Outline. Logic. Definition. Theorem (Gödel s Completeness Theorem) Summary of Previous Week. Undecidability. Unification

Size: px
Start display at page:

Download "Outline. Logic. Definition. Theorem (Gödel s Completeness Theorem) Summary of Previous Week. Undecidability. Unification"

Transcription

1 Logic Aart Middeldorp Vincent van Oostrom Franziska Rapp Christian Sternagel Department of Computer Science University of Innsbruck WS 2017/2018 AM UIBK) week 11 2/38 Definitions elimination x φ e φ[t/x] provided t is free for x in φ introduction φ[t/x] i x φ Definition (possibly infinite) set of formulas Γ, formula ψ sequent Γ ψ is valid if there exists (finite) natural deduction proof of ψ in which all premises are from Γ introduction elimination x 0. φ[x 0 /x] x φ i x φ x 0 χ φ[x 0 /x]. χ e (Gödel s Completeness ) natural deduction for predicate logic is sound and complete: Γ ψ Γ ψ is valid where x 0 is fresh variable that is used only inside box AM UIBK) week 11 3/38 AM UIBK) week 11 4/38

2 Overview Part I: Propositional Logic (Quantifier Equivalences) x φ x φ x φ x φ x φ x ψ x (φ ψ) x φ x ψ x (φ ψ) x y φ y x φ x y φ y x φ if x is not free in ψ then x φ ψ x (φ ψ) x φ ψ x (φ ψ) x φ ψ x (φ ψ) x φ ψ x (φ ψ) ψ x φ x (ψ φ) x φ ψ x (φ ψ) ψ x φ x (ψ φ) x φ ψ x (φ ψ) algebraic normal forms, binary decision diagrams, conjunctive normal forms, DPLL, Horn clauses, natural deduction, Post s adequacy theorem, resolution, SAT, semantics, soundness and completeness, sorting networks, syntax, Tseitin s transformation Part II: compactness, first-order theories, Gödel s incompleteness theorem, Löwenheim- Skolem, natural deduction, quantifier equivalences, reachability, resolution, second-order logic,, semantics, syntax, undecidability, unification Part III: Model Checking adequacy, branching-time temporal logic, CTL, fairness, linear-time temporal logic, model checking algorithms AM UIBK) week 11 5/38 AM UIBK) week 11 6/38 (Church) validity in predicate logic is undecidable: there is no algorithm input: formula φ in predicate logic output: yes if φ holds no if φ does not hold Proof Idea reduction from Post correspondence problem Post Correspondence Problem instance: question: finite sequence of pairs (s 1, t 1 ),..., (s k, t k ) of non-empty bitstrings is there sequence (i 1, i 2,..., i n ) with n 1 such that s i1 s i2... s in = t i1 t i2... t in? AM UIBK) week 11 7/38 AM UIBK) week 11 8/38

3 s s i : t i : s i : t i : s i : t i : solution s = t = no solution solution validity in predicate logic is undecidable Proof Idea translate PCP instance C into predicate logic formula φ such that φ C has solution (Post) Post correspondence problem is undecidable AM UIBK) week 11 9/38 AM UIBK) week 11 10/38 Proof let C = ((s 1, t 1 ), (s 2, t 2 ),..., (s k, t k )) function symbols e: constant f 0, f 1 : arity 1 predicate symbol P: arity 2 if b 1, b 2,..., b n {0, 1} then f b1 b 2 b n (t) denotes f bn ( (f b2 (f b1 (t))) ) φ = φ 1 φ 2 φ 3 with φ 1 = k P(f si (e), f ti (e)) i=1 φ 2 = v w ( P(v, w) ) k P(f si (v), f ti (w)) i=1 C = ((10, 101), (011, 11), (10, 0)) φ = P(f 0 (f 1 (e)), f 1 (f 0 (f 1 (e)))) P(f 1 (f 1 (f 0 (e))), f 1 (f 1 (e))) P(f 0 (f 1 (e)), f 0 (e)) v w ( P(v, w) P(f 0 (f 1 (v)), f 1 (f 0 (f 1 (w)))) z P(z, z) P(f 1 (f 1 (f 0 (v))), f 1 (f 1 (w))) P(f 0 (f 1 (v)), f 0 (w)) ) φ 3 = z P(z, z) φ C has solution AM UIBK) week 11 11/38 AM UIBK) week 11 12/38

4 Remark unification and skolemization are required to extend resolution from propositional logic to predicate logic Greek alphabet alpha α A iota ι I rho ρ P beta β B kappa κ K sigma σ ς Σ gamma γ Γ lambda λ Λ tau τ T delta δ mu µ M upsilon υ Υ epsilon ɛ ε E nu ν N phi φ ϕ Φ zeta ζ Z xi ξ Ξ chi χ X eta η H omicron o O psi ψ Ψ theta θ ϑ Θ pi π Π omega ω Ω AM UIBK) week 11 13/38 AM UIBK) week 11 14/38 Definition substitution is set of variable bindings θ = {x 1 t 1,..., x n t n } with terms t 1,..., t n and pairwise different variables x 1,..., x n given substitution θ = {x 1 t 1,..., x n t n } and expression E, instance Eθ of E is obtained by simultaneously replacing each occurrence of x i in E by t i composition of substitutions θ = {x 1 t 1,..., x n t n } and σ = {y 1 s 1,..., y k s k } is substitution θσ = {x 1 t 1 σ,..., x n t n σ} {y i s i y i x j for all 1 j n} Lemma composition of substitutions is associative: Definitions (ρσ)τ = ρ(στ) substitution θ is at least as general as substitution σ if θµ = σ for some substitution µ unifier of set S of terms is substitution θ such that sθ = tθ for all terms s, t S most general unifier (mgu) is at least as general as any other unifier θ = { x g(y, z), y a } E = P(f (y), x, y) σ = { x f (y), z f (x) } Eθ = P(f (a), g(y, z), a) θσ = { x g(y, f (x)), y a, z f (x) } Eσ = P(f (y), f (y), y) σθ = { x f (a), z f (g(y, z)), y a } terms f (x, g(y), x) and f (z, g(u), h(u)) are unifiable: { x h(a), y a, z h(a), u a } {u a } unifiers { x h(u), y u, z h(u) } mgu { x h(g(u)), y g(u), z h(g(u)), u g(u) } { u g(u) } AM UIBK) week 11 15/38 AM UIBK) week 11 16/38

