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Advanced Artificial Intelligence Lecture 3: LearningPowerPoint Presentation

Advanced Artificial Intelligence Lecture 3: Learning

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Advanced Artificial Intelligence Lecture 3: Learning. Bob McKay School of Computer Science and Engineering College of Engineering Seoul National University. Outline. Defining Learning Kinds of Learning Generalisation and Specialisation Some Simple Learning Algorithms. References.

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### Advanced Artificial IntelligenceLecture 3: Learning

Bob McKay

School of Computer Science and Engineering

College of Engineering

Seoul National University

Outline

- Defining Learning
- Kinds of Learning
- Generalisation and Specialisation
- Some Simple Learning Algorithms

References

- Mitchell, Tom M: Machine Learning, McGraw-Hill, 1997, ISBN 0 07 115467 1

Defining a Learning System (Mitchell)

- “A program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”

Specifying a Learning System

- Specifying the task T, the performance P and the experience E defines the learning problem. Specifying the learning system requires us to define:
- Exactly what knowledge is to be learnt
- How this knowledge is to be represented
- How this knowledge is to be learnt

Specifying What is to be Learnt

- Usually, the desired knowledge can be represented as a target valuation function V: I → D
- It takes in information about the problem and gives back a desired decision

- Often, it is unrealistic to expect to learn the ideal function V
- All that is required is a ‘good enough’ approximation, V’: I → D

Specifying How Knowledge is to be Represented

- The function V’ must be represented symbolically, in some language L
- The language may be a well-known language
- Boolean expressions
- Arithmetic functions
- ….

- Or for some systems, the language may be defined by a grammar

- The language may be a well-known language

Specifying How the Knowledge is to be Learnt

- If the learning system is to be implemented, we must specify an algorithm A, which defines the way in which the system is to search the language L for an acceptable V’
- That is, we must specify a search algorithm

Structure of a Learning System

- Four modules
- The Performance System
- The Critic
- The Generaliser (or sometimes Specialiser)
- The Experiment Generator

Performance Module

- This is the system which actually uses the function V’ as we learn it
- Learning Task
- Learning to play checkers

- Performance module
- System for playing checkers
- (I.e. makes the checkers moves)

- System for playing checkers

- Learning Task

Critic Module

- The critic module evaluates the performance of the current V’
- It produces a set of data from which the system can learn further

Generaliser/Specialiser Module

- Takes a set of data and produces a new V’ for the system to run again

Experiment Generator

- Takes the new V’
- Maybe also uses the previous history of the system

- Produces a new experiment for the performance system to undertake

The Importance of Bias

- Important theoretical results from learning theory (PAC learning) tell us that learning without some presuppositions is infeasible.
- Practical experience, of both machine and human learning, confirms this.
- To learn effectively, we must limit the class of V’s.

- Practical experience, of both machine and human learning, confirms this.
- Two approaches are used in machine learning:
- Language bias
- Search Bias
- Combined Bias
- Language and search bias are not mutually exclusive: most learning systems feature both

Language Bias

- The language L is restricted so that it cannot represent all possible target functions V
- This is usually on the basis of some knowledge we have about the likely form of V’
- It introduces risk
- Our system will fail if L does not contain an acceptable V’

Search Bias

- The order in which the system searches L is controlled, so that promising areas for V’ are searched first

The Downside:No Free Lunches

- Wolpert and MacReady’s No Free Lunch Theorem states, in effect, that averaged over all problems, all biases are equally good (or bad).
- Conventional view
- The choice of a learning system cannot be universal
- It must be matched to the problem being solved

- The choice of a learning system cannot be universal
- In most systems, the bias is not explicit
- The ability to identify the language and search biases of a particular system is an important aspect of machine learning

- Some more recent systems permit the explicit and flexible specification of both language and search biases

No Free Lunch:Does it Matter?

