SlideShare une entreprise Scribd logo
1  sur  31
Télécharger pour lire hors ligne
Bounded Arithmetic in Free Logic

         Yoriyuki Yamagata
         CTFM, 2013/02/20
Buss’s theories         𝑆2𝑖



  –a#b=2     𝑎 ⋅|𝑏|
• Language of Peano Arithmetic + “#”

• BASIC axioms

                   𝑥
            𝐴          , Γ → Δ, 𝐴(𝑥)
                  2
• PIND


              𝐴 0 , Γ → Δ, 𝐴(𝑡)
where 𝐴 𝑥 ∈ Σ 𝑖𝑏 , i.e. has 𝑖-alternations of
bounded quantifiers ∀𝑥 ≤ 𝑡, ∃𝑥 ≤ 𝑡.
PH and Buss’s theories              𝑆2𝑖

               𝑆2 = 𝑆2 = 𝑆2 = …
                1    2    3



            𝑃 = □(𝑁𝑁) = □(Σ2 ) = …
                             𝑝
                    Implies



We can approach (non) collapse of PH from
(non) collapse of hierarchy of Buss’s theories

                           (PH = Polynomial Hierarchy)
Our approach
• Separate 𝑆2𝑖 by Gödel incompleteness theorem
• Use analogy of separation of 𝐼Σ 𝑖
Separation of 𝐼Σ 𝑖


 𝐼Σ3 ⊢ Con(IΣ2 )
 …
 𝐼Σ2 ⊢ Con IΣ2
⊆


  𝐼Σ1
⊆
Consistency proof inside                 𝑆2𝑖

• Bounded Arithmetics generally are not

  – 𝑆2 does not prove consistency of Q (Paris, Wilkie)
  capable to prove consistency.

  – 𝑆2 does not prove bounded consistency of
    𝑆2 (Pudlák)
     1

  – 𝑆2 does not prove consistency the 𝐵 𝑖 𝑏 fragement
      𝑖

   of 𝑆2 (Buss and Ignjatović)
        −1
Buss and Ignjatović(1995)

 …𝑆2 ⊢ 𝐵3 − Con(𝑆2 )
   3    b        −1



  𝑆2
   2
       ⊢   𝐵2
            b
                −   Con(𝑆2 )
                         −1
⊆




  𝑆2
   1
       ⊢   𝐵1
            b
                −   Con(𝑆2 )
                         −1
⊆
Where…
• 𝐵 𝑖 𝑏 − 𝐶𝐶𝐶 𝑇
  – consistency of 𝐵 𝑖 𝑏 −proofs
  – 𝐵 𝑖 𝑏 −proofs : the proofs by 𝐵 𝑖 𝑏 -formule
  – 𝐵 𝑖 𝑏 :Σ0𝑏 (Σ 𝑖𝑏 )… Formulas generated from Σ 𝑖𝑏 by
   Boolean connectives and sharply bounded

• 𝑆2
   −1
   quantifiers.


  – Induction free fragment of 𝑆2𝑖
If…
 𝑆2    ⊢   𝐵i   − Con    𝑆2    ,j > i
   𝑗        b             −1



Then, Buss’s hierarchy does not collapse.
Consistency proof of 𝑆2 inside 𝑆2𝑖
                           −1



Problem
• No truth definition, because
• No valuation of terms, because
  • The values of terms increase exponentially


In 𝑆2 world, terms do not have values a priori.
     𝑖
    • E.g. 2#2#2#2#2#...#2




• We introduce the predicate 𝐸 which signifies existence of
• Thus, we must prove the existence of values in proofs.

  values.
Our result(2012)

𝑆2
…5
     ⊢3−   Con(𝑆2
                −1
                     𝐸)

𝑆2
 4
     ⊢2−   Con(𝑆2
                −1
                     𝐸)
⊆




𝑆2
 3
     ⊢1−   Con(𝑆2
                −1
                     𝐸)
⊆
Where…
• 𝑖 − 𝐶𝐶𝐶 𝑇
  – consistency of 𝑖-normal proofs
  – 𝑖-normal proofs : the proofs by 𝑖-normal formulas
  – 𝑖-normal formulas: Formulas in the form:
  ∃𝑥1 ≤ 𝑡1 ∀𝑥2 ≤ 𝑡2 … 𝑄𝑥 𝑖 ≤ 𝑡 𝑖 𝑄𝑥 𝑖+1 ≤ 𝑡 𝑖+1 . 𝐴(… )
  Where 𝐴 is quantifier free
Where…
• 𝑆2 𝐸
   −1

  – Induction free fragment of 𝑆2 𝐸
                                 𝑖

  – have predicate 𝐸 which signifies existence of
    values
     • Such logic is called Free logic
𝑆2𝑖
                        𝐸(Language)

• =, ≤, 𝐸
Predicates


Function symbols
• Finite number of polynomial functions

Formulas

• 𝐴 ∨ 𝐵, 𝐴 ∧ 𝐵
• Atomic formula, negated atomic formula

• Bounded quantifiers
𝑆2𝑖
                        𝐸(Axioms)
•    𝐸-axioms
•   Equality axioms
•   Data axioms
•   Defining axioms
•   Auxiliary axioms
Idea behind axioms…

