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Markov Logic Networks November 18, 2008 CS 486/686 University of Waterloo Outline Markov Logic Networks Alchemy Readings: Matt Richardson and Pedro Domingos (2006), Markov Logic Networks, Machine Learning , 62, 107-136, 2006.

  1. Markov Logic Networks November 18, 2008 CS 486/686 University of Waterloo

  2. Outline • Markov Logic Networks • Alchemy • Readings: – Matt Richardson and Pedro Domingos (2006), Markov Logic Networks, Machine Learning , 62, 107-136, 2006. 2 CS486/686 Lecture Slides (c) 2008 P. Poupart

  3. Markov Logic Networks • Bayesian networks and Markov networks: – Model uncertainty – But propositional representation (e.g., we need one variable per object in the world) • First-order logic: – First-order representation (e.g., quantifiers allow us to reason about several objects simultaneously) – But we can’t deal with uncertainty • Markov logic networks: combine Markov networks and first-order logic 3 CS486/686 Lecture Slides (c) 2008 P. Poupart

  4. Markov Logic • A logical KB is a set of hard constraints on the set of possible worlds • Let’s make them soft constraints : when a world violates a formula, it becomes less probable, not impossible • Give each formula a weight : (higher weight � stronger constraint) P(world) ∝ e Σ weights of formulas it satisfies 4 CS486/686 Lecture Slides (c) 2008 P. Poupart

  5. Markov Logic: Definition • A Markov Logic Network (MLN) is a set of pairs (F, w) where – F is a formula in first-order logic – w is a real number • Together with a set of constants, it defines a Markov network with – One node for each grounding of each predicate in the MLN – One feature for each grounding of each formula F in the MLN, with the corresponding weight w 5 CS486/686 Lecture Slides (c) 2008 P. Poupart

  6. Example: Friends & Smokers Smoking causes cancer. Friends have similar smoking habits. 6 CS486/686 Lecture Slides (c) 2008 P. Poupart

  7. Example: Friends & Smokers ∀ ⇒ ( ) ( ) x Smokes x Cancer x ( ) ∀ ⇒ ⇔ x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) 7 CS486/686 Lecture Slides (c) 2008 P. Poupart

  8. Example: Friends & Smokers ∀ ⇒ 1 . 5 ( ) ( ) x Smokes x Cancer x ( ) ∀ ⇒ ⇔ 1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) 8 CS486/686 Lecture Slides (c) 2008 P. Poupart

  9. Example: Friends & Smokers ∀ ⇒ 1 . 5 ( ) ( ) x Smokes x Cancer x ( ) ∀ ⇒ ⇔ 1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) 9 CS486/686 Lecture Slides (c) 2008 P. Poupart

  10. Example: Friends & Smokers ∀ ⇒ 1 . 5 ( ) ( ) x Smokes x Cancer x ( ) ∀ ⇒ ⇔ 1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) Smokes(A) Smokes(B) Cancer(A) Cancer(B) 10 CS486/686 Lecture Slides (c) 2008 P. Poupart

  11. Example: Friends & Smokers ∀ ⇒ 1 . 5 ( ) ( ) x Smokes x Cancer x ( ) ∀ ⇒ ⇔ 1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) Friends(A,B) Friends(A,A) Smokes(A) Smokes(B) Friends(B,B) Cancer(A) Cancer(B) Friends(B,A) 11 CS486/686 Lecture Slides (c) 2008 P. Poupart

  12. Example: Friends & Smokers ∀ ⇒ 1 . 5 ( ) ( ) x Smokes x Cancer x ( ) ∀ ⇒ ⇔ 1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) Friends(A,B) Friends(A,A) Smokes(A) Smokes(B) Friends(B,B) Cancer(A) Cancer(B) Friends(B,A) 12 CS486/686 Lecture Slides (c) 2008 P. Poupart

  13. Example: Friends & Smokers ∀ ⇒ 1 . 5 ( ) ( ) x Smokes x Cancer x ( ) ∀ ⇒ ⇔ 1 . 1 x , y Friends ( x , y ) Smokes ( x ) Smokes ( y ) Two constants: Anna (A) and Bob (B) Friends(A,B) Friends(A,A) Smokes(A) Smokes(B) Friends(B,B) Cancer(A) Cancer(B) Friends(B,A) 13 CS486/686 Lecture Slides (c) 2008 P. Poupart

  14. Markov Logic Networks • MLN is template for ground Markov nets • Probability of a world x : ⎛ ⎞ 1 ∑ = ⎜ ⎟ P ( x ) exp w n ( x ) i i ⎝ ⎠ Z i Weight of formula i No. of true groundings of formula i in x • Typed variables and constants greatly reduce size of ground Markov net 14 CS486/686 Lecture Slides (c) 2008 P. Poupart

  15. Alchemy • Open Source AI package • http://alchemy.cs.washington.edu • Implementation of Markov logic networks • Problem specified in two files: – File1.mln (Markov logic network) – File2.db (database / data set) • Learn weights and structure of MLN • Inference queries 15 CS486/686 Lecture Slides (c) 2008 P. Poupart

  16. Markov Logic Encoding • File.mln • Two parts: – Declaration • Domain of each variable • Predicates – Formula • Pairs of weights with logical formula 16 CS486/686 Lecture Slides (c) 2008 P. Poupart

  17. Markov Logic Encoding • Example declaration – Domain of each variable • person = {Anna, Bob} – Predicates: • Friends(person,person) • Smokes(person) • Cancer(person) • Example formula – 8 Smokes(x) => Cancer(x) – 5 Friends(x,y) => (Smokes(x)<=>Smokes(y)) NB: by default, formulas are universally quantified in Alchemy 17 CS486/686 Lecture Slides (c) 2008 P. Poupart

  18. Dataset • File.db • List of facts (ground atoms) • Example: – Friends(Anna,Bob) – Smokes(Anna) – Cancer(Bob) 18 CS486/686 Lecture Slides (c) 2008 P. Poupart

  19. Syntax • Logical connective: – ! (not), ^ (and), v (or), => (implies), <=> (iff) • Quantifiers: – forall ( ∀ ), exist ( ∃ ) – By default unquantified variables are universally quantified in Alchemy • Operator precedence: – ! > ^ > v > => > <=> > forall = exist 19 CS486/686 Lecture Slides (c) 2008 P. Poupart

  20. Syntax • Short hand for predicates – ! operator: indicates that the preceding variable has exactly one true grounding – Ex: HasPosition(x,y!) : for each grounding of x , exactly one grounding of y satisfies HasPosition • Short hand for multiple weights – + operator: indicates that a different weight should be learned for each grounding of the following variable – Ex: outcome(throw,+face): a different weight is learned for each grounding of face 20 CS486/686 Lecture Slides (c) 2008 P. Poupart

  21. Next Class • Example problems in Alchemy 21 CS486/686 Lecture Slides (c) 2008 P. Poupart

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