Intelligent Systems

1. Intelligent Systems Model Checking and Theorem Proving slides by Gulay Unel

2. Overview • Model Checking • Theorem Proving

3. Model Checking - Overview • Introduction • Models • LTL • CTL • Other logics • Model Checking

4. Introduction • Model checking is an automated technique that, given a finite-state model of a system and a formal property, checks whether this property holds for (a given state in) that model • Formally: M ⊨Ф where the model M represents a design, and the property Ф formalizes its correctness criteria • Model checking focuses mostly on automatic decision procedures • The model is often restricted to a finite state transition system • Properties are expressed in a propositional logic such as: LTL, CTL for which finite-state model checking is known to be decidable • Note that model checking problem is not limited to finite state systems or propositional logics

5. Model: Transition System • A transition system TS is a tuple (S, →, I, AP, L) where • S is a set of states • → ⊆ S × S • I ⊆ S is a set of initial states • AP is a set of atomic propositions • L: S → 2AP is a labeling function • An execution: π = s0, s1, ... such that • for every i ≥ 0, si → si+1 • s0 ∈ I • Trace of an execution: sequence of sets of atomic propositions, trace (π) = L(s0), L(s1), ... • Traces(TS): set of all traces of all executions of TS, it defines the observable behaviour of TS

6. Property: Temporal Logic • The properties of transition systems are expressed in temporal logics, most often in propositional: • Linear Time Temporal Logic (LTL) • Branching Time Temporal Logic (CTL) • In all of the logics the atomic formulas are the atomic propositions from AP • Each state s in a model TS has a set of atomic propositions L(s) that are true in that state, all the other atomic propositions are false in s

7. Syntax of LTL • propositional logic • true • a, b, …∈ AP •  α, αβ • temporal operators • X α : neXt step fulfills α • F α : sometimes in the Future α will hold • G α : α Globally holds • α U β : α holds Until β holds • linear temporal logic is a logic for describing linear time properties

8. Derived Operators false ≡ true α β ≡(α β) α β ≡α β α β ≡ (α β) (β  α) precedence order: the unary operators bind stronger than the binary ones. and X bind equally strong. U takes precedence over , , and  , X, F, G > U > , , , 

9. Examples – Properties of a traffic light • the light is never red and green: (redgreen) • whenever the light is red, it cannot become green immediately afterwards: red  X green • eventually, the light becomes green: F green

10. Practical properties in LTL • Reachability • reachability: F α • conditional reachability: α U β • reachability from any state: not expressible • Safety: G α • Liveness: G(α F β) and others • Fairness: G F α and others

11. Semantics over Words The language induced by LTL formula α over AP = {a1, …, an} is: L(α) = { w ∈ (2AP)w | M ⊨α} where ⊨ is defined as follows: (let w=A0A1A2... and w[i] = AiAi+1Ai+2...) w ⊨ true w ⊨ ai iff ai∈ A0 w ⊨αβiff w ⊨α and w ⊨β w ⊨  α iff w ⊭α w ⊨ X α iff w[1]=A1A2A3... ⊨α w ⊨ F α iff j ≥ 0. w[j] ⊨α w ⊨ G α iff j ≥ 0. w[j] ⊨α w ⊨ α U β iff j ≥ 0. w[j] ⊨βand0 ≤ i < j. w[i] ⊨α

12. Semantics for Transition Systems Semantics is defined via set inclusion, a system satisfies a formula iff all traces are allowed w.r.t. The formula TS ⊨ α iff Traces(TS) ⊆ L(α)

13. CTL in a nutshell • Branching time model • Path quantifiers (in addition to LTL) • A = “for all future paths” • E = “for some future path” • Example: A F a = “inevitably a” a a A F a a

14. Other logics : Mu - Calculus • Logic of relations with fixed point operator • Can express transitive closure • Good for describing algorithms

15. Model Checking G(a Þ F b) • input: • temporal logic (property) • transition system (model) • output • yes • no + counterexample yes MC no a a b b

16. LTL Model Checking • Vardi and Wolper • Apply Büchi’s technique to LTL • Automaton construction yields optimal decision algorithm • Kurshan • Specify properties directly as automata • example: infinitely often a (G F a) a Øa true

17. CTL Model Checking • Reasoning about properties of non-deterministic programs • branching time properties of programs • fixed point characterizations (Tarski) • every monotonic function has least/greatest fixed point • key idea: apply to finite graphs, not infinite trees • can directly calculate Tarski fixed points • Applications • finite state machines in hardware • protocols • proved incorrectness of some published designs

18. Theorem Proving (Resolution) - Overview • Introduction • Proof System • Resolution • Heuristic Search • Applications • Other Topics

19. Introduction Unlike in model checking, theorem proving solves the general validity of a formula (whether a formula α holds in all models) ⊨ α • Utilizes the proof inference technique in some proof system • Problem is transformed to a sequent, a working representation for the theorem proving problem • The simplest sequent used in natural deduction: ⊢ α • A sequent holds when it satisfies its intended semantics • For example, ⊢ α is derivable in natural deduction only if the formula α holds in any model

20. Proof System A proof system is collection of inference rules of the form: P1 …    Pn name C where C is a conclusion sequent, and Pi‘s are premises sequents . If an infererence rule does not have any premises (called an axiom), its conclusion automatically holds.