            → 𝑎= 𝑎

Because there is no guarantee of 𝐸𝐸
Thus, we add 𝐸𝐸 in the antecedent

           𝐸𝐸 → 𝑎 = 𝑎
E-axioms
• 𝐸𝐸 𝑎1 , … , 𝑎 𝑛 → 𝐸𝑎 𝑗
• 𝑎1 = 𝑎2 → 𝐸𝑎 𝑗
• 𝑎1 ≠ 𝑎2 → 𝐸𝑎 𝑗
• 𝑎1 ≤ 𝑎2 → 𝐸𝑎 𝑗
• ¬𝑎1 ≤ 𝑎2 → 𝐸𝑎 𝑗
Equality axioms
• 𝐸𝐸 → 𝑎 = 𝑎
• 𝐸𝐸 ⃗ , ⃗ = 𝑏 → 𝑓 ⃗ = 𝑓 𝑏
     𝑎 𝑎           𝑎
Data axioms
• → 𝐸𝐸
• 𝐸𝐸 → 𝐸𝑠0 𝑎
• 𝐸𝐸 → 𝐸𝑠1 𝑎
Defining axioms
 𝑓 𝑢 𝑎1 , 𝑎2 , … , 𝑎 𝑛 = 𝑡(𝑎1 , … , 𝑎 𝑛 )

                           𝑢 𝑎 = 0, 𝑎, 𝑠0 𝑎, 𝑠1 𝑎



  𝐸𝑎1 , … , 𝐸𝑎 𝑛 , 𝐸𝐸 𝑎1 , … , 𝑎 𝑛 →
𝑓 𝑢 𝑎1 , 𝑎2 , … , 𝑎 𝑛 = 𝑡(𝑎1 , … , 𝑎 𝑛 )
Auxiliary axioms

      𝑎 = 𝑏 ⊃ 𝑎#𝑐 = 𝑏#𝑐


𝐸𝐸#𝑐, 𝐸𝐸#𝑐, 𝑎 = |𝑏| → 𝑎#𝑐 = 𝑏#𝑐
PIND-rule



where 𝐴 is an Σ 𝑖𝑏 -formulas
Bootstrapping     𝑆2𝑖
                                           𝐸
I.     𝑆2 𝐸 ⊢ Tot(𝑓) for any 𝑓, 𝑖 ≥ 0
         𝑖

II.    𝑆2 𝐸 ⊢ BASIC∗ , equality axioms
         𝑖                                     ∗

III.   𝑆2 𝐸 ⊢ predicate logic
         𝑖                       ∗

IV.    𝑆2𝑖
             𝐸⊢   Σ𝑖𝑏
                        −PIND∗
Theorem (Consistency)



 𝑆2𝑖+2
         ⊢i−   Con(𝑆2
                    −1
                         𝐸)
Valuation trees
ρ-valuation tree bounded by 19
                       ρ(a)=2, ρ(b)=3
     a=2

   a#a=16              b=3



     𝑣    𝑎#𝑎 + 𝑏 , 𝜌 ↓19 19
            a#a+b=19

         𝑣 𝑡 , 𝜌 ↓ 𝑢 𝑐 is Σ1𝑏
Bounded truth definition (1)
• 𝑇 𝑢, 𝑡1 = 𝑡2 , 𝜌 ⇔def
     ∃𝑐 ≤ 𝑢, 𝑣 𝑡1 , 𝜌 ↓ 𝑢 𝑐 ∧ 𝑣 𝑡1 , 𝜌 ↓ 𝑢 𝑐
• 𝑇 𝑢, 𝜙1 ∧ 𝜙2 , 𝜌 ⇔def
     𝑇 𝑢, 𝜙1 , 𝜌 ∧ 𝑇 𝑢, 𝜙2 , 𝜌
• 𝑇 𝑢, 𝜙1 ∨ 𝜙2 , 𝜌 ⇔def
     𝑇 𝑢, 𝜙1 , 𝜌 ∨ 𝑇 𝑢, 𝜙2 , 𝜌
Bounded truth definition (2)
• 𝑇 𝑢, ∃𝑥 ≤ 𝑡, 𝜙(𝑥) , 𝜌 ⇔def
          ∃𝑐 ≤ 𝑢, 𝑣 𝑡 , 𝜌 ↓ 𝑢 𝑐 ∧
             ∃𝑑 ≤ 𝑐, 𝑇 𝑢, 𝜙 𝑥 , 𝜌 𝑥 ↦ 𝑑
• 𝑇 𝑢, ∀𝑥 ≤ 𝑡, 𝜙(𝑥) , 𝜌 ⇔def
          ∃𝑐 ≤ 𝑢, 𝑣 𝑡 , 𝜌 ↓ 𝑢 𝑐 ∧
             ∀𝑑 ≤ 𝑐, 𝑇(𝑢, 𝜙 𝑥 , 𝜌[𝑥 ↦ 𝑑])

           Remark: If 𝜙 is Σ 𝑖𝑏 , 𝑇 𝑢, 𝜙   is Σ 𝑖+1
                                                 𝑏
induction hypothesis
 𝑢: enough large integer
𝑟: node of a proof of 0=1
Γ 𝑟 → Δ 𝑟 : the sequent of node 𝑟
 𝜌: assignment 𝜌 𝑎 ≤ 𝑢