- Alternative view
- We aren’t interested in all problems
- We are only interested in prolems which have solutions of less than some bounded complexity
- (so that we can understand the solutions)

- We are only interested in prolems which have solutions of less than some bounded complexity
- The No Free Lunch Theorem may not apply in this case

- We aren’t interested in all problems

Some Dimensions of Learning

- Induction vs Discovery:
- Guided learning vs learning from raw data
- Learning How vs Learning That (vs Learning a Better That)
- Stochastic vs Deterministic; Symbolic vs Subsymbolic
- Clean vs Noisy Data
- Discrete vs continuous variables
- Attribute vs Relational Learning
- The Importance of Background Knowledge

Induction vs Discovery

- Has the target concept been previously identified?
- Pearson: cloud classifications from satellite data

- vs
- Autoclass and H - R diagrams
- AM and prime numbers
- BACON and Boyle's Law

Guided Learning vs Learning from Raw Data

- Does the learning system require carefully selected examples and counterexamples, as in a teacher – student situation?
- (allows fast learning)
- CIGOL learning sort/merge

- vs
- Garvan institute's thyroid data

Learning How vs Learning That vs Learning a Better That vs vs

- Classifying handwritten symbols
- Distinguishing vowel sounds (Sejnowski & Rosenberg)
- Learning to fly a (simulated!) plane

- Michalski & learning diagnosis of soy diseases

- Mitchell & learning about chess forks

Stochastic vs Deterministic;Symbolic vs Subsymbolic vs vs vs

- Classifying handwritten symbols (stochastic, subsymbolic)

- Predicting plant distributions (stochastic, symbolic)

- Cloud classification (deterministic, symbolic)

- ? (deterministic, subsymbolic)

Attibute vs Relational Learning vs

- Predicting plant distributions

- Predicting animal distributions
- (because plants can’t move, they don’t care - much - about spatial relationships)

The importance of Background Knowledge

- Learning about faults in a satellite power supply
- general electric circuit theory
- knowledge about the particular circuit

Generalisation and Learning

- What do we mean when we say of two propositions, S and G, that G is a generalisation of S?
- Suppose skippy is a grey kangaroo.
- We would regard ‘Kangaroos are grey as a generalisation of ‘Skippy is grey’.
- In any world in which ‘kangaroos are grey’ is true, ‘Skippy is grey’ will also be true.

- In other words, if G is a generalisation of specialisation S, then G is 'at least as true' as S,
- That is, S is true in all states of the world in which G is, and perhaps in other states as well.

Generalisation and Inference

- In logic, we assume that if S is true in all worlds in which G is, then
- G → S

- That is, G is a generalisation of S exactly when G implies S
- So we can think of learning from S as a search for a suitable G for which G → S

- In propositional learning, this is often used as a definition:
- G is more general than S if and only if G → S

Issues

- Equating generalisation and logical implication is only useful if the validity of an implication can be readily computed
- In the propositional calculus, validity is an exponential problem
- in the predicate calculus, validity is an undecidable problem

- so the definition is not universally useful
- (although for some parts of logic - eg learning rules - it is perfectly adequate).

A Common Misunderstanding

- Suppose we have two rules,
- 1) A ∧ Β → G
- 2) A ∧ Β ∧ C → G

- Clearly, we would want 1 to be a generalisation of 2
- This is OK with our definition, because
- ((A ^ B → G) → (A ^ B ^ C → G))

- is valid
- But the confusing thing is that ((A^B^C) → (A∧Β)) is valid
- Iif you only look at the hypotheses of the rule, rather than the whole rule, the implication is the wrong way around
- Note that some textbooks are themselves confused about this

- But the confusing thing is that ((A^B^C) → (A∧Β)) is valid

Defining Generalisaion

- We could try to define the properties that generalisation must satisfy,
- So let's write down some axioms. We need some notation.
- We will write 'S <G G' as shorthand for 'S is less general than G'.

- Axioms:
- Transitivity: If A <G B and B <G C then also A <G C
- Antisymmetry: If A <G B then it's not true that B <G A
- Top: there is a unique element, ⊥, for which it is always true that A <G⊥.
- Bottom: there is a unique element, T, for which it is always true that T <GA.

