based planning Jim Blythe, with additional slides from presentations by Manuela Veloso.
Università di Milano-Bicocca Laurea Magistrale in Informatica

Università di Milano-Bicocca Laurea Magistrale in Informatica
Knowledge in Learning Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 19 Spring 2005.

Knowledge in Learning Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 19 Spring 2005.
LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.

LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.
Relational Data Mining in Finance Haonan Zhang CFWin03-37 03/04/2003.

Relational Data Mining in Finance Haonan Zhang CFWin /04/2003.
Feature Selection for Regression Problems

Feature Selection for Regression Problems
Machine Learning CSE 473. © Daniel S. Weld 2 473 Topics Agency Problem Spaces Search Knowledge Representation Reinforcement Learning InferencePlanning.

Machine Learning CSE 473. © Daniel S. Weld Topics Agency Problem Spaces Search Knowledge Representation Reinforcement Learning InferencePlanning.
Chapter 1 Conducting & Reading Research Baumgartner et al Chapter 1 Nature and Purpose of Research.

Chapter 1 Conducting & Reading Research Baumgartner et al Chapter 1 Nature and Purpose of Research.
LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.

LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.
1 MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING By Kaan Tariman M.S. in Computer Science CSCI 8810 Course Project.

1 MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING By Kaan Tariman M.S. in Computer Science CSCI 8810 Course Project.
MACHINE LEARNING. What is learning? A computer program learns if it improves its performance at some task through experience (T. Mitchell, 1997) A computer.

MACHINE LEARNING. What is learning? A computer program learns if it improves its performance at some task through experience (T. Mitchell, 1997) A computer.

Similar presentations

Ads by Google