∀𝑢′ ≤ 𝑢 ⊖ 𝑟, { ∀𝐴 ∈ Γ 𝑟 𝑇 𝑢′ , 𝐴 , 𝜌 ⊃
                  [∃𝐵 ∈ Δr , 𝑇(𝑢′ ⊕ 𝑟, 𝐵 , 𝜌)]}
Conjecture
    𝑆2
     −1
          𝐸 is weak enough
    – 𝑆2 can prove 𝑖-consistency of 𝑆2 𝐸
•
        𝑖+2                          −1

• While 𝑆2 𝐸 is strong enough
         −1

    – 𝑆2 𝐸 can interpret 𝑆2
        𝑖                  𝑖



    𝑆2 𝐸 is a good candidate to separate 𝑆2 and 𝑆2 .
• Conjecture
     −1                                    𝑖      𝑖+2
Future works

                𝑆2 ⊢ 𝑖−Con(𝑆2 𝐸)?
                  𝑖         −1
• Long-term goal


  – Simplify 𝑆2 𝐸
• Short-term goal
               𝑖
Publications
• Bounded Arithmetic in Free Logic
  Logical Methods in Computer Science
  Volume 8, Issue 3, Aug. 10, 2012

Contenu connexe

Tendances

Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric SpaceFixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric Spaceinventionjournals
 
Relations and functions
Relations and functions Relations and functions
Relations and functions Seyid Kadher
 
Relations and functions
Relations and functions Relations and functions
Relations and functions Leslie Amoguis
 
Relations & functions
Relations & functionsRelations & functions
Relations & functionsindu thakur
 
Mkk1013 chapter 2.1
Mkk1013 chapter 2.1Mkk1013 chapter 2.1
Mkk1013 chapter 2.1ramlahmailok
 
Prerequisite for metric space
Prerequisite for metric spacePrerequisite for metric space
Prerequisite for metric spaceROHAN GAIKWAD
 
Group theory notes
Group theory notesGroup theory notes
Group theory notesmkumaresan
 
BCA_Semester-II-Discrete Mathematics_unit-i Group theory
BCA_Semester-II-Discrete Mathematics_unit-i Group theoryBCA_Semester-II-Discrete Mathematics_unit-i Group theory
BCA_Semester-II-Discrete Mathematics_unit-i Group theoryRai University
 
Conformal Boundary conditions
Conformal Boundary conditionsConformal Boundary conditions
Conformal Boundary conditionsHassaan Saleem
 

Tendances (18)

Relations in Discrete Math
Relations in Discrete MathRelations in Discrete Math
Relations in Discrete Math
 
MT102 Лекц 13
MT102 Лекц 13MT102 Лекц 13
MT102 Лекц 13
 
Dld 4
Dld 4Dld 4
Dld 4
 
MT102 Лекц 4
MT102 Лекц 4MT102 Лекц 4
MT102 Лекц 4
 
Group Theory
Group TheoryGroup Theory
Group Theory
 
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric SpaceFixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
Fixed Point Results for Weakly Compatible Mappings in Convex G-Metric Space
 
MT102 Лекц 5
MT102 Лекц 5MT102 Лекц 5
MT102 Лекц 5
 
Relations and functions
Relations and functions Relations and functions
Relations and functions
 
MT102 Лекц 15
MT102 Лекц 15MT102 Лекц 15
MT102 Лекц 15
 
Relations and functions
Relations and functions Relations and functions
Relations and functions
 
Relations & functions
Relations & functionsRelations & functions
Relations & functions
 
Mkk1013 chapter 2.1
Mkk1013 chapter 2.1Mkk1013 chapter 2.1
Mkk1013 chapter 2.1
 
Prerequisite for metric space
Prerequisite for metric spacePrerequisite for metric space
Prerequisite for metric space
 
4
44
4
 
Group theory notes
Group theory notesGroup theory notes
Group theory notes
 
BCA_Semester-II-Discrete Mathematics_unit-i Group theory
BCA_Semester-II-Discrete Mathematics_unit-i Group theoryBCA_Semester-II-Discrete Mathematics_unit-i Group theory
BCA_Semester-II-Discrete Mathematics_unit-i Group theory
 
Conformal Boundary conditions
Conformal Boundary conditionsConformal Boundary conditions
Conformal Boundary conditions
 
thesis
thesisthesis
thesis
 

En vedette

Camomile - OCaml用Unicodeライブラリ
Camomile - OCaml用UnicodeライブラリCamomile - OCaml用Unicodeライブラリ
Camomile - OCaml用UnicodeライブラリYamagata Yoriyuki
 
Consistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmeticConsistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmeticYamagata Yoriyuki
 
Camomile : A Unicode library for OCaml
Camomile : A Unicode library for OCamlCamomile : A Unicode library for OCaml
Camomile : A Unicode library for OCamlYamagata Yoriyuki
 
CSPによるコンカレントシステムの検証(1)
CSPによるコンカレントシステムの検証(1)CSPによるコンカレントシステムの検証(1)
CSPによるコンカレントシステムの検証(1)Yamagata Yoriyuki
 
Consistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmeticConsistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmeticYamagata Yoriyuki
 
Consistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmeticConsistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmeticYamagata Yoriyuki
 
Bounded arithmetic in free logic
Bounded arithmetic in free logicBounded arithmetic in free logic
Bounded arithmetic in free logicYamagata Yoriyuki
 
Scalaによるドメイン特化言語を使ったソフトウェアの動作解析
Scalaによるドメイン特化言語を使ったソフトウェアの動作解析Scalaによるドメイン特化言語を使ったソフトウェアの動作解析
Scalaによるドメイン特化言語を使ったソフトウェアの動作解析Yamagata Yoriyuki
 
ヴォイニッチ手稿と私
ヴォイニッチ手稿と私ヴォイニッチ手稿と私
ヴォイニッチ手稿と私Yamagata Yoriyuki
 
CSPによる並行システムの検証(2)
CSPによる並行システムの検証(2)CSPによる並行システムの検証(2)
CSPによる並行システムの検証(2)Yamagata Yoriyuki
 
Google 日本語入力 TechTalk 2010
Google 日本語入力 TechTalk 2010Google 日本語入力 TechTalk 2010
Google 日本語入力 TechTalk 2010Yamagata Yoriyuki
 
透明な真理観を巡って
透明な真理観を巡って透明な真理観を巡って
透明な真理観を巡ってShunsuke Yatabe
 
機械学習に取り組んでいる企業の紹介
機械学習に取り組んでいる企業の紹介機械学習に取り組んでいる企業の紹介
機械学習に取り組んでいる企業の紹介Kazuma Kadomae
 
CSPを用いたログ解析その他
CSPを用いたログ解析その他CSPを用いたログ解析その他
CSPを用いたログ解析その他Yamagata Yoriyuki
 

En vedette (20)

Translating STM to CSP
Translating STM to CSPTranslating STM to CSP
Translating STM to CSP
 
UML&FM 2012
UML&FM 2012UML&FM 2012
UML&FM 2012
 
Camomile - OCaml用Unicodeライブラリ
Camomile - OCaml用UnicodeライブラリCamomile - OCaml用Unicodeライブラリ
Camomile - OCaml用Unicodeライブラリ
 
Consistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmeticConsistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmetic
 
Camomile : A Unicode library for OCaml
Camomile : A Unicode library for OCamlCamomile : A Unicode library for OCaml
Camomile : A Unicode library for OCaml
 
CSPによるコンカレントシステムの検証(1)
CSPによるコンカレントシステムの検証(1)CSPによるコンカレントシステムの検証(1)
CSPによるコンカレントシステムの検証(1)
 
モデル検査紹介
モデル検査紹介モデル検査紹介
モデル検査紹介
 
OCamlとUnicode
OCamlとUnicodeOCamlとUnicode
OCamlとUnicode
 
Consistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmeticConsistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmetic
 
Consistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmeticConsistency proof of a feasible arithmetic inside a bounded arithmetic
Consistency proof of a feasible arithmetic inside a bounded arithmetic
 
CamomileでUnicode
CamomileでUnicodeCamomileでUnicode
CamomileでUnicode
 
Bounded arithmetic in free logic
Bounded arithmetic in free logicBounded arithmetic in free logic
Bounded arithmetic in free logic
 
Scalaによるドメイン特化言語を使ったソフトウェアの動作解析
Scalaによるドメイン特化言語を使ったソフトウェアの動作解析Scalaによるドメイン特化言語を使ったソフトウェアの動作解析
Scalaによるドメイン特化言語を使ったソフトウェアの動作解析
 
ヴォイニッチ手稿と私
ヴォイニッチ手稿と私ヴォイニッチ手稿と私
ヴォイニッチ手稿と私
 
CSPによる並行システムの検証(2)
CSPによる並行システムの検証(2)CSPによる並行システムの検証(2)
CSPによる並行システムの検証(2)
 
Google 日本語入力 TechTalk 2010
Google 日本語入力 TechTalk 2010Google 日本語入力 TechTalk 2010
Google 日本語入力 TechTalk 2010
 
透明な真理観を巡って
透明な真理観を巡って透明な真理観を巡って
透明な真理観を巡って
 
LT資料
LT資料LT資料
LT資料
 
機械学習に取り組んでいる企業の紹介
機械学習に取り組んでいる企業の紹介機械学習に取り組んでいる企業の紹介
機械学習に取り組んでいる企業の紹介
 
CSPを用いたログ解析その他
CSPを用いたログ解析その他CSPを用いたログ解析その他
CSPを用いたログ解析その他
 

Similaire à Bounded arithmetic in free logic

Regularisation & Auxiliary Information in OOD Detection
Regularisation & Auxiliary Information in OOD DetectionRegularisation & Auxiliary Information in OOD Detection
Regularisation & Auxiliary Information in OOD Detectionkirk68
 
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...mathsjournal
 
Quadratic form and functional optimization
Quadratic form and functional optimizationQuadratic form and functional optimization
Quadratic form and functional optimizationJunpei Tsuji
 
Lecture_9_LA_Review.pptx
Lecture_9_LA_Review.pptxLecture_9_LA_Review.pptx
Lecture_9_LA_Review.pptxSunny432360
 
