Introduction to Artificial Intelligence. Learning from Oberservations
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1 Introduction to Artificial Intelligence Learning from Oberservations Bernhard Beckert UNIVERSITÄT KOBLENZ-LANDAU Winter Term 2004/2005 B. Beckert: KI für IM p.1
2 Outline Learning agents Inductive learning Decision tree learning B. Beckert: KI für IM p.2
3 Learning Reasons for learning Learning is essential for unknown environments, when designer lacks omniscience B. Beckert: KI für IM p.3
4 Learning Reasons for learning Learning is essential for unknown environments, when designer lacks omniscience Learning is useful as a system construction method, expose the agent to reality rather than trying to write it down B. Beckert: KI für IM p.3
5 Learning Reasons for learning Learning is essential for unknown environments, when designer lacks omniscience Learning is useful as a system construction method, expose the agent to reality rather than trying to write it down Learning modifies the agent s decision mechanisms to improve performance B. Beckert: KI für IM p.3
6 Learning Agents B. Beckert: KI für IM p.4
7 Learning Element Design of learning element is dictated by what type of performance element is used which functional component is to be learned how that functional component is represented what kind of feedback is available B. Beckert: KI für IM p.5
8 Types of Learning Supervised learning Correct answers for each example instance known Requires teacher B. Beckert: KI für IM p.6
9 Types of Learning Supervised learning Correct answers for each example instance known Requires teacher Reinforcement learning Occasional rewards Learning is harder Requires no teacher B. Beckert: KI für IM p.6
10 Inductive Learning (a.k.a. Science) Simplest form Learn a function f from examples (tabula rasa), i.e., find an hypothesis h such that h f given a training set of examples f is the target function An example is a pair x, f (x) B. Beckert: KI für IM p.7
11 Inductive Learning (a.k.a. Science) Simplest form Learn a function f from examples (tabula rasa), i.e., find an hypothesis h such that h f given a training set of examples f is the target function An example is a pair x, f (x) Example (for an example) O O X X, +1 X B. Beckert: KI für IM p.7
12 Inductive Learning Method This is a highly simplified model of real learning Ignores prior knowledge Assumes a deterministic, observable environment Assumes examples are given Assumes that the agent wants to learn f (why?) B. Beckert: KI für IM p.8
13 Inductive Learning Method Idea Construct/adjust h to agree with f on training set h is consistent if it agrees with f on all examples Example: Curve fitting B. Beckert: KI für IM p.9
14 Inductive Learning Method Idea Construct/adjust h to agree with f on training set h is consistent if it agrees with f on all examples Example: Curve fitting B. Beckert: KI für IM p.9
15 Inductive Learning Method Idea Construct/adjust h to agree with f on training set h is consistent if it agrees with f on all examples Example: Curve fitting B. Beckert: KI für IM p.9
16 Inductive Learning Method Idea Construct/adjust h to agree with f on training set h is consistent if it agrees with f on all examples Example: Curve fitting B. Beckert: KI für IM p.9
17 Inductive Learning Method Idea Construct/adjust h to agree with f on training set h is consistent if it agrees with f on all examples Example: Curve fitting B. Beckert: KI für IM p.9
18 Inductive Learning Method Idea Construct/adjust h to agree with f on training set h is consistent if it agrees with f on all examples Example: Curve fitting B. Beckert: KI für IM p.9
19 Inductive Learning Method Idea Construct/adjust h to agree with f on training set h is consistent if it agrees with f on all examples Example: Curve fitting Ockham s razor Maximize a combination of consistency and simplicity B. Beckert: KI für IM p.9
20 Attribute-based Representations Example description consists of Attribute values (boolean, discrete, continuous, etc.) Target value B. Beckert: KI für IM p.10
21 Attribute-based Representations Example Situations where I will/won t wait for a table in a restaurant Exmpl. Attributes Target Alt Bar Fri Hun Pat Price Rain Res Type Est WillWait X 1 T F F T Some $$$ F T French 0 10 T X 2 T F F T Full $ F F Thai F X 3 F T F F Some $ F F Burger 0 10 T X 4 T F T T Full $ F F Thai T X 5 T F T F Full $$$ F T French >60 F X 6 F T F T Some $$ T T Italian 0 10 T X 7 F T F F None $ T F Burger 0 10 F X 8 F F F T Some $$ T T Thai 0 10 T X 9 F T T F Full $ T F Burger >60 F X 10 T T T T Full $$$ F T Italian F X 11 F F F F None $ F F Thai 0 10 F X 12 T T T T Full $ F F Burger T B. Beckert: KI für IM p.11
22 Decision Trees A possible representation for hypotheses Example The correct tree for deciding whether to wait B. Beckert: KI für IM p.12
23 Decision Trees Properties Decision trees can approximate any function of the input attributes ( correct decision tree may be infinite) B. Beckert: KI für IM p.13
24 Decision Trees Properties Decision trees can approximate any function of the input attributes ( correct decision tree may be infinite) Trivially, there is a consistent decision tree for any training set with one path to leaf for each example (unless f nondeterministic) B. Beckert: KI für IM p.13
