Cognitive Computing 2013 Consciousness and Computations 7. THE SELF APPLICABILITY PROBLEM (SAP); PENROSE ON UNDERSTANDING UNDERSTANDING Mark Bishop.

Slides:



Advertisements
Similar presentations
Cognitive Computing 2013 Consciousness and Computations 8. THE REDUCTION PRINCIPLE Prof. Mark Bishop.
Advertisements

Cognitive Computing: 2012 Consciousness and Computation: computing machinery and intelligence 4. NORMA PROGRAM SPEEDUP Mark Bishop.
Cognitive Computing: 2012 Consciousness and Computation: computing machinery & intelligence 3. EMULATION Mark Bishop.
Cognitive Computing 2012 Consciousness and Computation: computing machinery and intelligence 2. ON NORMA AND nCODING Mark Bishop.
Formal Models of Computation Part III Computability & Complexity
Turing Machines January 2003 Part 2:. 2 TM Recap We have seen how an abstract TM can be built to implement any computable algorithm TM has components:
Completeness and Expressiveness
Chapter 11: Models of Computation
David Evans cs302: Theory of Computation University of Virginia Computer Science Lecture 17: ProvingUndecidability.
Solving Absolute Value Equations Solving Absolute Value Equations
Introduction to Proofs
AE1APS Algorithmic Problem Solving John Drake
Finite-state Recognizers
Lecture 15 Functions CSCI – 1900 Mathematics for Computer Science Fall 2014 Bill Pine.
Introduction to Computability Theory
We want to prove the above statement by mathematical Induction for all natural numbers (n=1,2,3,…) Next SlideSlide 1.
Lecture 3 Universal TM. Code of a DTM Consider a one-tape DTM M = (Q, Σ, Γ, δ, s). It can be encoded as follows: First, encode each state, each direction,
1 Decidability continued…. 2 Theorem: For a recursively enumerable language it is undecidable to determine whether is finite Proof: We will reduce the.
David Evans CS150: Computer Science University of Virginia Computer Science Lecture 26: Proving Uncomputability Visualization.
Copyright © Cengage Learning. All rights reserved.
CSCI 4325 / 6339 Theory of Computation Zhixiang Chen Department of Computer Science University of Texas-Pan American.
1 COMP 382: Reasoning about algorithms Unit 9: Undecidability [Slides adapted from Amos Israeli’s]
Copyright © Cengage Learning. All rights reserved. CHAPTER 5 SEQUENCES, MATHEMATICAL INDUCTION, AND RECURSION SEQUENCES, MATHEMATICAL INDUCTION, AND RECURSION.
CSE115/ENGR160 Discrete Mathematics 02/28/12
Copyright © Cengage Learning. All rights reserved.
1 Undecidability Andreas Klappenecker [based on slides by Prof. Welch]
1 Introduction to Computability Theory Lecture15: Reductions Prof. Amos Israeli.
1 Introduction to Computability Theory Lecture12: Reductions Prof. Amos Israeli.
1 Introduction to Computability Theory Lecture13: Mapping Reductions Prof. Amos Israeli.
CPSC 411, Fall 2008: Set 12 1 CPSC 411 Design and Analysis of Algorithms Set 12: Undecidability Prof. Jennifer Welch Fall 2008.
Lecture 9 Recursive and r.e. language classes
1 Module 9 Recursive and r.e. language classes –representing solvable and half-solvable problems Proofs of closure properties –for the set of recursive.
Lecture 8 Recursively enumerable (r.e.) languages
1 Undecidability Andreas Klappenecker [based on slides by Prof. Welch]
CSE115/ENGR160 Discrete Mathematics 03/03/11 Ming-Hsuan Yang UC Merced 1.
Dr. Muhammed Al-Mulhem 1ICS ICS 535 Design and Implementation of Programming Languages Part 1 Computability (Chapter 2) ICS 535 Design and Implementation.
CHAPTER 4 Decidability Contents Decidable Languages
So far we have learned about:
1 Lecture 7 Halting Problem –Fundamental program behavior problem –A specific unsolvable problem –Diagonalization technique revisited Proof more complex.
Fall 2004COMP 3351 Reducibility. Fall 2004COMP 3352 Problem is reduced to problem If we can solve problem then we can solve problem.
Courtesy Costas Busch - RPI1 Reducibility. Courtesy Costas Busch - RPI2 Problem is reduced to problem If we can solve problem then we can solve problem.
1 Reducibility. 2 Problem is reduced to problem If we can solve problem then we can solve problem.
Chapter 4: A Universal Program 1. Coding programs Example : For our programs P we have variables that are arranged in a certain order: Y 1 X 1 Z 1 X 2.
Introduction to Proofs
MATH 224 – Discrete Mathematics
Copyright © Cengage Learning. All rights reserved. CHAPTER 4 ELEMENTARY NUMBER THEORY AND METHODS OF PROOF ELEMENTARY NUMBER THEORY AND METHODS OF PROOF.
Algorithms and their Applications CS2004 ( ) Dr Stephen Swift 1.2 Introduction to Algorithms.
1 Sections 1.5 & 3.1 Methods of Proof / Proof Strategy.
1 The Halting Problem and Decidability How powerful is a TM? Any program in a high level language can be simulated by a TM. Any algorithmic procedure carried.
Course Overview and Road Map Computability and Logic.
Great Theoretical Ideas in Computer Science.
Copyright © Cengage Learning. All rights reserved.
1 Introduction to Abstract Mathematics Chapter 2: The Logic of Quantified Statements. Predicate Calculus Instructor: Hayk Melikya 2.3.
Great Theoretical Ideas in Computer Science.
Naïve Set Theory. Basic Definitions Naïve set theory is the non-axiomatic treatment of set theory. In the axiomatic treatment, which we will only allude.
UNIT-I INTRODUCTION ANALYSIS AND DESIGN OF ALGORITHMS CHAPTER 1:
CS 3813: Introduction to Formal Languages and Automata Chapter 12 Limits of Algorithmic Computation These class notes are based on material from our textbook,
1 Turing’s Thesis. 2 Turing’s thesis: Any computation carried out by mechanical means can be performed by a Turing Machine (1930)
 2005 SDU Lecture13 Reducibility — A methodology for proving un- decidability.
1Computer Sciences Department. Book: INTRODUCTION TO THE THEORY OF COMPUTATION, SECOND EDITION, by: MICHAEL SIPSER Reference 3Computer Sciences Department.
Fundamentals of Informatics Lecture 13 Reduction Bas Luttik.
Donghyun (David) Kim Department of Mathematics and Computer Science North Carolina Central University 1 Chapter 4 Decidability Some slides are in courtesy.
1 Undecidability Andreas Klappenecker [based on slides by Prof. Welch]
THE HALTING PROBLEM - PROOF. Review  What makes a problem decidable?  3 properties of an efficient algorithm?  What is the meaning of “complete”, “mechanistic”,
Recursively Enumerable and Recursive Languages. Definition: A language is recursively enumerable if some Turing machine accepts it.
Busch Complexity Lectures: Reductions
Reductions Costas Busch - LSU.
Great Theoretical Ideas in Computer Science
CSCE 411 Design and Analysis of Algorithms
Decidable Languages Costas Busch - LSU.
Presentation transcript:

