Advanced Artificial Intelligence Lecture 5: Inductive Logic Programming

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Advanced Artificial IntelligenceLecture 5
Inductive Logic Programming

• Bob McKay
• School of Computer Science and Engineering
• College of Engineering
• Seoul National University

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Outline

• Inductive Logic Programming
• FOIL

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What is Relational Learning?

• Relational Learning systems use a representation
  language which goes beyond the propositional
• Permitting learning about relationships between
  data items
• a definition of a sort or append function
• family relationships
• spatial or geometric relationships
• temporal relationships
• Relational systems
• Inductive Logic Programming
• Genetic Programming
• Recurrent neural networks can learn simple
  relationships
• Its often possible to turn a known relationship
  into a propositional learning problem
• Eg learning time series

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Relational Learning - status

• Still largely a research domain rather than
  practical applications
• Relational learning is difficult
• Computationally expensive
• Data expensive
• Finding good algorithms is difficult
• Hence achievements so far are limited
• An important research domain because
• Relational problems are widespread in practical
  applications
• There are often no alternative approaches to
  these problems

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Inductive Logic Programming

• Representation Language
• Prolog (ie first order predicate rules)
• Some systems slightly extend the representation
  language
• Learning Algorithms
• Usually Deterministic
• Some stochastic (evolutionary) approaches have
  been tried, but with limited success
• Generally gradient descent algorithms, often
  extended with special-purpose heuristic

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Some Terminology A Reminder

• An atom a is a formula of the form
• P(x1,, xn)
• (in general logic, x1,, xn may contain function
  symbols, but almost ILP systems are limited to
  the case where there are no function symbols)
• A literal is either an atom or the negation of an
  atom
• A (predicate calculus or relational) rule is a
  formula of the form
• L1 Ln ? L0
• Where all of the Li are literals
• And in particular, L0 is a positive literal (ie
  an atom)

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Learning Example

• For example, a relational learner might be asked
  to learn the member predicate from examples
• That is, given
• member(1,1).
• member(1,1,2).
• member(2,1,2).
• Etc.
• It should learn
• member(X,YYs) - X Y.
• member(X,YYs - member(X,Ys).

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Negative Examples

• Many relational learning systems require also
  negative examples for their learning, such as
• That is, given
• Not member(2,1).
• Not member(3,1,2).
• Not, member(4,1,2,3).
• Etc.
• Alternatively, such systems may rely on the
  Closed World Assumption
• I.e. anything not included in the positive
  examples is assumed to be a negative example

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