Some Common Fixed Point Results for Expansive Mappings in a Cone Metric Space
Some Common Fixed Point Results for Expansive Mappings in a Cone Metric SpaceSome Common Fixed Point Results for Expansive Mappings in a Cone Metric Space
Some Common Fixed Point Results for Expansive Mappings in a Cone Metric SpaceIOSR Journals
 
Approximate Nearest Neighbour in Higher Dimensions
Approximate Nearest Neighbour in Higher DimensionsApproximate Nearest Neighbour in Higher Dimensions
Approximate Nearest Neighbour in Higher DimensionsShrey Verma
 
PR 113: The Perception Distortion Tradeoff
PR 113: The Perception Distortion TradeoffPR 113: The Perception Distortion Tradeoff
PR 113: The Perception Distortion TradeoffTaeoh Kim
 
Optimum Engineering Design - Day 2b. Classical Optimization methods
Optimum Engineering Design - Day 2b. Classical Optimization methodsOptimum Engineering Design - Day 2b. Classical Optimization methods
Optimum Engineering Design - Day 2b. Classical Optimization methodsSantiagoGarridoBulln
 
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix MappingDual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mappinginventionjournals
 
Matrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence SpacesMatrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence SpacesIOSR Journals
 
A direct method for estimating linear non-Gaussian acyclic models
A direct method for estimating linear non-Gaussian acyclic modelsA direct method for estimating linear non-Gaussian acyclic models
A direct method for estimating linear non-Gaussian acyclic modelsShiga University, RIKEN
 
20180831 riemannian representation learning
20180831 riemannian representation learning20180831 riemannian representation learning
20180831 riemannian representation learningsegwangkim
 
Discrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdfDiscrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdfMuhammadUmerIhtisham
 
A Non Local Boundary Value Problem with Integral Boundary Condition
A Non Local Boundary Value Problem with Integral Boundary ConditionA Non Local Boundary Value Problem with Integral Boundary Condition
A Non Local Boundary Value Problem with Integral Boundary ConditionIJMERJOURNAL
 
Calculus Review Session Brian Prest Duke University Nicholas School of the En...
Calculus Review Session Brian Prest Duke University Nicholas School of the En...Calculus Review Session Brian Prest Duke University Nicholas School of the En...
Calculus Review Session Brian Prest Duke University Nicholas School of the En...rofiho9697
 
Computer aided design
Computer aided designComputer aided design
Computer aided designAbhi23396
 
Differential Geometry for Machine Learning
Differential Geometry for Machine LearningDifferential Geometry for Machine Learning
Differential Geometry for Machine LearningSEMINARGROOT
 
Linear Combination of vectors, Span and dependency
Linear Combination of vectors, Span and dependencyLinear Combination of vectors, Span and dependency
Linear Combination of vectors, Span and dependencyLambitDontPosts
 
Generalised Statistical Convergence For Double Sequences
Generalised Statistical Convergence For Double SequencesGeneralised Statistical Convergence For Double Sequences
Generalised Statistical Convergence For Double SequencesIOSR Journals
 

Similaire à Bounded arithmetic in free logic (20)

Regularisation & Auxiliary Information in OOD Detection
Regularisation & Auxiliary Information in OOD DetectionRegularisation & Auxiliary Information in OOD Detection
Regularisation & Auxiliary Information in OOD Detection
 
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
A Probabilistic Algorithm for Computation of Polynomial Greatest Common with ...
 
Quadratic form and functional optimization
Quadratic form and functional optimizationQuadratic form and functional optimization
Quadratic form and functional optimization
 
Lecture_9_LA_Review.pptx
Lecture_9_LA_Review.pptxLecture_9_LA_Review.pptx
Lecture_9_LA_Review.pptx
 
Some Common Fixed Point Results for Expansive Mappings in a Cone Metric Space
Some Common Fixed Point Results for Expansive Mappings in a Cone Metric SpaceSome Common Fixed Point Results for Expansive Mappings in a Cone Metric Space
Some Common Fixed Point Results for Expansive Mappings in a Cone Metric Space
 
Approximate Nearest Neighbour in Higher Dimensions
Approximate Nearest Neighbour in Higher DimensionsApproximate Nearest Neighbour in Higher Dimensions
Approximate Nearest Neighbour in Higher Dimensions
 
PR 113: The Perception Distortion Tradeoff
PR 113: The Perception Distortion TradeoffPR 113: The Perception Distortion Tradeoff
PR 113: The Perception Distortion Tradeoff
 
Optimum Engineering Design - Day 2b. Classical Optimization methods
Optimum Engineering Design - Day 2b. Classical Optimization methodsOptimum Engineering Design - Day 2b. Classical Optimization methods
Optimum Engineering Design - Day 2b. Classical Optimization methods
 
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix MappingDual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
Dual Spaces of Generalized Cesaro Sequence Space and Related Matrix Mapping
 
Matrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence SpacesMatrix Transformations on Some Difference Sequence Spaces
Matrix Transformations on Some Difference Sequence Spaces
 
A direct method for estimating linear non-Gaussian acyclic models
A direct method for estimating linear non-Gaussian acyclic modelsA direct method for estimating linear non-Gaussian acyclic models
A direct method for estimating linear non-Gaussian acyclic models
 