5 unifiable terms have mgu which can be computed by unification algorithm Algorithm d decomposition f (x, g(y), x) f (z, g(u), h(u)) d x z, g(y) g(u), x h(u) E 1, f (s 1,..., s n ) f (t 1,..., t n ), E 2 E 1, s 1 t 1,..., s n t n, E 2 v { x z } g(y) g(u), z h(u) t removal of trivial equations d mgu { x h(u), y u, z h(u) } E 1, t t, E 2 E 1, E 2 y u, z h(u) v { y u } v variable elimination z h(u) E 1, x t, E 2 (E 1, E 2 )σ and E 1, t x, E 2 (E 1, E 2 )σ if x does not occur in t (occurs check) and σ = {x t} v { z h(u) } AM UIBK) week 11 17/38 AM UIBK) week 11 18/38 there are no infinite derivations U σ1 V σ2 W σ3 if s and t are unifiable then for every maximal derivation s t σ1 E 1 σ2 E 2 σ3 σn E n E n = σ 1 σ 2 σ 3 σ n is mgu of s and t Optional Failure Rules E 1, f (s 1,..., s n ) g(t 1,..., t m ), E 2 E 1, x t, E 2 E 1, t x, E 2 if x occurs in t and x t AM UIBK) week 11 19/38 AM UIBK) week 11 20/38

6 Definitions prenex normal form is predicate logic formula Q 1 x 1 Q 2 x 2... Q n x n φ with Q i {, } and φ quantifier free Skolem normal form is closed (no free variables) prenex normal form x 1 x 2... x n φ with φ quantifier free CNF x y ((P(f (x)) P(g(y)) Q(g(y))) ( Q(g(y)) P(g(y)) Q(g(x)))) { { P(f (x)), P(g(y)), Q(g(y)) }, { Q(g(y)), P(g(y)), Q(g(x)) } } formula φ prenex normal form ψ such that φ ψ Proof 1 rename all bound variables such that all variables in quantifications are distinct 2 push logical connectives through quantifiers: x φ x φ x φ x φ x φ ψ x (φ ψ) φ x ψ x (φ ψ) x φ ψ x (φ ψ) φ x ψ x (φ ψ) x φ ψ x (φ ψ) φ x ψ x (φ ψ) x φ ψ x (φ ψ) φ x ψ x (φ ψ) x φ ψ x (φ ψ) φ x ψ x (φ ψ) x φ ψ x (φ ψ) φ x ψ x (φ ψ) AM UIBK) week 11 21/38 AM UIBK) week 11 22/38 Definition formulas φ and ψ are equisatisfiable (φ ψ) if φ is satisfiable ψ is satisfiable sentence φ Skolem normal form ψ such that φ ψ Proof () 1 transform φ into closed prenex normal form Q 1 x 1 Q 2 x 2... Q n x n χ with χ in CNF 2 repeatedly replace by x 1... x i 1 x i Q i+1 x i+1... Q n x n ψ x 1... x i 1 Q i+1 x i+1... Q n x n ψ[f (x 1,..., x i 1 )/x i ] where f is new function symbol of arity i 1 s z x y ((P(x) P(y) Q(z)) ( Q(x) P(y) Q(z))) { x f (z) } z y ((P(f (z)) P(y) Q(z)) ( Q(f (z)) P(y) Q(z))) { y g(z) } z ((P(f (z)) P(g(z)) Q(z)) ( Q(f (z)) P(g(z)) Q(z))) x y ( z P(z) u (Q(x, u) v Q(y, v))) x y z u v (P(z) ( Q(x, u) Q(y, v))) { y f (x) } {z g(x) } x u v (P(g(x)) ( Q(x, u) Q(f (x), v))) { v h(x, u) } x u (P(g(x)) ( Q(x, u) Q(f (x), h(x, u)))) AM UIBK) week 11 23/38 AM UIBK) week 11 24/38

7 Definitions literal is atom p or negation of atom p clause is set of literals {l 1,..., l n }, representing l 1 l n Propositional Logic Propositional Logic clausal form is set of clauses {C 1,..., C m }, representing C 1 C m literals l 1 and l 2 are complementary if l 1 = l2 c clauses C 1 and C 2 clash if literal l with l C 1 and l c C 2 resolvent of clashing clauses C 1 and C 2 is clause (C 1 \ {l}) (C 2 \ {l c }) AM UIBK) week 11 25/38 AM UIBK) week 11 26/38 Propositional Logic input: clausal form S output: yes if S is satisfiable no if S is unsatisfiable 1 repeatedly add resolvent of pair of clashing clauses in S 2 return no as soon as empty clause is derived 3 return yes if all clashing clauses have been resolved Definition refutation of S is resolution derivation of from S Propositional Logic resolution is sound and complete: clausal form S is unsatisfiable if and only if S admits refutation AM UIBK) week 11 27/38 AM UIBK) week 11 28/38

8 Definitions literal is atom P(t 1,..., t n ) or negation of atom P(t 1,..., t n ) clause is set of literals {l 1,..., l n }, representing l 1 l n clausal form is set of clauses {C 1,..., C m }, representing (C 1 C m ) clauses C 1 and C 2 without common variables clash if literals l 1 C 1 and l 2 C 2 such that l 1 and l2 c are unifiable resolvent of clashing clauses C 1 and C 2 is clause where θ is mgu of l 1 and l c 2 ((C 1 \ {l 1 }) (C 2 \ {l 2 }))θ (1) 1. { P(x), Q(x), R(x, f (x)) } 2. { P(x), Q(x), S(f (x)) } 3. { T (a) } 4. { P(a) } 5. { R(a, y), T (y) } 6. { T (x), Q(x) } 7. { T (x), S(x) } 8. { Q(a) } resolve 3, 6 { x a } 9. { Q(a), S(f (a)) } resolve 2, 4 { x a } 10. { Q(a), R(a, f (a)) } resolve 1, 4 { x a } 11. { S(f (a)) } resolve 8, { R(a, f (a)) } resolve 8, { T (f (a)) } resolve 5, 12 { y f (a) } 14. { S(f (a)) } resolve 7, 13 { x f (a) } 15. resolve 11,14 AM UIBK) week 11 29/38 AM UIBK) week 11 30/38 (2) 1. { P(x, y), P(y, x) } 2. { P(x, y), P(y, z), P(x, z) } 3. { P(x, f (x)) } 4. { P(x, x) } 3. { P(x, f (x )) } rename 3 5. { P(f (x), x) } resolve 1, 3 { y f (x), x x } 6. { P(f (x), z), P(x, z) } resolve 2, 3 { y f (x), x x } 5. { P(f (x ), x ) } rename 5 7. { P(z, z) } resolve 6, 5 { x z, x z } 8. resolve 4, 7 { x z } x y z ( ( P(x, y) P(y, x)) ( P(x, y) P(y, z) P(x, z)) P(x, f (x)) P(x, x) ) resolution is sound: clausal form S is unsatisfiable if S admits refutation Problem resolution is incomplete 1. { P(x), P(y) } 2. { P(x ), P(y ) } 3. { P(y), P(y ) } resolve 1, 2 { x x } unsatisfiable but no refutation AM UIBK) week 11 31/38 AM UIBK) week 11 32/38