Picturing Generalisaion

- We can draw a 'picture' of a generalisation hierarchy satisfying these axioms:

Specifying Generalisaion

- In a particular domain, the generalisation hierarchy may be defined in either of two ways:
- By giving a general definition of what generalisation means in that domain
- Example: our earlier definition in terms of implication

- By directly specifying the specialisation and generalisation operators that may be used to climb up and down the links in the generalisation hierarchy

- By giving a general definition of what generalisation means in that domain

Learning and Generalisaion

- How does learning relate to generalisation?
- We can view most learning as an attempt to find an appropriate generalisation that generalises the examples.
- In noise free domains, we usually want the generalisation to cover all the examples.
- Once we introduce noise, we want the generalisation to cover 'enough' examples, and the interesting bit is in defining what 'enough' is.

- In our picture of a generalisation hierarchy, most learning algorithms can be viewed as methods for searching the hierarchy.
- The examples can be pictured as locations low down in the hierarchy, and the learning algorithm attempts to find a location that is above all (or 'enough') of them in the hierarchy, but usually, no higher 'than it needs to be'

Searching the Generalisaion Hierarchy

- The commonest approaches are:
- generalising search
- the search is upward from the original examples, towards the more general hypotheses

- specialising search
- the search is downward from the most general hypothesis, towards the more special examples

- Some algorithms use different approaches. Mitchell's version space approach, for example, tries to 'home in' on the right generalisation from both directions at once.

- generalising search

Completeness and Generalisaion

- Many approaches to axiomatising generalisation add an extra axiom:
- Completeness: For any set Σ of members of the generalisation hierarchy, there is a unique 'least general generalisation' L, which satisfies two properties:
- 1) for every S in Σ, S <GL
- 2) if any other L' satisfies 1), then L <GL'

- If this definition is hard to understand, compare it with the definition of 'Least Upper Bound' in set theory, or of 'Least Common Multiple' in arithmetic

- Completeness: For any set Σ of members of the generalisation hierarchy, there is a unique 'least general generalisation' L, which satisfies two properties:

Restricting Generalisation

- Let's go back to our original definition of generalisation:
- G generalises S iff G → S

- In the general predicate calculus case, this relation is uncomputable, so it's not very useful
- One approach to avoiding the problem is to limit the implications allowed

Generalisation and Substitution

- Very commonly, the generalisations we want to make involve turning a constant into a variable.
- So we see a particular black crow, fred, so we notice:
- crow(fred) → black(fred)

- and we may wish to generalise this to
- ∀X(crow(X) → black(X))

- So we see a particular black crow, fred, so we notice:
- Notice that the original proposition can be recovered from the generalisation by substituting 'fred' for the variable 'X'
- The original is a substitution instance of the generalisation
- So we could define a new, restricted generalisation:
- G subsumes S if S is a substitution instance of G

- An example of our earlier definition, because a substitution instance is always implied by the original proposition.

Learning Algorithms

- For the rest of this lecture, we will work with a specific learning dataset (due to Mitchell):
- Item Sky AirT Hum Wnd Wtr Fcst Enjy
- 1 Sun Wrm Nml Str Wrm Sam Yes
- 2 Sun Wrm High Str Wrm Sam Yes
- 3 Rain Cold High Str Wrm Chng No
- 4 Sun Wrm High Str Cool Chng Yes

- First, we look at a really simple algorithm, Maximally Specific Learning

Maximally Specific Learning

- The learning language consists of sets of tuples, representing the values of these attributes
- A ‘?’ represents that any value is acceptable for this attribute
- A particular value represents that only that value is acceptable for this attribute
- A ‘φ’ represents that no value is acceptable for this attribute
- Thus (?, Cold, High, ?, ?, ?) represents the hypothesis that water sport is enjoyed only on cold, moist days.

- Note that our language is already heavily biased: only conjunctive hypotheses (hypotheses built with ‘^’) are allowed.

