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CDT314

FABER

Formal Languages, Automata and Models of Computation

Lecture 15School of Innovation, Design and Engineering

Mälardalen University2012

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Content

Church-Turing Thesis

Other Models of ComputationTuring MachinesRecursive FunctionsPost SystemsRewriting Systems Matrix Grammars Markov Algorithms Lindenmayer-Systems

Natural/Physical ComputationBiological ComputingQuantum Computing

THE CONCEPT OF COMPUTABILITY

“I explore the conceptual foundations of Alan Turing’s analysis of computability, which still dominates thinking about computability today. I argue that Turing’s account represents a last vestige of a famous but unsuccessful program in pure mathematics, viz., Hilbert’s formalist program.

It is my contention that the plausibility of Turing’s account as an analysis of the computational capacities of physical machines rests upon a number of highly problematic assumptions whose plausibility in turn is grounded in the formalist stance towards mathematics.

More specifically, the Turing account conflates concepts that are crucial for understanding the computational capacities of physical machines. These concepts include the idea of an “operation” or “action” that is “formal,” “mechanical,” “well-defined,” and “precisely described,” and the idea of a “symbol” that is “formal,” “uninterpreted,” and “shaped”.

When these concepts are disentangled, the intuitive appeal of Turing’s account is significantly undermined. This opens the way for exploring models of hypercomputability that are fundamentally di:erent from those currently entertained in the literature.” Carol E. Cleland

http://www.sciencedirect.com/science/journal/03043975

http://www.damtp.cam.ac.uk/events/strings02/dirac/hawking Stephen Hawking: Gödel and the end of Physics

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Church-Turing Thesis*

*Source: Stanford Encyclopaedia of Philosophy

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A Turing machine is an abstract representation of a computing device.

It is more like a computer program (software)

than a computer (hardware).

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LCMs [Logical Computing Machines:

Turing’s expression for Turing machines]

were first proposed by Alan Turing,

in an attempt to give

a mathematically precise definition

of "algorithm" or "mechanical procedure".

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The Church-Turing thesis concerns an effective or mechanical method in logic and mathematics.

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A method, M, is called ‘effective’ or ‘mechanical’ just in case:

1. M is set out in terms of a finite number of exact instructions (each instruction being expressed by means of a finite number of symbols);

2. M will, if carried out without error, always produce the desired result in a finite number of steps;

3. M can (in practice or in principle) be carried out by a human being unaided by any machinery except for paper and pencil;

4. M demands no insight or ingenuity on the part of the human being carrying it out.

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Turing’s thesis: LCMs [logical computing machines; TMs] can do anything that could be described as "rule of thumb" or "purely mechanical". (Turing 1948:7)

He adds: This is sufficiently well established that it is now agreed amongst logicians that "calculable by means of an LCM" is the correct accurate rendering of such phrases.

http://www.alanturing.net/turing_archive/pages/Reference%20Articles/The%20Turing-Church%20Thesis.html

AlanTuring.net (Jack Copeland)

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Turing introduced this thesis in the course of arguing that the Entscheidungsproblem, or decision problem, for the predicate calculus - posed by Hilbert (1928) - is unsolvable.

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Church’s account of the Entscheidungsproblem

By the Entscheidungsproblem of a system of symbolic logic is here understood the problem to find an effective method by which, given any expression Q in the notation of the system, it can be determined whether or not Q is provable in the system.

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The truth table test is such a method for the propositional logic.

Turing showed that, given his thesis, there can be no such method for the predicate logic.

Propositional logic (also called sentential calculus or sentential logic) is a formal system in which formulas of a formal language may be interpreted as representing propositions. A system of inference rules and axioms allows theorems to be derived as true propositions. http://en.wikipedia.org/wiki/Propositional_calculus

In mathematical logic, predicate logic is the symbolic formal systems like first-order logic, second-order logic or many-sorted logic. This formal system is distinguished from other systems in that its formulae contain variables which can be quantified. Two common quantifiers are the existential ("there exists") and universal ("for all") quantifiers. ∃ ∀http://en.wikipedia.org/wiki/Predicate_logic

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Turing proved formally that there is no TM which can determine, in a finite number of steps, whether or not any given formula of the predicate calculus is a theorem of the calculus.

So, given his thesis that if an effective method exists then it can be carried out by one of his machines, it follows that there is no such method to be found.