21. Proof Tree • A proof of a sequent is a proof tree whose nodes are sequents • The root is the sequent to be proven (the theorem) • For each sequent in the tree, all of its children are premises of some inference rule in which that sequent is a conclusion • A proof is complete when each sequent in the proof tree has an associated inference rule • There are two ways for building a proof tree: • Bottom-up • Top-down

22. Proof by Refutation • (Proof by contradiction, reductio ad absurdum) • Method: • Negate the theorem statement & add to axioms • Show that this set of sentences is self-inconsistent • Use rules of inference to derive the False statement • This means that the sentences can’t all be true at same time • But the axioms are true • Hence the negated theorem must be false • Hence the theorem must be true

23. The Resolution Method • Uses proof by refutation • Requires sentences to be in a particular format • Conjunctive Normal Form [CNF] • Uses a single inference rule • Generalised resolution rule • Need to understand the unification method (this lecture) • Method is refutation-complete • If a theorem is true and representable in first order logic • Then this method will prove it [amazing result by Robinson, 1965] • No guarantees given about how long it will take • Actually takes a long time to prove even fairly trivial theorems • Can use heuristics to speed it up

24. The Resolution Method - Overview • Maintain a knowledge base of clauses • Start with the axioms and negation of theorem • Resolve pairs of clauses • Using single rule of inference (generalised resolution) • Resolved sentence contains fewer literals • Proof ends with the empty clause • Signifies a contradiction • Must mean the negated theorem is false • Because the axioms are consistent • Therefore the original theorem was true

25. Resolution Rule • Takes two clauses in Conjunctive Normal Form • Finds a literal L1 in one and and a literal L2 in the other • Such that the L1 unifies with ¬L2 (with substitution mu) • In the resolved clause, L1 and L2 are omitted • And the substitution is applied to the whole disjunction p1∨ ... ∨ pj∨ ... ∨ pm,     q1∨ ... ∨ qk∨ ... ∨ qn, Subst(mu, (p1∨ ... ∨ pj-1 ∨ pj+1∨ ... ∨ pm∨ q1∨ ... qk-1∨ qk+1∨ ... ∨ qn))

26. Empty Clause Signifies False • Resolution theorem proving ends • When the resolved clause has no literals (empty) • This can only be because: • Two unit clauses were resolved • One was the negation of the other (after substitution) • Example: q(X) and ¬q(X) or: p(X) and ¬p(simon) • Hence if we see the empty clause • This was because there was an inconsistency • Hence the proof by contradiction has occurred

27. Aristotle Example • All men are mortal and Socrates is a man • Therefore Socrates is mortal • Initial Knowledge Base • is_man(X) → is_mortal(X) [universal quant. assumed] • is_man(socrates) • In Conjunctive Normal Form • ¬is_man(X) ∨ mortal(X) • is_man(socrates) • ¬is_mortal(socrates) [negation of theorem]

28. Reading off a ProofBacktrack and then Read Forward • You said that all men were mortal. • That means that for all things X, either X is not a man, or X is mortal [CNF step]. • If we assume that Socrates is not mortal, then, given your previous statement, this means Socrates is not a man [first resolution step]. • But you said that Socrates is a man, which means that our assumption was false [second resolution step], so Socrates must be mortal.

29. Alternative Search Tree How do you read the proof for this search tree?

30. Dealing with EqualityApproach 1: Add Knowledge • Problem with equality: • was_president(george_bush) and was_president(g_bush) • will not unify (syntactically different constants) • unification algorithm does not allow this unification • Even if we add to the knowledge base: • george_bush = g_bush • One alternative: add extra knowledge to KB • Axioms of equality (X=X, X=Y → Y=X, etc.) • Equality statements for each predicate: • X = Y → P(X) = P(Y) • Must be done for all predicates

31. Dealing with EqualityApproach 2: Add Demodulation rule • Demodulation rule of inference • Takes two input sentences, one expressing an equality • That sentence X = sentence Y • Finds a unification, mu, for X with a term, Z, in other clause • Applies mu to Y (not X) • Replaces occurrence of Z with Subst(mu, Y) X=Y, (…Z…) (…Subst(mu,Y)…)

32. Heuristic Search Overview • Pure Resolution Search tends to be slow • For interesting problems • Lots of clauses in the initial knowledge base • Each step adds a new clause (which can be used) • The search space gets too big • We can choose any pair of clauses to try to resolve • Heuristic type 1: • Intelligently choose which pair to resolve at any time • Heuristic type 2: • Prune the space: • Don’t allow resolution with certain clauses