25 Decision Trees Properties Decision trees can approximate any function of the input attributes ( correct decision tree may be infinite) Trivially, there is a consistent decision tree for any training set with one path to leaf for each example (unless f nondeterministic) Decision tree for training examples probably won t generalize to new examples B. Beckert: KI für IM p.13
26 Decision Trees Properties Decision trees can approximate any function of the input attributes ( correct decision tree may be infinite) Trivially, there is a consistent decision tree for any training set with one path to leaf for each example (unless f nondeterministic) Decision tree for training examples probably won t generalize to new examples Compact decision trees are preferable B. Beckert: KI für IM p.13
27 Decision Trees Properties Decision trees can approximate any function of the input attributes ( correct decision tree may be infinite) Trivially, there is a consistent decision tree for any training set with one path to leaf for each example (unless f nondeterministic) Decision tree for training examples probably won t generalize to new examples Compact decision trees are preferable More expressive hypothesis space increases chance that target function can be expressed increases number of hypotheses consistent with training set may get worse predictions B. Beckert: KI für IM p.13
28 Decision Trees Example For Boolean functions: truth-table row = path to leaf in decision tree B. Beckert: KI für IM p.14
29 Hypothesis Spaces How many distinct decision trees with n Boolean attributes? B. Beckert: KI für IM p.15
30 Hypothesis Spaces How many distinct decision trees with n Boolean attributes? = number of Boolean functions B. Beckert: KI für IM p.15
31 Hypothesis Spaces How many distinct decision trees with n Boolean attributes? = number of Boolean functions = number of distinct truth tables with 2 n rows B. Beckert: KI für IM p.15
32 Hypothesis Spaces How many distinct decision trees with n Boolean attributes? = number of Boolean functions = number of distinct truth tables with 2 n rows = 2 2n B. Beckert: KI für IM p.15
33 Hypothesis Spaces How many distinct decision trees with n Boolean attributes? = number of Boolean functions = number of distinct truth tables with 2 n rows = 2 2n Example With 6 Boolean attributes, there are 18,446,744,073,709,551,616 trees B. Beckert: KI für IM p.15
34 Decision Tree Learning Aim Find a small tree consistent with the training examples Idea (Recursively) choose most significant attribute as root of (sub)tree B. Beckert: KI für IM p.16
35 Choosing an Attribute Idea A good attribute splits the examples into subsets that are (ideally) all positive or all negative, i.e., gives much information about the classification B. Beckert: KI für IM p.17
36 Choosing an Attribute Idea A good attribute splits the examples into subsets that are (ideally) all positive or all negative, i.e., gives much information about the classification Example Patrons is a better choice B. Beckert: KI für IM p.17
37 Decision Tree Learning: Algorithm function DTL(examples,attributes,default) returns a decision tree if examples is empty then return default else if all examples have the same classification then return the classification else if attributes is empty then return MAJORITY-VALUE(examples) else best CHOOSE-ATTRIBUTE(attributes,examples) tree a new decision tree with root test best m MAJORITY-VALUE(examples) for each value v i of best do examples i {elements of examples with best = v i } subtree,dtl(examples i,attributes best,m) add a branch to tree with label v i and subtree subtree return tree B. Beckert: KI für IM p.18
38 Example Decision tree learned from the 12 examples Substantially simpler than true tree A more complex hypothesis isn t justified by small amount of data B. Beckert: KI für IM p.19
39 Performance Measurement Hume s Problem of Induction How do we know that h f? Use theorems of computational/statistical learning theory Try h on a new test set of examples (use same distribution over example space as training set) B. Beckert: KI für IM p.20
40 Performance Measurement Learning curve % correct on test set as a function of training set size % correct on test set Training set size B. Beckert: KI für IM p.21
41 Performance Measurement (cont.) Learning curve depends on realizable (can express target function) vs. non-realizable Non-realizability can be due to missing attributes, or restricted hypothesis class (e.g., thresholded linear function) redundant expressiveness (e.g., loads of irrelevant attributes) B. Beckert: KI für IM p.22
42 Summary Learning needed for unknown environments, lazy designers Learning agent = performance element + learning element Learning method depends on type of performance element, available feedback, type of component to be improved For supervised learning, the aim is to find a simple hypothesis approximately consistent with training examples Decision tree learning using information gain Learning performance = prediction accuracy measured on test set B. Beckert: KI für IM p.23
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