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Church’s thesis: A function of positive integers is effectively calculable only if recursive.

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Misunderstandings of the Turing Thesis

Turing did not show that his machines can solve any problem that can be solved "by instructions, explicitly stated rules, or procedures" and nor did he prove that a universal Turing machine "can compute any function that any computer, with any architecture, can compute".

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Turing proved that his universal machine can compute any function that any Turing machine can compute; and he put forward, and advanced philosophical arguments in support of, the thesis here called Turing’s thesis.

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A thesis concerning the extent of effective methods - procedures that a human being unaided by machinery is capable of carrying out - has no implication concerning the extent of the procedures that machines are capable of carrying out, even machines acting in accordance with ‘explicitly stated rules’.

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Among a machine’s repertoire of atomic operations there may be those that no human being unaided by machinery can perform.

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Turing introduces his machines as an idealised description of a certain human activity, the tedious one of numerical computation, which until the advent of automatic computing machines was the occupation of many thousands of people in commerce, government, and research establishments. (Jack Copeland)

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Turing’s "Machines". These machines are humans who calculate. (Wittgenstein)

A man provided with paper, pencil, and rubber, and subject to strict discipline, is in effect a universal machine. (Turing)

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The Entscheidungsproblem is the problem of finding a humanly executable procedure of a certain sort, and Turing’s aim was precisely to show that there is no such procedure in the case of predicate logic.

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Other Models of ComputationEquivalent to

Turing Machines

Based on C Busch, RPI, Models of Computation

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Equivalent Models of Computation

• Turing Machines

• Recursive Functions

• Post Systems

• Rewriting Systems

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All models of computation are equivalent.

A computation is mechanical if and only if

it can be performed by a Turing Machine.

Turing’s Thesis

Church’s Thesis (extended)

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Post Systems

• Axioms

• Productions

Similar to unrestricted grammars

A Post canonical system, as created by Emil Post, is a string-manipulation system that starts with finitely-many strings and repeatedly transforms them by applying a finite set of specified rules, thus generating a formal language. Today they are mainly of historical relevance because every Post canonical system can be reduced to a string rewriting system

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Example: Unary Addition

Axiom: 1111

Productions:

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11

321321

321321

VVVVVV

VVVVVV

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111111111111111111

11 321321 VVVVVV

11 321321 VVVVVV

Production: Axiom: 1111

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Rewriting Systems

• Matrix Grammars

• Markov Algorithms

• Lindenmayer-Systems (L-Systems)

They convert one string to another

Very similar to unrestricted grammars.

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Matrix Grammars

Example:

213

22112

211

,:

,:

:

SSP

cbSSaSSP

SSSP

A set of productions is applied simultaneously.

Derivation:

aabbccccbbSaaScbSaSSSS 212121

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213

22112

211

,:

,:

:

SSP

cbSSaSSP

SSSP

}0:{ ncbaL nnn

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Markov Algorithms

Grammars that produce

Example:

S

SaSb

Sab

Derivation:

SaSbaaSbbaaabbb

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S

SaSb

Sab

}0:{ nbaL nn

In general: }:{*

wwL

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Lindenmayer-Systems

They are parallel rewriting systems

Example: aaa

aaaaaaaaaaaaaaa Derivation:

}0:{ 2 naLn

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Lindenmayer-Systems are not general

as recursively enumerable languages

Extended Lindenmayer-Systems: uyax ),,(

context

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L-System Example: Fibonacci numbers

Consider the following simple grammar:

variables : A B

constants : none

start: A

rules: A B

B AB

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This L-system produces the following sequence of strings ...

Stage 0 : A Stage 1 : B Stage 2 : AB Stage 3 : BAB Stage 4 : ABBAB Stage 5 : BABABBAB Stage 6 : ABBABBABABBAB Stage 7 : BABABBABABBABBABABBAB

start: A rules: A B B AB

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If we count the length of each string, we obtain the Fibonacci sequence of numbers :

1 1 2 3 5 8 13 21 34 ....

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Example - Algal growth

The figure shows the pattern of cell lineages found in the alga Chaetomorpha linum.

To describe this pattern, we must let the symbols denote cells in different states, rather than different structures.

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This growth process can be generated from an axiom A and growth rules

A DB

B C

C D

D E

E A

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Here is the pattern generated by this model.

It matches the arrangement of cells in the original alga.