33. Unit Preference Strategy • Greedy search • Prefer to resolve certain clause types when possible • Unit clauses: • Contain only a single literal, e.g., C = is_pm(tony) • Idea: • We are looking for the smallest (empty) clause • Resolving with the unit clause keeps clauses small • Effectiveness • Was very effective early on for simple problems • Doesn’t reduce branching rate for medium problems

34. Set of Support Strategy • Maintain a set of (support) clauses, SOS • Only allow resolution steps involving members of SOS • Idea: choose clauses not in SOS to be consistent • Hence a clause in SOS must eventually be resolved • In order to find a path to the solution • In practice: • Initially choose the SOS to be the negated theorem • Add any newly resolved clause to the SOS • Otter theorem prover uses this strategy

35. Input Resolution Strategy • Special case of the SOS strategy • Restrict the SOS to include • Only the clauses in the initial knowledge base • Clearly brings down the search space size • However, it is not complete for first order logic • But it is complete for • Horn-clause knowledge bases • such as Prolog programs

36. Subsumption of Clauses • One clause, C, subsumes clause D • If D is more specific than C (or, C is more general) • Naïve check for subsumption • Find a unifying substitution • allowing us to write D as a subset of the literals of C • such that variables and constants in D become variables in C • Example: • p(george) ∨ q(X) is subsumed by p(A) ∨ q(B) ∨ r(C) • Substitution: {george/A, X/B} • Second clause is clearly more general

37. Subsumption Strategy • Whenever a new clause is found • Check that there is no existing clause • which subsumes the new clause • Idea: removing more specific clauses • Will not change the inconsistency in the database • Because specific clauses can be inferred by the general ones • Hence the theorem will still be provable • But the search space will be reduced • Have to be careful: • Subsumption checking can be expensive • must be outweighed by the reduction in search space

38. Applications of ResolutionAlgebraic Theorem Proving • Bill McCune and Larry Wos • Argonne National Laboratories • Writing first order provers such as EQP & Otter • Solution of the Robbins Problem (boolean algs) • Stated over 60 years ago, mathematicians tried & failed • EQP solved this in 8 days in 1996 (after much devel) • Also nice: axiomatisations of algebras • Attempt to find more succinct ways of describing algebras • Use Otter to prove that the new way • Is equivalent to the normal way of axiomatising algebras

39. ApplicationsAutomated Conjecture Making • Automated Theory Formation (HR) • Used in mathematical (and bioinformatics) domains • Theories contain • concepts, examples, conjectures, proofs • HR uses Otter to prove its theorems • Effective in algebraic domains • See notes for anti-associative algebra results • In number theory • Otter is used as a filter (discard theorems it can prove) • Example conjectures made by HR (and proved by me): • Sum of divisors is prime → number of divisors is prime • Sum of divisors of a square is an odd number • Perfect numbers are pernicious [and many more…..]

40. Other Topics in Automated Reasoning: Interactive Proving • Interactive theorem proving • Necessary to interact with humans in order to prove theorems of any difficulty • Two (of many) approaches: • Let a theorem prover do simple tasks while you develop a theory (e.g., Buchberger’s Theorema) • Allow user to follow a proof attempt and step in to guide the prover • Needs visualisation tools to draw and annotate proof trees

41. Other TopicsHigher Order Theorem Proving • Exactly what you would expect • Expressing theorems in higher order logic • See lecture 4 • And proving them (possibly interactively) • HOL theorem prover • Larry Paulson’s group in cambridge • Has been used for verification tasks • type safety for Java • verification of crytographic protocols

42. Other TopicsDatabases and Competitions • TPTP library by Geoff Sutcliffe & Christian Suttner • Thousands of problems for theorem provers • Used to benchmark first order theorem provers • Contains 6973 theorems at present • HR is only non-human to add to this library • CASC competition by Sutcliffe et al. • Every year: who has the fastest/most accurate first order theorem prover on the planet? • Uses blind test from the TPTP library • Current chamption: Vampire • By Voronkov and Riazonov in Manchester

43. Bibliography • Model Checking and Theorem Proving: a Unified Framework, Thesis by Sergey Berezin, http://reports-archive.adm.cs.cmu.edu/anon/2002/CMU-CS-02-100.pdf. • A brief history of model checking, Talk by Ken McMillan, http://www.cs.uiowa.edu/~tinelli/classes/196/Spring07/notes/McMillan.pdf. • Artificial Intelligence, Course by Jeremy Gow, http://www.doc.ic.ac.uk/~sgc/teaching/v231/. • Introduction to Model Checking, Course by René Thiemann, http://cl-informatik.uibk.ac.at/teaching/ws07/imc/schedule.php .