9 with Factoring Solution incorporate factoring: Cσ is a factor of C if two or more literals in C have mgu σ 1. { P(x), P(y) } 2. { P(x ), P(y ) } 3. { P(x) } factor 1 4. { P(x ) } factor 2 5. resolve 3, 4 input: clausal form S output: yes if S is satisfiable no if S is unsatisfiable if S is satisfiable (or unsatisfiable) 1 repeatedly add resolvents (renaming clauses if necessary) and factors 2 return no as soon as empty clause is derived 3 return yes if all clashing clauses have been resolved and factoring produces no new clauses (modulo renaming) 1. { R(x), Q(f (x)) } 1. { R(x ), Q(f (x )) } rename 1 2. { R(f (x)), Q(f (y)) } 4. { Q(f (y)), Q(f (f (x))) } resolve 1, 2 { x f (x) } 3. { Q(f (f (f (a)))) } 5. { Q(f (f (x))) } factor 4 { y f (x) } 6. resolve 3, 5 { x f (a) } AM UIBK) week 11 33/38 AM UIBK) week 11 34/38 resolution with factoring is sound and complete: clausal form S is unsatisfiable if and only if S admits refutation Validity Procedure sentence φ 1 transform φ into Skolem normal form ψ 1. { P(x), P(f (x)) } 2. { P(a) } 3. { P(f (a)) } resolve 1, 2 { x a } 4. { P(f (f (a))) } resolve 1, 3 { x f (a) } 5. { P(f (f (f (a)))) } resolve 1, 4 { x f (f (a)) } 6. { P(f (f (f (f (a))))) } resolve 1, 5 { x f (f (f (a))) } 2 extract clausal form S from ψ 3 apply resolution to S 4 φ is valid if and only if S admits a refutation. AM UIBK) week 11 35/38 AM UIBK) week 11 36/38

10 Huth and Ryan Section 2.5 Wikipedia [ accessed August 14, 2017 ] Wikipedia [ accessed August 15, 2017 ] Wikipedia [ accessed August 15, 2017 ] AM UIBK) week 11 37/38 AM UIBK) week 11 38/38

Mathematics Review Exercises. (answers at end)

Mathematics Review Exercises. (answers at end) Brock University Physics 1P21/1P91 Mathematics Review Exercises (answers at end) Work each exercise without using a calculator. 1. Express each number in scientific notation. (a) 437.1 (b) 563, 000 (c)

More information

First-Order Logic. Resolution

First-Order Logic. Resolution First-Order Logic Resolution 1 Resolution for predicate logic Gilmore s algorithm is correct and complete, but useless in practice. We upgrade resolution to make it work for predicate logic. 2 Recall:

More information

COMP9414: Artificial Intelligence First-Order Logic

COMP9414: Artificial Intelligence First-Order Logic COMP9414, Wednesday 13 April, 2005 First-Order Logic 2 COMP9414: Artificial Intelligence First-Order Logic Overview Syntax of First-Order Logic Semantics of First-Order Logic Conjunctive Normal Form Wayne

More information

First-Order Logic (FOL)

First-Order Logic (FOL) First-Order Logic (FOL) Also called Predicate Logic or Predicate Calculus 2. First-Order Logic (FOL) FOL Syntax variables x, y, z, constants a, b, c, functions f, g, h, terms variables, constants or n-ary

More information

CS156: The Calculus of Computation Zohar Manna Winter 2010

CS156: The Calculus of Computation Zohar Manna Winter 2010 Page 3 of 35 Page 4 of 35 quantifiers CS156: The Calculus of Computation Zohar Manna Winter 2010 Chapter 2: First-Order Logic (FOL) existential quantifier x. F [x] there exists an x such that F [x] Note:

More information

Entities for Symbols and Greek Letters

Entities for Symbols and Greek Letters Entities for Symbols and Greek Letters The following table gives the character entity reference, decimal character reference, and hexadecimal character reference for symbols and Greek letters, as well

More information

1 Integration of Rational Functions Using Partial Fractions

1 Integration of Rational Functions Using Partial Fractions MTH Fall 008 Essex County College Division of Mathematics Handout Version 4 September 8, 008 Integration of Rational Functions Using Partial Fractions In the past it was far more usual to simplify or combine

More information

T m / A. Table C2 Submicroscopic Masses [2] Symbol Meaning Best Value Approximate Value

T m / A. Table C2 Submicroscopic Masses [2] Symbol Meaning Best Value Approximate Value APPENDIX C USEFUL INFORMATION 1247 C USEFUL INFORMATION This appendix is broken into several tables. Table C1, Important Constants Table C2, Submicroscopic Masses Table C3, Solar System Data Table C4,

More information

CSSS/STAT/SOC 321 Case-Based Social Statistics I. Levels of Measurement

CSSS/STAT/SOC 321 Case-Based Social Statistics I. Levels of Measurement CSSS/STAT/SOC 321 Case-Based Social Statistics I Levels of Measurement Christopher Adolph Department of Political Science and Center for Statistics and the Social Sciences University of Washington, Seattle

More information

Handout Proof Methods in Computer Science

Handout Proof Methods in Computer Science Handout Proof Methods in Computer Science Sebastiaan A. Terwijn Institute of Logic, Language and Computation University of Amsterdam Plantage Muidergracht 24 1018 TV Amsterdam the Netherlands terwijn@logic.at

More information

The non-logical symbols determine a specific F OL language and consists of the following sets. Σ = {Σ n } n<ω

