1. Inference in FOL Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 9 Fall 2004

2. CSE 471/598, CBS 598 by H. Liu2 Inference with quantifiers Previous Rules for propositional logic Modus Ponens (p 211) And-Elimination, plus Fig 7.11 (p 210) Resolution (p 213) What’s new now are variables and quantifiers How can we reuse the above rules?  By introducing more rules to remove V’s and Q’s More rules with SUBST( ,  )  - binding list,  - sentence SUBST({x/Sam}, …)  Sibling(x,John)) = Sibling(Sam,John)

3. CSE 471/598, CBS 598 by H. Liu3 Inference rules for quantifiers Universal Instantiation (UI) For any sentence , variable v, and ground term g, E.g., for all greedy kings are evil Existential Instantiation (EI) For any , v, and constant k new to KB, E.g., John has a crown on his head

4. CSE 471/598, CBS 598 by H. Liu4 UI and EI The new k in EI is used to name a specific object and called a Skolem constant in logic If it’s not new, what would happen? UI and EI are different in inference UI can be applied many times EI can be applied once, then the sentence with EQ can be removed  Is the new KB logically equivalent to the old KB?  They are only inferentially equivalent

5. CSE 471/598, CBS 598 by H. Liu5 Generalized Modus Ponens It raises Modus Ponens from Prop Logic to FOL – lifting It takes bigger steps in inference It’s focused - not randomly trying UIs If we know King(John),  y Greedy(y), and  x King(x) ^ Greddy(x) => Evil(x), we can apply GMP with Θ is {x/John, y/John}, q is Evil(x), and SUBST(Θ, q) = Evil(John)

6. CSE 471/598, CBS 598 by H. Liu6 Unification UNIFY (p,q)=  (unifier - the binding list) where SUBST( ,p) = SUBST( ,q) Some examples: Unifying Knows(Jo,x) with Knows(Jo,Ja)Knows(y,Bi)Knows(y,Mother(y)) Knows(x,Elizabeth) Standardizing apart: Rename one sentence to avoid name clashes Most general unifier Finding MGU algorithm in Fig 9.1  Occur-Check if the variable itself occurs inside the complex term; omitting it can result in unsound inferences

7. CSE 471/598, CBS 598 by H. Liu7 An example proof The law says that it is a crime for an American to sell weapons to hostile nations. The country Nono, an enemy of America, has some missiles, and all of its missiles were sold to it by Colonel West, who is American. (p 280) To prove (infer) that West is a criminal. FOL, using these predicates American, Weapon, Sells(x,y,z), Hostile, Criminal, Owns(x,y), Missile, Enemy(x,America)  Datalog knowledge bases – FO definite clauses with no function symbols How do you prove it?

8. CSE 471/598, CBS 598 by H. Liu8 Example (continued) The proof can be very LONG for such a simple problem if using UI (substituting with a ground term). The branching factor increases as KB grows. Universal Instantiation has an enormous branching factor. We need more principled ways for proofs Combining atomic sentences to conjunctions, instantiating universal rules to match, then applying Generalized Modus Ponens Two ways to proceed

9. CSE 471/598, CBS 598 by H. Liu9 Forward chaining A first-order definite clause either is atomic or is an implication p1^p2^…^p3  p p’s are positive literals Start with the KB and generate new conclusions using Modes Ponens FC is usually used when a new fact is added into the KB, check if there are any new consequences Algorithm FOL-FC-ASK (Fig 9.3) An example of answering who is criminal - Criminal(x) (Fig 9.4)

10. CSE 471/598, CBS 598 by H. Liu10 Backward chaining Start with something to be proved and find implication sentences to conclude it The list of goals is a stack waiting to be worked on When all goals are satisfied, the proof succeeds BC is used when there is a goal to be proved Algorithm FOL-BC-ASK (Fig 9.6) An example of answering who is criminal -Criminal(x) (Fig 9.7) BC is a depth-first search algorithm that suffers from problems with repeated states and incompleteness Which chaining method to use? Any suggestion?

11. CSE 471/598, CBS 598 by H. Liu11 Completeness An incomplete proof procedure - there are sentences entailed by the KB, the procedure cannot find them (Fig 9.10). Significance of completeness (Math) All conjectures can be established mechanically We only need a set of fundamental axioms Significance to AI - a machine can solve any problem that can be stated in FOL Looking for a complete proof procedure

12. CSE 471/598, CBS 598 by H. Liu12 Completeness theorem If KB  , then KB  R  - Godel’s completeness theorem What’s the procedure R ? “There exists one” does tell us which one Resolution algorithm is one such procedure Entailment in FOL is semidecidable: We can show sentences follow, if they do; but we can’t always show if they don’t.

13. CSE 471/598, CBS 598 by H. Liu13 Resolution A refutation complete inference procedure Algorithm (Fig. 7.12): the same for both logics Resolution proves KB   by proving KB^!  unsatisfiable, or by deriving the empty clause Conjunctive normal form for FOL Resolution inference rule where UNIFY(l i, ¬m j ) =  First-order literals are complementary if one unifies with the negation of the other

14. CSE 471/598, CBS 598 by H. Liu14 Canonical forms for resolution Conjunctive normal form (CNF) CNF: Conjunction of disjunctions of literals The KB is one big, implicit conjunctions Implicative normal form (INF) CNF and INF are notational variants Skolemization Eliminate existential quantifiers  It is not as straightforward to replace with a new constant Skolem functions  The arguments of the Skolem function are all universally quantified variables

15. CSE 471/598, CBS 598 by H. Liu15 Conversion to normal form 1. Eliminate implications 2. Move NOT inwards 3. Standardize variables 4. Skolemize - removing E-Quantifier introducing a function associated with the variable 5. Drop universal quantifiers 6. Distribute ^ over v An example  Everyone is loved by someone  Everyone loves all animals  Everyone who loves all animals is loved by someone

16. CSE 471/598, CBS 598 by H. Liu16 Proof revisit Criminal(West)

17. CSE 471/598, CBS 598 by H. Liu17 Another example proof Everyone who loves all animals is loved by someone. Anyone who kills an animal is loved by no one. Jack loves all animals. Either Jack or Curiosity killed the cat, who is named Tuna. Did Curiosity kill the cat?

18. CSE 471/598, CBS 598 by H. Liu18 Resolution strategies To guide the fast proof using refutation Unit preference  Prefer inferences that produce shorter sentences Set of support  Use the negated query as the set of support Input resolution  Combines one of the input sentences (KB or Q) Subsumption  Keeps KB small by eliminating all sentences that are subsumed by an existing sentence

19. CSE 471/598, CBS 598 by H. Liu19 Completeness of resolution Resolution is refutation-complete If a set of sentences is unsatisfiable, then resolution will always be able to derive a contradiction – the empty clause.

20. CSE 471/598, CBS 598 by H. Liu20 Summary Simple FOL proofs are complex and long Generalized MP is natural and powerful, used in forward or backward chaining Resolution with refutation using resolution is complete There are strategies to guide search