20180831 riemannian representation learning
20180831 riemannian representation learning20180831 riemannian representation learning
20180831 riemannian representation learning
 
Discrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdfDiscrete Structure Lecture #5 & 6.pdf
Discrete Structure Lecture #5 & 6.pdf
 
A Non Local Boundary Value Problem with Integral Boundary Condition
A Non Local Boundary Value Problem with Integral Boundary ConditionA Non Local Boundary Value Problem with Integral Boundary Condition
A Non Local Boundary Value Problem with Integral Boundary Condition
 
Calculus Review Session Brian Prest Duke University Nicholas School of the En...
Calculus Review Session Brian Prest Duke University Nicholas School of the En...Calculus Review Session Brian Prest Duke University Nicholas School of the En...
Calculus Review Session Brian Prest Duke University Nicholas School of the En...
 
Computer aided design
Computer aided designComputer aided design
Computer aided design
 
Differential Geometry for Machine Learning
Differential Geometry for Machine LearningDifferential Geometry for Machine Learning
Differential Geometry for Machine Learning
 
lec10.ppt
lec10.pptlec10.ppt
lec10.ppt
 
Linear Combination of vectors, Span and dependency
Linear Combination of vectors, Span and dependencyLinear Combination of vectors, Span and dependency
Linear Combination of vectors, Span and dependency
 
Generalised Statistical Convergence For Double Sequences
Generalised Statistical Convergence For Double SequencesGeneralised Statistical Convergence For Double Sequences
Generalised Statistical Convergence For Double Sequences
 

Dernier

2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch TuesdayIvanti
 
A Glance At The Java Performance Toolbox
A Glance At The Java Performance ToolboxA Glance At The Java Performance Toolbox
A Glance At The Java Performance ToolboxAna-Maria Mihalceanu
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observabilityitnewsafrica
 
Kuma Meshes Part I - The basics - A tutorial
Kuma Meshes Part I - The basics - A tutorialKuma Meshes Part I - The basics - A tutorial
Kuma Meshes Part I - The basics - A tutorialJoão Esperancinha
 
Infrared simulation and processing on Nvidia platforms
Infrared simulation and processing on Nvidia platformsInfrared simulation and processing on Nvidia platforms
Infrared simulation and processing on Nvidia platformsYoss Cohen
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Farhan Tariq
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersNicole Novielli
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesBernd Ruecker
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Hiroshi SHIBATA
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Mark Goldstein
 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsNathaniel Shimoni
 
Varsha Sewlal- Cyber Attacks on Critical Critical Infrastructure
Varsha Sewlal- Cyber Attacks on Critical Critical InfrastructureVarsha Sewlal- Cyber Attacks on Critical Critical Infrastructure
Varsha Sewlal- Cyber Attacks on Critical Critical Infrastructureitnewsafrica
 
Landscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfLandscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfAarwolf Industries LLC
 
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sectoritnewsafrica
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesKari Kakkonen
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfNeo4j
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfpanagenda
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesManik S Magar
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024TopCSSGallery
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Kaya Weers
 

Dernier (20)

2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch Tuesday
 
A Glance At The Java Performance Toolbox
A Glance At The Java Performance ToolboxA Glance At The Java Performance Toolbox
A Glance At The Java Performance Toolbox
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
 
Kuma Meshes Part I - The basics - A tutorial
Kuma Meshes Part I - The basics - A tutorialKuma Meshes Part I - The basics - A tutorial
Kuma Meshes Part I - The basics - A tutorial
 
Infrared simulation and processing on Nvidia platforms
Infrared simulation and processing on Nvidia platformsInfrared simulation and processing on Nvidia platforms
Infrared simulation and processing on Nvidia platforms
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architectures
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directions
 
Varsha Sewlal- Cyber Attacks on Critical Critical Infrastructure
Varsha Sewlal- Cyber Attacks on Critical Critical InfrastructureVarsha Sewlal- Cyber Attacks on Critical Critical Infrastructure
Varsha Sewlal- Cyber Attacks on Critical Critical Infrastructure
 
Landscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfLandscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdf
 
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
4. Cobus Valentine- Cybersecurity Threats and Solutions for the Public Sector
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examples
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)
 