The non-logical symbols determine a specific F OL language and consists of the following sets. Σ = {Σ n } n<ω 1 Preliminaries In this chapter we first give a summary of the basic notations, terminology and results which will be used in this thesis. The treatment here is reduced to a list of definitions. For the

More information

Introduction to Logic in Computer Science: Autumn 2007

Introduction to Logic in Computer Science: Autumn 2007 Introduction to Logic in Computer Science: Autumn 2007 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Tableaux for First-order Logic The next part of

More information

Syntax of FOL. Introduction to Logic in Computer Science: Autumn Tableaux for First-order Logic. Syntax of FOL (2)

Syntax of FOL. Introduction to Logic in Computer Science: Autumn Tableaux for First-order Logic. Syntax of FOL (2) Syntax of FOL Introduction to Logic in Computer Science: Autumn 2007 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam The syntax of a language defines the way in which

More information

Logic as a Tool Chapter 4: Deductive Reasoning in First-Order Logic 4.4 Prenex normal form. Skolemization. Clausal form.

Logic as a Tool Chapter 4: Deductive Reasoning in First-Order Logic 4.4 Prenex normal form. Skolemization. Clausal form. Logic as a Tool Chapter 4: Deductive Reasoning in First-Order Logic 4.4 Prenex normal form. Skolemization. Clausal form. Valentin Stockholm University October 2016 Revision: CNF and DNF of propositional

More information

COMP4418: Knowledge Representation and Reasoning First-Order Logic

COMP4418: Knowledge Representation and Reasoning First-Order Logic COMP4418: Knowledge Representation and Reasoning First-Order Logic Maurice Pagnucco School of Computer Science and Engineering University of New South Wales NSW 2052, AUSTRALIA morri@cse.unsw.edu.au COMP4418

More information

4 Predicate / First Order Logic

4 Predicate / First Order Logic 4 Predicate / First Order Logic 4.1 Syntax 4.2 Substitutions 4.3 Semantics 4.4 Equivalence and Normal Forms 4.5 Unification 4.6 Proof Procedures 4.7 Implementation of Proof Procedures 4.8 Properties First

More information

INF3170 / INF4171 Notes on Resolution

INF3170 / INF4171 Notes on Resolution INF3170 / INF4171 Notes on Resolution Andreas Nakkerud Autumn 2015 1 Introduction This is a short description of the Resolution calculus for propositional logic, and for first order logic. We will only

More information

Contents. basic algebra. Learning outcomes. Time allocation. 1. Mathematical notation and symbols. 2. Indices. 3. Simplification and factorisation

Contents. basic algebra. Learning outcomes. Time allocation. 1. Mathematical notation and symbols. 2. Indices. 3. Simplification and factorisation basic algebra Contents. Mathematical notation and symbols 2. Indices 3. Simplification and factorisation 4. Arithmetic of algebraic fractions 5. Formulae and transposition Learning outcomes In this workbook

More information

06 From Propositional to Predicate Logic

06 From Propositional to Predicate Logic Martin Henz February 19, 2014 Generated on Wednesday 19 th February, 2014, 09:48 1 Syntax of Predicate Logic 2 3 4 5 6 Need for Richer Language Predicates Variables Functions 1 Syntax of Predicate Logic

More information

Syntax. Notation Throughout, and when not otherwise said, we assume a vocabulary V = C F P.

Syntax. Notation Throughout, and when not otherwise said, we assume a vocabulary V = C F P. First-Order Logic Syntax The alphabet of a first-order language is organised into the following categories. Logical connectives:,,,,, and. Auxiliary symbols:.,,, ( and ). Variables: we assume a countable

More information

Example. Lemma. Proof Sketch. 1 let A be a formula that expresses that node t is reachable from s

Example. Lemma. Proof Sketch. 1 let A be a formula that expresses that node t is reachable from s Summary Summary Last Lecture Computational Logic Π 1 Γ, x : σ M : τ Γ λxm : σ τ Γ (λxm)n : τ Π 2 Γ N : τ = Π 1 [x\π 2 ] Γ M[x := N] Georg Moser Institute of Computer Science @ UIBK Winter 2012 the proof

More information

Overview. CS389L: Automated Logical Reasoning. Lecture 7: Validity Proofs and Properties of FOL. Motivation for semantic argument method

Overview. CS389L: Automated Logical Reasoning. Lecture 7: Validity Proofs and Properties of FOL. Motivation for semantic argument method Overview CS389L: Automated Logical Reasoning Lecture 7: Validity Proofs and Properties of FOL Agenda for today: Semantic argument method for proving FOL validity Işıl Dillig Important properties of FOL

More information

First-Order Logic. 1 Syntax. Domain of Discourse. FO Vocabulary. Terms

First-Order Logic. 1 Syntax. Domain of Discourse. FO Vocabulary. Terms First-Order Logic 1 Syntax Domain of Discourse The domain of discourse for first order logic is FO structures or models. A FO structure contains Relations Functions Constants (functions of arity 0) FO

More information

Predicate Calculus. Formal Methods in Verification of Computer Systems Jeremy Johnson

Predicate Calculus. Formal Methods in Verification of Computer Systems Jeremy Johnson Predicate Calculus Formal Methods in Verification of Computer Systems Jeremy Johnson Outline 1. Motivation 1. Variables, quantifiers and predicates 2. Syntax 1. Terms and formulas 2. Quantifiers, scope

More information

Resolution for Predicate Logic

Resolution for Predicate Logic Resolution for Predicate Logic The connection between general satisfiability and Herbrand satisfiability provides the basis for a refutational approach to first-order theorem proving. Validity of a first-order

More information

Resolution for Predicate Logic

Resolution for Predicate Logic Logic and Proof Hilary 2016 James Worrell Resolution for Predicate Logic A serious drawback of the ground resolution procedure is that it requires looking ahead to predict which ground instances of clauses

More information

Computational Logic Automated Deduction Fundamentals

Computational Logic Automated Deduction Fundamentals Computational Logic Automated Deduction Fundamentals 1 Elements of First-Order Predicate Logic First Order Language: An alphabet consists of the following classes of symbols: 1. variables denoted by X,

More information

Predicate Calculus. CS 270 Math Foundations of Computer Science Jeremy Johnson

Predicate Calculus. CS 270 Math Foundations of Computer Science Jeremy Johnson Predicate Calculus CS 270 Math Foundations of Computer Science Jeremy Johnson Presentation uses material from Huth and Ryan, Logic in Computer Science: Modelling and Reasoning about Systems, 2nd Edition

More information

AP Physics 1 Summer Assignment. Directions: Find the following. Final answers should be in scientific notation. 2.)