Cognitive Computing 2013 Consciousness and Computations 7. THE SELF APPLICABILITY PROBLEM (SAP); PENROSE ON UNDERSTANDING UNDERSTANDING Mark Bishop

01/04/2014(c) Bishop: Consciousness and computations2 On unsolvable decision problems Introduction We will see that there exist decision problems connected with NORMA programs that are fundamentally undecidable. Some of these problems have practical implications and, (as NORMA is a universal machine), if NORMA can't solve them, no other universal machine can. E.g. A programmer who writes some code to carry out a given task may need to know if the program terminates under specific conditions. A programmer may also need to know if the code of one program is exactly equivalent to a second, more efficient program.

01/04/2014(c) Bishop: Consciousness and computations3 PC: program correctness Determining if an algorithm will terminate given specific input or determining if one algorithm is exactly equivalent to another define aspects of correct program functionality. Program correctness. I.e. {WHILE X=0 DO INC Y} will not terminate for [X = 0]. However we will show that both termination and equivalence are not algorithmically solvable. This does not imply that for a particular program the above qualities cannot be formulated. Simply that there is no general algorithmic solution to the above problems.

01/04/2014(c) Bishop: Consciousness and computations4 Totality and computability The algorithm given in the previous seminar for the function Y = X 2 is both total and computable. i.e. It is defined for all elements of its domain (it is total) and could be implemented on any Universal Machine (it is a computable function). Much current research in computer science is concerned with modern notations & languages, such as Z, that enable properties of specific algorithms to be proved in a manner analogous to mathematical proof.