Bounded arithmetic in free logic

  • 1. Bounded Arithmetic in Free Logic Yoriyuki Yamagata CTFM, 2013/02/20
  • 2. Buss’s theories 𝑆2𝑖 –a#b=2 𝑎 ⋅|𝑏| • Language of Peano Arithmetic + “#” • BASIC axioms 𝑥 𝐴 , Γ → Δ, 𝐴(𝑥) 2 • PIND 𝐴 0 , Γ → Δ, 𝐴(𝑡) where 𝐴 𝑥 ∈ Σ 𝑖𝑏 , i.e. has 𝑖-alternations of bounded quantifiers ∀𝑥 ≤ 𝑡, ∃𝑥 ≤ 𝑡.
  • 3. PH and Buss’s theories 𝑆2𝑖 𝑆2 = 𝑆2 = 𝑆2 = … 1 2 3 𝑃 = □(𝑁𝑁) = □(Σ2 ) = … 𝑝 Implies We can approach (non) collapse of PH from (non) collapse of hierarchy of Buss’s theories (PH = Polynomial Hierarchy)
  • 4. Our approach • Separate 𝑆2𝑖 by Gödel incompleteness theorem • Use analogy of separation of 𝐼Σ 𝑖
  • 5. Separation of 𝐼Σ 𝑖 𝐼Σ3 ⊢ Con(IΣ2 ) … 𝐼Σ2 ⊢ Con IΣ2 ⊆ 𝐼Σ1 ⊆
  • 6. Consistency proof inside 𝑆2𝑖 • Bounded Arithmetics generally are not – 𝑆2 does not prove consistency of Q (Paris, Wilkie) capable to prove consistency. – 𝑆2 does not prove bounded consistency of 𝑆2 (Pudlák) 1 – 𝑆2 does not prove consistency the 𝐵 𝑖 𝑏 fragement 𝑖 of 𝑆2 (Buss and Ignjatović) −1
  • 7. Buss and Ignjatović(1995) …𝑆2 ⊢ 𝐵3 − Con(𝑆2 ) 3 b −1 𝑆2 2 ⊢ 𝐵2 b − Con(𝑆2 ) −1 ⊆ 𝑆2 1 ⊢ 𝐵1 b − Con(𝑆2 ) −1 ⊆
  • 8. Where… • 𝐵 𝑖 𝑏 − 𝐶𝐶𝐶 𝑇 – consistency of 𝐵 𝑖 𝑏 −proofs – 𝐵 𝑖 𝑏 −proofs : the proofs by 𝐵 𝑖 𝑏 -formule – 𝐵 𝑖 𝑏 :Σ0𝑏 (Σ 𝑖𝑏 )… Formulas generated from Σ 𝑖𝑏 by Boolean connectives and sharply bounded • 𝑆2 −1 quantifiers. – Induction free fragment of 𝑆2𝑖
  • 9. If… 𝑆2 ⊢ 𝐵i − Con 𝑆2 ,j > i 𝑗 b −1 Then, Buss’s hierarchy does not collapse.
  • 10. Consistency proof of 𝑆2 inside 𝑆2𝑖 −1 Problem • No truth definition, because • No valuation of terms, because • The values of terms increase exponentially In 𝑆2 world, terms do not have values a priori. 𝑖 • E.g. 2#2#2#2#2#...#2 • We introduce the predicate 𝐸 which signifies existence of • Thus, we must prove the existence of values in proofs. values.
  • 11. Our result(2012) 𝑆2 …5 ⊢3− Con(𝑆2 −1 𝐸) 𝑆2 4 ⊢2− Con(𝑆2 −1 𝐸) ⊆ 𝑆2 3 ⊢1− Con(𝑆2 −1 𝐸) ⊆
  • 12. Where… • 𝑖 − 𝐶𝐶𝐶 𝑇 – consistency of 𝑖-normal proofs – 𝑖-normal proofs : the proofs by 𝑖-normal formulas – 𝑖-normal formulas: Formulas in the form: ∃𝑥1 ≤ 𝑡1 ∀𝑥2 ≤ 𝑡2 … 𝑄𝑥 𝑖 ≤ 𝑡 𝑖 𝑄𝑥 𝑖+1 ≤ 𝑡 𝑖+1 . 𝐴(… ) Where 𝐴 is quantifier free
  • 13. Where… • 𝑆2 𝐸 −1 – Induction free fragment of 𝑆2 𝐸 𝑖 – have predicate 𝐸 which signifies existence of values • Such logic is called Free logic