AP Physics 1 Summer Assignment. Directions: Find the following. Final answers should be in scientific notation. 2.) AP Physics 1 Summer Assignment DUE THE FOURTH DAY OF SCHOOL- 2018 Purpose: The purpose of this packet is to make sure that we all have a common starting point and understanding of some of the basic concepts

More information

Propositional and Predicate Logic - V

Propositional and Predicate Logic - V Propositional and Predicate Logic - V Petr Gregor KTIML MFF UK WS 2016/2017 Petr Gregor (KTIML MFF UK) Propositional and Predicate Logic - V WS 2016/2017 1 / 21 Formal proof systems Hilbert s calculus

More information

Introduction to Logic in Computer Science: Autumn 2006

Introduction to Logic in Computer Science: Autumn 2006 Introduction to Logic in Computer Science: Autumn 2006 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Plan for Today Today s class will be an introduction

More information

First-Order Logic First-Order Theories. Roopsha Samanta. Partly based on slides by Aaron Bradley and Isil Dillig

First-Order Logic First-Order Theories. Roopsha Samanta. Partly based on slides by Aaron Bradley and Isil Dillig First-Order Logic First-Order Theories Roopsha Samanta Partly based on slides by Aaron Bradley and Isil Dillig Roadmap Review: propositional logic Syntax and semantics of first-order logic (FOL) Semantic

More information

Automated Reasoning in First-Order Logic

Automated Reasoning in First-Order Logic Automated Reasoning in First-Order Logic Peter Baumgartner http://users.cecs.anu.edu.au/~baumgart/ 7/11/2011 Automated Reasoning in First-Order Logic... First-Order Logic Can express (mathematical) structures,

More information

Propositional Logic: Models and Proofs

Propositional Logic: Models and Proofs Propositional Logic: Models and Proofs C. R. Ramakrishnan CSE 505 1 Syntax 2 Model Theory 3 Proof Theory and Resolution Compiled at 11:51 on 2016/11/02 Computing with Logic Propositional Logic CSE 505

More information

README - Syntax. Skolemization. Skolemization - Example 1. Skolemization - Example 1. Skolemization - Example 1. Skolemization - Example 2

README - Syntax. Skolemization. Skolemization - Example 1. Skolemization - Example 1. Skolemization - Example 1. Skolemization - Example 2 README - Syntax Skolemization Logical implication is commonly included in the syntax of first-order and propositional logical languages. The symbol used to denote logical implication differs from language

More information

First-Order Theorem Proving and Vampire. Laura Kovács (Chalmers University of Technology) Andrei Voronkov (The University of Manchester)

First-Order Theorem Proving and Vampire. Laura Kovács (Chalmers University of Technology) Andrei Voronkov (The University of Manchester) First-Order Theorem Proving and Vampire Laura Kovács (Chalmers University of Technology) Andrei Voronkov (The University of Manchester) Outline Introduction First-Order Logic and TPTP Inference Systems

More information

Propositional Logic Language

Propositional Logic Language Propositional Logic Language A logic consists of: an alphabet A, a language L, i.e., a set of formulas, and a binary relation = between a set of formulas and a formula. An alphabet A consists of a finite

More information

Convert to clause form:

Convert to clause form: Convert to clause form: Convert the following statement to clause form: x[b(x) ( y [ Q(x,y) P(y) ] y [ Q(x,y) Q(y,x) ] y [ B(y) E(x,y)] ) ] 1- Eliminate the implication ( ) E1 E2 = E1 E2 x[ B(x) ( y [

More information

Part 2: First-Order Logic

Part 2: First-Order Logic Part 2: First-Order Logic First-order logic formalizes fundamental mathematical concepts is expressive (Turing-complete) is not too expressive (e. g. not axiomatizable: natural numbers, uncountable sets)

More information

First-Order Theorem Proving and Vampire

First-Order Theorem Proving and Vampire First-Order Theorem Proving and Vampire Laura Kovács 1,2 and Martin Suda 2 1 TU Wien 2 Chalmers Outline Introduction First-Order Logic and TPTP Inference Systems Saturation Algorithms Redundancy Elimination

More information

1 Introduction to Predicate Resolution

1 Introduction to Predicate Resolution 1 Introduction to Predicate Resolution The resolution proof system for Predicate Logic operates, as in propositional case on sets of clauses and uses a resolution rule as the only rule of inference. The

More information

CogSysI Lecture 8: Automated Theorem Proving

CogSysI Lecture 8: Automated Theorem Proving CogSysI Lecture 8: Automated Theorem Proving Intelligent Agents WS 2004/2005 Part II: Inference and Learning Automated Theorem Proving CogSysI Lecture 8: Automated Theorem Proving p. 200 Remember......

More information

CSE 1400 Applied Discrete Mathematics Definitions

CSE 1400 Applied Discrete Mathematics Definitions CSE 1400 Applied Discrete Mathematics Definitions Department of Computer Sciences College of Engineering Florida Tech Fall 2011 Arithmetic 1 Alphabets, Strings, Languages, & Words 2 Number Systems 3 Machine

More information

Quantum Mechanics for Scientists and Engineers. David Miller

Quantum Mechanics for Scientists and Engineers. David Miller Quantum Mechanics for Scientists and Engineers David Miller Background mathematics Basic mathematical symbols Elementary arithmetic symbols Equals Addition or plus 3 5 Subtraction, minus or less 3 or Multiplication

More information

KE/Tableaux. What is it for?

KE/Tableaux. What is it for? CS3UR: utomated Reasoning 2002 The term Tableaux refers to a family of deduction methods for different logics. We start by introducing one of them: non-free-variable KE for classical FOL What is it for?