Find-S

- Find-S is a simple algorithm: its initial hypothesis is that water sport is never enjoyed
- It expands the hypothesis as positive data items are noted

Running Find-S

- Initial Hypothesis
- The most specific hypothesis (water sports are never enjoyed):
- h ← (φ,φ,φ,φ,φ,φ)

- After First Data Item
- Water sport is enjoyed only under the conditions of the first item:
- h ← (Sun,Wrm,Nml,Str,Wrm,Sam)

- After Second Data Item
- Water sport is enjoyed only under the common conditions of the first two items:
- h ← (Sun,Wrm,?,Str,Wrm,Sam)

Running Find-S

- After Third Data Item
- Since this item is negative, it has no effect on the learning hypothesis:
- h ← (Sun,Wrm,?,Str,Wrm,Sam)

- After Final Data Item
- Further generalises the conditions encountered:
- h ← (Sun,Wrm,?,Str,?,?)

Discussion

- We have found the most specific hypothesis corresponding to the dataset and the restricted (conjunctive) language
- It is not clear it is the best hypothesis
- If the best hypothesis is not conjunctive (eg if we enjoy swimming if it’s warm or sunny), it will not be found
- Find-S will not handle noise and inconsistencies well.
- In other languages (not using pure conjunction) there may be more than one maximally specific hypothesis; Find-S will not work well here

Version Spaces

- One possible improvement on Find-S is to search many possible solutions in parallel
- Consistency
- A hypothesis h is consistent with a dataset D of training examples iff h gives the same answer on every element of the dataset as the dataset does

- Version Space
- The version space with respect to the language L and the dataset D is the set of hypotheses h in the language L which are consistent with D

List-then-Eliminate

- Obvious algorithm
- The list-then-eliminate algorithm aims to find the version space in L for the given dataset D
- It can thus return all hypotheses which could explain D

- It works by beginning with L as its set of hypotheses H
- As each item d of the dataset D is examined in turn, any hypotheses in H which are inconsistent with d are eliminated

- The language L is usually large, and often infinite, so this algorithm is computationally infeasible as it stands

Version Space Representation

- One of the problems with the previous algorithm is the representation of the search space
- We need to represent version spaces efficiently

- General Boundary
- The general boundary G with respect to language L and dataset D is the set of hypotheses h in L which are consistent with D, and for which there is no more general hypothesis in L which is consistent with D

- Specific Boundary
- The specific boundary S with respect to language L and dataset D is the set of hypotheses h in L which are consistent with D, and for which there is no more specific hypothesis in L which is consistent with D

Version Space Representation 2

- A version space may be represented by its general and specific boundary
- That is, given the general and specific boundaries, the whole version space may be recovered
- The Candidate Elimination Algorithm traces the general and specific boundaries of the version space as more examples and counter-examples of the concept are seen
- Positive examples are used to generalise the specific boundary
- Negative examples permit the general boundary to be specialised.

Candidate Elimination Algorithm

Set G to the set of most general hypotheses in L

Set S to the set of most specific hypotheses in L

For each example d in D:

Candidate Elimination Algorithm

If d is a positive example

Remove from G any hypothesis inconsistent with d

For each hypothesis s in S that is not consistent with d

Remove s from S

Add to S all minimal generalisations h of s such that h is consistent with d, and some member of G is more general than h

Remove from S any hypothesis that is more general than another hypothesis in S

Candidate Elimination Algorithm

If d is a negative example

Remove from S any hypothesis inconsistent with d

For each hypothesis g in G that is not consistent with d

Remove g from G

Add to G all minimal specialisations h of g such that h is consistent with d, and some member of S is more specific than h

Remove from G any hypothesis that is less general than another hypothesis in G

Summary

- Defining Learning
- Kinds of Learning
- Generalisation and Specialisation
- Some Simple Learning Algorithms
- Find-S
- Version Spaces
- List-then-Eliminate
- Candidate Elimination

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