01/04/2014(c) Bishop: Consciousness and computations5 The Self Applicability Problem, SAP The Self Applicability Problem is important since all unsolvable decision problems can be reduced to it. Given that a NORMA program is Defined iff it terminates; SAP can be stated in the following form: to decide, given an arbitrary NORMA program P, whether or not P (p) is defined, where p = nCode (P). Ie. Is there a NORMA program Q, that can compute the function: Q (p)= 0iff NORMA p (p) is defined = 1Otherwise

01/04/2014(c) Bishop: Consciousness and computations6 Solution Assume SAP is total and computable. Let Q be a program that can compute SAP. And define R be the program: l. Q; (l+1).WHILE (Y = 0) DO (l+1); … where r = nCode (R). Thus R carries out each computation of Q, but before terminating tests the output of Q. If the output is zero, (i.e. P (p) is defined), then Y must equal zero; hence by inspection R will not terminate! Clearly R (x)= Q (x) iff Q (x) <> 0 = undefined otherwise

01/04/2014(c) Bishop: Consciousness and computations7 … Solution (contd) But Consider: Suppose R (r) is defined, then from inspection of R, Q (r) => 1 However from the definition of Q, Q (r) = 1, iff R (r) is undefined! Therefore R (r) cannot be defined! Suppose R (r) is undefined, then from inspection of R, Q (r) => 0 However from the definition of Q, Q (r) = 0 iff R (r) is defined! Therefore R (r) cannot be undefined! If R cannot be defined or undefined then R cannot exist. Therefore R cannot be constructed. But clearly R can be constructed if Q can. Therefore Q cannot exist and SAP is NORMA unsolvable.

01/04/2014(c) Bishop: Consciousness and computations8 Penrose, On understanding understanding Consider a to be a sound set of rules (an effective procedure) to determine if the computation C (n) does not stop. Like NORMA p (x), C (n) is merely some computation on the natural number n. E.g. Calculate an odd number that is the sum of (n) even numbers.. Let A be a formalization of all such procedures known to human mathematicians. Ex-hypothesi, the application of the set of rules, A, terminates only iff C (n) does not stop... imagine a group of human mathematicians continuously analysing C (n) only ceasing their contemplation if and when one shouts out, Eureka!! C (n) does not stop! NB. A must be sound (i.e. it cannot be wrong) if it decides that C (n) does not stop, as if any of the procedures in A were unsound it would eventually be found out.

01/04/2014(c) Bishop: Consciousness and computations9 Enumerating computations on (n) We have previously seen that computations of one parameter, n, can be enumerated (listed): C 0 (n), C 1 (n), C 2 (n).. C p (n) where C p is the p th computation on n. … I.e. It defines the p th computation of one parameter, (n). NB. This ordering is computable and in NORMA (where a single input cardinal number is entered to the NORMA program P, via the X register) is defined by the ordering specified by the function nCode (P): I.e. nCode (P) = pI.e. The p th NORMA computation of one parameter; NORMA p (X = n) Hence A (p, n) is the effective procedure that when presented with p and n attempts to discover if C p (n) will not halt. If A (p, n) HALTS we KNOW that C p (n) does not HALT.

01/04/2014(c) Bishop: Consciousness and computations10 Penrose argument 1. If A (p, n) halts THEN C p (n) does not halt. 2. Let (p = n) [i.e. the Penrose SAP on n; SAP (n)] 3. IF A (n, n) halts THEN C n (n) [i.e. SAP (n)] does not halt. 4. But A (n, n) is a function of one number (n) hence it must occur in the enumeration of C. Let us say it occurs at position k, i.e. it is computation C k (n) 5. Hence A (n, n) = C k (n) Recall k is not a parameter but some specific numeric identifier [label]; i.e. the location of A (n, n) in the enumeration of C. 6. Now we examine the particular computation where (n = k) 7. Hence, substituting (n = k) into (5) we get: A (k, k) = C k (k) 8. But now rewriting [3] with (n = k) we observe: … 9. iff A (k, k) halts THEN C k (k) does not halt 10. Now substituting from [7] into [9] we get the following contradiction [11] if C k (k) halts: 11. iff C k (k) halts THEN C k (k) does not halt ! 12. Hence from [11] we know that IF A IS SOUND C k (k) CANNOT HALT (otherwise we instantiate this contradiction)

01/04/2014(c) Bishop: Consciousness and computations11 Penrose, We know what A is unable to ascertain 13. But from [7] we know that A (k, k) CANNOT HALT either As from [7], A (k, k) = C k (k). 14. Thus, if A is sound, A is not capable of ascertaining if this particular computation - C k (k) - does not stop !! even though A cannot halt if it is sound or, from (11) we get a contradiction. 15. But if A exists and it is sound WE KNOW - from 12 - that C k (k) MUST not stop and hence WE KNOW - from 13 - something that, if A is sound, it is provably unable to ascertain. 16. Hence A cannot encapsulate mathematical understanding. Penrose, Human mathematicians are not using a knowably sound argument to ascertain mathematical truth, (Shadows, pp. 76).