More information

03 Review of First-Order Logic

03 Review of First-Order Logic CAS 734 Winter 2014 03 Review of First-Order Logic William M. Farmer Department of Computing and Software McMaster University 18 January 2014 What is First-Order Logic? First-order logic is the study of

More information

Mathematical Logics. 12. Soundness and Completeness of tableaux reasoning in first order logic. Luciano Serafini

Mathematical Logics. 12. Soundness and Completeness of tableaux reasoning in first order logic. Luciano Serafini 12. Soundness and Completeness of tableaux reasoning in first order logic Fondazione Bruno Kessler, Trento, Italy November 14, 2013 Example of tableaux Example Consider the following formulas: (a) xyz(p(x,

More information

Some Rewriting Systems as a Background of Proving Methods

Some Rewriting Systems as a Background of Proving Methods Some Rewriting Systems as a Background of Proving Methods Katalin Pásztor Varga Department of General Computer Science Eötvös Loránd University e-mail: pkata@ludens.elte.hu Magda Várterész Institute of

More information

Resolution: Motivation

Resolution: Motivation Resolution: Motivation Steps in inferencing (e.g., forward-chaining) 1. Define a set of inference rules 2. Define a set of axioms 3. Repeatedly choose one inference rule & one or more axioms (or premices)

More information

Two hours. Examination definition sheet is available at the back of the examination. UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE

Two hours. Examination definition sheet is available at the back of the examination. UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE COMP 60332 Two hours Examination definition sheet is available at the back of the examination. UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE Automated Reasoning and Verification Date: Wednesday 30th

More information

Logic and Computation

Logic and Computation Logic and Computation CS245 Dr. Borzoo Bonakdarpour University of Waterloo (Fall 2012) Resolution in First-order Predicate Logic Logic and Computation p. 1/38 Agenda Resolution in Propositional Logic Prenex

More information

Normal Forms for First-Order Logic

Normal Forms for First-Order Logic Logic and Proof Hilary 2016 James Worrell Normal Forms for First-Order Logic In this lecture we show how to transform an arbitrary formula of first-order logic to an equisatisfiable formula in Skolem form.

More information

Computational Logic. Davide Martinenghi. Spring Free University of Bozen-Bolzano. Computational Logic Davide Martinenghi (1/30)

Computational Logic. Davide Martinenghi. Spring Free University of Bozen-Bolzano. Computational Logic Davide Martinenghi (1/30) Computational Logic Davide Martinenghi Free University of Bozen-Bolzano Spring 2010 Computational Logic Davide Martinenghi (1/30) Propositional Logic - sequent calculus To overcome the problems of natural

More information

Inference in first-order logic

Inference in first-order logic CS 2710 Foundations of AI Lecture 15 Inference in first-order logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square Logical inference in FOL Logical inference problem: Given a knowledge base KB

More information

Resolution for mixed Post logic

Resolution for mixed Post logic Resolution for mixed Post logic Vladimir Komendantsky Institute of Philosophy of Russian Academy of Science, Volkhonka 14, 119992 Moscow, Russia vycom@pochtamt.ru Abstract. In this paper we present a resolution

More information

Advanced Topics in LP and FP

Advanced Topics in LP and FP Lecture 1: Prolog and Summary of this lecture 1 Introduction to Prolog 2 3 Truth value evaluation 4 Prolog Logic programming language Introduction to Prolog Introduced in the 1970s Program = collection

More information

Automated Reasoning (in First Order Logic) Andrei Voronkov. University of Manchester

Automated Reasoning (in First Order Logic) Andrei Voronkov. University of Manchester Automated Reasoning (in First Order Logic) Andrei Voronkov University of Manchester 1 Overview Automated reasoning in first-order logic. Completeness. Theory of Resolution and Redundancy. From Theory to

More information

COMP9414: Artificial Intelligence Propositional Logic: Automated Reasoning

COMP9414: Artificial Intelligence Propositional Logic: Automated Reasoning COMP9414, Monday 26 March, 2012 Propositional Logic 2 COMP9414: Artificial Intelligence Propositional Logic: Automated Reasoning Overview Proof systems (including soundness and completeness) Normal Forms

More information

First-Order Predicate Logic. Basics

First-Order Predicate Logic. Basics First-Order Predicate Logic Basics 1 Syntax of predicate logic: terms A variable is a symbol of the form x i where i = 1, 2, 3.... A function symbol is of the form fi k where i = 1, 2, 3... und k = 0,

More information

Inference in first-order logic

Inference in first-order logic CS 57 Introduction to AI Lecture 5 Inference in first-order logic Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square Logical inference in FOL Logical inference problem: Given a knowledge base KB (a

More information

Language of Propositional Logic

Language of Propositional Logic Logic A logic has: 1. An alphabet that contains all the symbols of the language of the logic. 2. A syntax giving the rules that define the well formed expressions of the language of the logic (often called

More information

07 Equational Logic and Algebraic Reasoning

07 Equational Logic and Algebraic Reasoning CAS 701 Fall 2004 07 Equational Logic and Algebraic Reasoning Instructor: W. M. Farmer Revised: 17 November 2004 1 What is Equational Logic? Equational logic is first-order logic restricted to languages

More information

Superposition for Fixed Domains. Matthias Horbach and Christoph Weidenbach

Superposition for Fixed Domains. Matthias Horbach and Christoph Weidenbach Superposition for Fixed Domains Matthias Horbach and Christoph Weidenbach MPI I 2009 RG1 005 Oct. 2009 Authors Addresses Matthias Horbach and Christoph Weidenbach Max Planck Institute for Informatics Campus

More information

PROOFS IN PREDICATE LOGIC AND COMPLETENESS; WHAT DECIDABILITY MEANS HUTH AND RYAN 2.3, SUPPLEMENTARY NOTES 2

PROOFS IN PREDICATE LOGIC AND COMPLETENESS; WHAT DECIDABILITY MEANS HUTH AND RYAN 2.3, SUPPLEMENTARY NOTES 2 PROOFS IN PREDICATE LOGIC AND COMPLETENESS; WHAT DECIDABILITY MEANS HUTH AND RYAN 2.3, SUPPLEMENTARY NOTES 2 Neil D. Jones DIKU 2005 12 September, 2005 Some slides today new, some based on logic 2004 (Nils

More information

1 FUNDAMENTALS OF LOGIC NO.10 HERBRAND THEOREM Tatsuya Hagino hagino@sfc.keio.ac.jp lecture URL https://vu5.sfc.keio.ac.jp/slide/ 2 So Far Propositional Logic Logical connectives (,,, ) Truth table Tautology