  • 14. 𝑆2𝑖 𝐸(Language) • =, ≤, 𝐸 Predicates Function symbols • Finite number of polynomial functions Formulas • 𝐴 ∨ 𝐵, 𝐴 ∧ 𝐵 • Atomic formula, negated atomic formula • Bounded quantifiers
  • 15. 𝑆2𝑖 𝐸(Axioms) • 𝐸-axioms • Equality axioms • Data axioms • Defining axioms • Auxiliary axioms
  • 16. Idea behind axioms… → 𝑎= 𝑎 Because there is no guarantee of 𝐸𝐸 Thus, we add 𝐸𝐸 in the antecedent 𝐸𝐸 → 𝑎 = 𝑎
  • 17. E-axioms • 𝐸𝐸 𝑎1 , … , 𝑎 𝑛 → 𝐸𝑎 𝑗 • 𝑎1 = 𝑎2 → 𝐸𝑎 𝑗 • 𝑎1 ≠ 𝑎2 → 𝐸𝑎 𝑗 • 𝑎1 ≤ 𝑎2 → 𝐸𝑎 𝑗 • ¬𝑎1 ≤ 𝑎2 → 𝐸𝑎 𝑗
  • 18. Equality axioms • 𝐸𝐸 → 𝑎 = 𝑎 • 𝐸𝐸 ⃗ , ⃗ = 𝑏 → 𝑓 ⃗ = 𝑓 𝑏 𝑎 𝑎 𝑎
  • 19. Data axioms • → 𝐸𝐸 • 𝐸𝐸 → 𝐸𝑠0 𝑎 • 𝐸𝐸 → 𝐸𝑠1 𝑎
  • 20. Defining axioms 𝑓 𝑢 𝑎1 , 𝑎2 , … , 𝑎 𝑛 = 𝑡(𝑎1 , … , 𝑎 𝑛 ) 𝑢 𝑎 = 0, 𝑎, 𝑠0 𝑎, 𝑠1 𝑎 𝐸𝑎1 , … , 𝐸𝑎 𝑛 , 𝐸𝐸 𝑎1 , … , 𝑎 𝑛 → 𝑓 𝑢 𝑎1 , 𝑎2 , … , 𝑎 𝑛 = 𝑡(𝑎1 , … , 𝑎 𝑛 )
  • 21. Auxiliary axioms 𝑎 = 𝑏 ⊃ 𝑎#𝑐 = 𝑏#𝑐 𝐸𝐸#𝑐, 𝐸𝐸#𝑐, 𝑎 = |𝑏| → 𝑎#𝑐 = 𝑏#𝑐
  • 22. PIND-rule where 𝐴 is an Σ 𝑖𝑏 -formulas
  • 23. Bootstrapping 𝑆2𝑖 𝐸 I. 𝑆2 𝐸 ⊢ Tot(𝑓) for any 𝑓, 𝑖 ≥ 0 𝑖 II. 𝑆2 𝐸 ⊢ BASIC∗ , equality axioms 𝑖 ∗ III. 𝑆2 𝐸 ⊢ predicate logic 𝑖 ∗ IV. 𝑆2𝑖 𝐸⊢ Σ𝑖𝑏 −PIND∗
  • 24. Theorem (Consistency) 𝑆2𝑖+2 ⊢i− Con(𝑆2 −1 𝐸)
  • 25. Valuation trees ρ-valuation tree bounded by 19 ρ(a)=2, ρ(b)=3 a=2 a#a=16 b=3 𝑣 𝑎#𝑎 + 𝑏 , 𝜌 ↓19 19 a#a+b=19 𝑣 𝑡 , 𝜌 ↓ 𝑢 𝑐 is Σ1𝑏
  • 26. Bounded truth definition (1) • 𝑇 𝑢, 𝑡1 = 𝑡2 , 𝜌 ⇔def ∃𝑐 ≤ 𝑢, 𝑣 𝑡1 , 𝜌 ↓ 𝑢 𝑐 ∧ 𝑣 𝑡1 , 𝜌 ↓ 𝑢 𝑐 • 𝑇 𝑢, 𝜙1 ∧ 𝜙2 , 𝜌 ⇔def 𝑇 𝑢, 𝜙1 , 𝜌 ∧ 𝑇 𝑢, 𝜙2 , 𝜌 • 𝑇 𝑢, 𝜙1 ∨ 𝜙2 , 𝜌 ⇔def 𝑇 𝑢, 𝜙1 , 𝜌 ∨ 𝑇 𝑢, 𝜙2 , 𝜌
  • 27. Bounded truth definition (2) • 𝑇 𝑢, ∃𝑥 ≤ 𝑡, 𝜙(𝑥) , 𝜌 ⇔def ∃𝑐 ≤ 𝑢, 𝑣 𝑡 , 𝜌 ↓ 𝑢 𝑐 ∧ ∃𝑑 ≤ 𝑐, 𝑇 𝑢, 𝜙 𝑥 , 𝜌 𝑥 ↦ 𝑑 • 𝑇 𝑢, ∀𝑥 ≤ 𝑡, 𝜙(𝑥) , 𝜌 ⇔def ∃𝑐 ≤ 𝑢, 𝑣 𝑡 , 𝜌 ↓ 𝑢 𝑐 ∧ ∀𝑑 ≤ 𝑐, 𝑇(𝑢, 𝜙 𝑥 , 𝜌[𝑥 ↦ 𝑑]) Remark: If 𝜙 is Σ 𝑖𝑏 , 𝑇 𝑢, 𝜙 is Σ 𝑖+1 𝑏
  • 28. induction hypothesis 𝑢: enough large integer 𝑟: node of a proof of 0=1 Γ 𝑟 → Δ 𝑟 : the sequent of node 𝑟 𝜌: assignment 𝜌 𝑎 ≤ 𝑢 ∀𝑢′ ≤ 𝑢 ⊖ 𝑟, { ∀𝐴 ∈ Γ 𝑟 𝑇 𝑢′ , 𝐴 , 𝜌 ⊃ [∃𝐵 ∈ Δr , 𝑇(𝑢′ ⊕ 𝑟, 𝐵 , 𝜌)]}
  • 29. Conjecture 𝑆2 −1 𝐸 is weak enough – 𝑆2 can prove 𝑖-consistency of 𝑆2 𝐸 • 𝑖+2 −1 • While 𝑆2 𝐸 is strong enough −1 – 𝑆2 𝐸 can interpret 𝑆2 𝑖 𝑖 𝑆2 𝐸 is a good candidate to separate 𝑆2 and 𝑆2 . • Conjecture −1 𝑖 𝑖+2
  • 30. Future works 𝑆2 ⊢ 𝑖−Con(𝑆2 𝐸)? 𝑖 −1 • Long-term goal – Simplify 𝑆2 𝐸 • Short-term goal 𝑖
  • 31. Publications • Bounded Arithmetic in Free Logic Logical Methods in Computer Science Volume 8, Issue 3, Aug. 10, 2012