More information

CTL-RP: A Computational Tree Logic Resolution Prover

CTL-RP: A Computational Tree Logic Resolution Prover 1 -RP: A Computational Tree Logic Resolution Prover Lan Zhang a,, Ullrich Hustadt a and Clare Dixon a a Department of Computer Science, University of Liverpool Liverpool, L69 3BX, UK E-mail: {Lan.Zhang,

More information

General Physics. Prefixes. Aims: The Greek Alphabet Units. Provided Data

General Physics. Prefixes. Aims: The Greek Alphabet Units. Provided Data General Physics Aims: The Greek Alphabet Units Prefixes Provided Data Name Upper Case Lower Case The Greek Alphabet When writing equations and defining terms, letters from the Greek alphabet are often

More information

Propositional Resolution

Propositional Resolution Artificial Intelligence Propositional Resolution Marco Piastra Propositional Resolution 1] Deductive systems and automation Is problem decidible? A deductive system a la Hilbert (i.e. derivation using

More information

LOGIC. Mathematics. Computer Science. Stanley N. Burris

LOGIC. Mathematics. Computer Science. Stanley N. Burris LOGIC for Mathematics and Computer Science Stanley N. Burris Department of Pure Mathematics University of Waterloo Prentice Hall Upper Saddle River, New Jersey 07458 Contents Preface The Flow of Topics

More information

Foundations of Artificial Intelligence

Foundations of Artificial Intelligence Foundations of Artificial Intelligence 9. Predicate Logic Syntax and Semantics, Normal Forms, Herbrand Expansion, Resolution Wolfram Burgard, Bernhard Nebel and Martin Riedmiller Albert-Ludwigs-Universität

More information

Logic: First Order Logic

Logic: First Order Logic Logic: First Order Logic Raffaella Bernardi bernardi@inf.unibz.it P.zza Domenicani 3, Room 2.28 Faculty of Computer Science, Free University of Bolzano-Bozen http://www.inf.unibz.it/~bernardi/courses/logic06

More information

Cooperation of Background Reasoners in Theory Reasoning by Residue Sharing

Cooperation of Background Reasoners in Theory Reasoning by Residue Sharing Cooperation of Background Reasoners in Theory Reasoning by Residue Sharing Cesare Tinelli tinelli@cs.uiowa.edu Department of Computer Science The University of Iowa Report No. 02-03 May 2002 i Cooperation

More information

Two Mathematical Constants

Two Mathematical Constants 1 Two Mathematical Constants Two of the most important constants in the world of mathematics are π (pi) and e (Euler s number). π = 3.14159265358979323846264338327950288419716939937510... e = 2.71828182845904523536028747135266249775724709369995...

More information

CSC384: Intro to Artificial Intelligence Knowledge Representation II. Required Readings: 9.1, 9.2, and 9.5 Announcements:

CSC384: Intro to Artificial Intelligence Knowledge Representation II. Required Readings: 9.1, 9.2, and 9.5 Announcements: CSC384: Intro to Artificial Intelligence Knowledge Representation II Required Readings: 9.1, 9.2, and 9.5 Announcements: 1 Models Examples. Environment A Language (Syntax) Constants: a,b,c,e Functions:

More information

Computational Logic. Recall of First-Order Logic. Damiano Zanardini

Computational Logic. Recall of First-Order Logic. Damiano Zanardini Computational Logic Recall of First-Order Logic Damiano Zanardini UPM European Master in Computational Logic (EMCL) School of Computer Science Technical University of Madrid damiano@fi.upm.es Academic

More information

First Order Logic (FOL) 1 znj/dm2017

First Order Logic (FOL) 1   znj/dm2017 First Order Logic (FOL) 1 http://lcs.ios.ac.cn/ znj/dm2017 Naijun Zhan March 19, 2017 1 Special thanks to Profs Hanpin Wang (PKU) and Lijun Zhang (ISCAS) for their courtesy of the slides on this course.

More information

Computational Logic. Davide Martinenghi. Spring Free University of Bozen-Bolzano. Computational Logic Davide Martinenghi (1/26)

Computational Logic. Davide Martinenghi. Spring Free University of Bozen-Bolzano. Computational Logic Davide Martinenghi (1/26) Computational Logic Davide Martinenghi Free University of Bozen-Bolzano Spring 2010 Computational Logic Davide Martinenghi (1/26) Propositional Logic - algorithms Complete calculi for deciding logical

More information

Closed Book Examination. Two hours UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE. M.Sc. in Advanced Computer Science

Closed Book Examination. Two hours UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE. M.Sc. in Advanced Computer Science Closed Book Examination COMP60121 Appendix: definition sheet (3 pages) Two hours UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE M.Sc. in Advanced Computer Science Automated Reasoning Tuesday 27 th

More information

or R n +, R n ++ etc.;

or R n +, R n ++ etc.; Mathematical Prerequisites for Econ 7005, Microeconomic Theory I, and Econ 7007, Macroeconomic Theory I, at the University of Utah by Gabriel A Lozada The contents of Sections 1 6 below are required for

More information

Negative Hyper-Resolution as Procedural Semantics of Disjunctive Logic Programs

Negative Hyper-Resolution as Procedural Semantics of Disjunctive Logic Programs Negative Hyper-Resolution as Procedural Semantics of Disjunctive Logic Programs Linh Anh Nguyen Institute of Informatics, University of Warsaw ul. Banacha 2, 02-097 Warsaw, Poland nguyen@mimuw.edu.pl Abstract.

More information

Comp487/587 - Boolean Formulas

Comp487/587 - Boolean Formulas Comp487/587 - Boolean Formulas 1 Logic and SAT 1.1 What is a Boolean Formula Logic is a way through which we can analyze and reason about simple or complicated events. In particular, we are interested

More information

3. The Logic of Quantified Statements Summary. Aaron Tan August 2017

3. The Logic of Quantified Statements Summary. Aaron Tan August 2017 3. The Logic of Quantified Statements Summary Aaron Tan 28 31 August 2017 1 3. The Logic of Quantified Statements 3.1 Predicates and Quantified Statements I Predicate; domain; truth set Universal quantifier,

More information

Part 1: Propositional Logic

Part 1: Propositional Logic Part 1: Propositional Logic Literature (also for first-order logic) Schöning: Logik für Informatiker, Spektrum Fitting: First-Order Logic and Automated Theorem Proving, Springer 1 Last time 1.1 Syntax

More information

Logic. Knowledge Representation & Reasoning Mechanisms. Logic. Propositional Logic Predicate Logic (predicate Calculus) Automated Reasoning

Logic. Knowledge Representation & Reasoning Mechanisms. Logic. Propositional Logic Predicate Logic (predicate Calculus) Automated Reasoning Logic Knowledge Representation & Reasoning Mechanisms Logic Logic as KR Propositional Logic Predicate Logic (predicate Calculus) Automated Reasoning Logical inferences Resolution and Theorem-proving Logic

More information

Deductive Systems. Lecture - 3

Deductive Systems. Lecture - 3 Deductive Systems Lecture - 3 Axiomatic System Axiomatic System (AS) for PL AS is based on the set of only three axioms and one rule of deduction. It is minimal in structure but as powerful as the truth

More information

ArgoCaLyPso SAT-Inspired Coherent Logic Prover

ArgoCaLyPso SAT-Inspired Coherent Logic Prover ArgoCaLyPso SAT-Inspired Coherent Logic Prover Mladen Nikolić and Predrag Janičić Automated Reasoning GrOup (ARGO) Faculty of Mathematics University of, February, 2011. Motivation Coherent logic (CL) (also

More information

Between proof theory and model theory Three traditions in logic: Syntactic (formal deduction)

Between proof theory and model theory Three traditions in logic: Syntactic (formal deduction) Overview Between proof theory and model theory Three traditions in logic: Syntactic (formal deduction) Jeremy Avigad Department of Philosophy Carnegie Mellon University avigad@cmu.edu http://andrew.cmu.edu/

More information

Outline. Formale Methoden der Informatik First-Order Logic for Forgetters. Why PL1? Why PL1? Cont d. Motivation

Outline. Formale Methoden der Informatik First-Order Logic for Forgetters. Why PL1? Why PL1? Cont d. Motivation Outline Formale Methoden der Informatik First-Order Logic for Forgetters Uwe Egly Vienna University of Technology Institute of Information Systems Knowledge-Based Systems Group Motivation Syntax of PL1

More information

GS03/4023: Validation and Verification Predicate Logic Jonathan P. Bowen Anthony Hall

GS03/4023: Validation and Verification Predicate Logic Jonathan P. Bowen   Anthony Hall GS03/4023: Validation and Verification Predicate Logic Jonathan P. Bowen www.cs.ucl.ac.uk/staff/j.bowen/gs03 Anthony Hall GS03 W1 L3 Predicate Logic 12 January 2007 1 Overview The need for extra structure

More information

Automated Synthesis of Tableau Calculi

Automated Synthesis of Tableau Calculi Automated Synthesis of Tableau Calculi Renate A. Schmidt 1 and Dmitry Tishkovsky 1 School of Computer Science, The University of Manchester Abstract This paper presents a method for synthesising sound

More information

AAA615: Formal Methods. Lecture 2 First-Order Logic

AAA615: Formal Methods. Lecture 2 First-Order Logic AAA615: Formal Methods Lecture 2 First-Order Logic Hakjoo Oh 2017 Fall Hakjoo Oh AAA615 2017 Fall, Lecture 2 September 24, 2017 1 / 29 First-Order Logic An extension of propositional logic with predicates,

More information

Propositional and Predicate Logic - XIII

Propositional and Predicate Logic - XIII Propositional and Predicate Logic - XIII Petr Gregor KTIML MFF UK WS 2016/2017 Petr Gregor (KTIML MFF UK) Propositional and Predicate Logic - XIII WS 2016/2017 1 / 22 Undecidability Introduction Recursive

More information

Cooperation of Background Reasoners in Theory Reasoning by Residue Sharing

Cooperation of Background Reasoners in Theory Reasoning by Residue Sharing Cooperation of Background Reasoners in Theory Reasoning by Residue Sharing Cesare Tinelli (tinelli@cs.uiowa.edu) Department of Computer Science The University of Iowa Iowa City, IA, USA Abstract. We propose

More information

A Tableau Calculus for Minimal Modal Model Generation

A Tableau Calculus for Minimal Modal Model Generation M4M 2011 A Tableau Calculus for Minimal Modal Model Generation Fabio Papacchini 1 and Renate A. Schmidt 2 School of Computer Science, University of Manchester Abstract Model generation and minimal model

More information

Introductory Unit for A Level Physics

Introductory Unit for A Level Physics Introductory Unit for A Level Physics Name: 1 Physics Year 12 Induction Objectives: To give you the skills needed for the successful study of Physics at A level. To help you to identify areas in which

More information

NOTES ON PREDICATE CALCULUS AND LOGIC PROGRAMMING

NOTES ON PREDICATE CALCULUS AND LOGIC PROGRAMMING NOTES ON PREDICATE CALCULUS AND LOGIC PROGRAMMING REFERENCES [1] Apt, K. R.: Logic Programming, Handbook of Theoretical Computer Science (J. van Leeuwen, ed.), Elsevier Science Publishers B.V., 1990. [2]

More information

CMPS 217 Logic in Computer Science. Lecture #17

CMPS 217 Logic in Computer Science.   Lecture #17 CMPS 217 Logic in Computer Science https://courses.soe.ucsc.edu/courses/cmps217/spring13/01 Lecture #17 1 The Complexity of FO-Truth on a Structure Structure A Complexity of Th(A) Structure of the natural

More information

LOGIC PROPOSITIONAL REASONING

LOGIC PROPOSITIONAL REASONING LOGIC PROPOSITIONAL REASONING WS 2017/2018 (342.208) Armin Biere Martina Seidl biere@jku.at martina.seidl@jku.at Institute for Formal Models and Verification Johannes Kepler Universität Linz Version 2018.1

More information

Using Structural Equation Modeling to Conduct Confirmatory Factor Analysis

Using Structural Equation Modeling to Conduct Confirmatory Factor Analysis Using Structural Equation Modeling to Conduct Confirmatory Factor Analysis Advanced Statistics for Researchers Session 3 Dr. Chris Rakes Website: http://csrakes.yolasite.com Email: Rakes@umbc.edu Twitter:

More information

Strong AI vs. Weak AI Automated Reasoning

Strong AI vs. Weak AI Automated Reasoning Strong AI vs. Weak AI Automated Reasoning George F Luger ARTIFICIAL INTELLIGENCE 6th edition Structures and Strategies for Complex Problem Solving Artificial intelligence can be classified into two categories:

More information