Stage 0 : A

Stage 1 : D B

Stage 2 : E C

Stage 3 : A D

Stage 4 : D B E

Stage 5 : E C A

Stage 6 : A D D B

Stage 7 : D B E E C

Stage 8 : E C A A D

Stage 9 : A D D B D B E

Stage 10 : D B E E C E C A

Stage 11 : E C A A D A D D B

A DB B C C D D E E A

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Example - a compound leaf (or branch)

Leaf1 { ; Name of the l-system, "{" indicates start ; Compound leaf with alternating branches, angle 8 ; Set angle increment to (360/8)=45 degrees axiom x ; Starting character string a=n ; Change every "a" into an "n" n=o ; Likewise change "n" to "o" etc ... o=p p=x b=e e=h h=j j=y x=F[+A(4)]Fy ; Change every "x" into "F[+A(4)]Fy" y=F[-B(4)]Fx ; Change every "y" into "F[-B(4)]Fx" F=@1.18F@i1.18 } ; final } indicates end

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http://www.xs4all.nl/~cvdmark/tutor.html L-systems tutorial with animations

http://www.youtube.com/watch?v=ZVyi4ykVHJM&feature=related apple tree generated by L system

http://www.youtube.com/watch?v=l3Txk_difKA&feature=related rose

http://nptel.iitm.ac.in/courses/106106049/39 Lecture on L systems by Prof. Kamala Krithivasan

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Here is a series of forms created by slowly changing the angle parameter. lsys00.ls

Check the rest of the Gallery of L-systems:http://home.wanadoo.nl/laurens.lapre

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A model of a horse chestnut tree inspired by the work of Chiba and Takenaka.

Here branches compete for light from the sky hemisphere. Clusters of leaves cast shadows on branches further down. An apex in shade does not produce new branches. An existing branch whose leaves do not receive enough light dies and is shed from the tree. In such a manner, the competition for light controls the density of branches in the tree crowns.

Reception

Internal processes

Response

Plant

Response

Internal processes

Reception

Environment

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Reception

Internal processes

Response

Plant

Response

Internal processes

Reception

Environment

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Apropos adaptive reactive systems:"What's the color of a chameleon put onto a mirror?"

Stewart Brand (Must be possible to verify experimentally, isn’t it?)

http://research.microsoft.com/pubs/144550/CACM_11.pdf  Biology as Reactivity From Communications of ACM (Association for Computing Machinery)

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Physical (Natural) Computation

Feynman, R. The Character of Physical Law, page 57:

"It always bothers me that, according to the laws as we

understand them today, it takes a computing machine an infinite

number of logical operations to figure out what goes on in no

matter how tiny a region of space, and no matter how tiny a

region of time. How can all that be going on in that tiny space?

Why should it take an infinite amount of logic to figure out what

one tiny piece of space/time is going to do? So I have often

made the hypotheses that ultimately physics will not require a

mathematical statement, that in the end the machinery will be

revealed, and the laws will turn out to be simple, like the

chequer board with all its apparent complexities".

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Biological Computing

This part of the lecture can be made into a whole course, seehttp://web.eecs.utk.edu/~mclennan http://web.eecs.utk.edu/~mclennan/Classes/420/ Biologically-Inspired Computation

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DNA Computing

DNA computing is a form of computing which uses DNA, biochemistry and molecular biology, instead of the traditional silicon-based computer technologies. DNA computing, or, more generally, biomolecular computing, is a fast developing interdisciplinary area. Research and development in this area concerns theory, experiments and applications of DNA computing.

http://en.wikipedia.org/wiki/DNA_computing

DNA

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DNA Computing

Despite their respective complexities, biological and mathematical operations have some similarities:

• The very complex structure of a living being is the result of applying simple operations to initial information encoded in a DNA sequence (genes).

• All complex math problems can be reduced to simple operations like addition and subtraction.

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DNA Computing

Despite their respective complexities, biological and mathematical operations have some similarities:

• The very complex structure of a living being is the result of applying simple operations to initial information encoded in a DNA sequence (genes).

• All complex math problems can be reduced to simple operations like addition and subtraction.

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For the same reasons that DNA was presumably selected (by natural selection) for living organisms as a genetic material, its stability and predictability in reactions, DNA strings can also be used to encode information for mathematical systems.

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The Hamiltonian Path Problem

The objective is to find a path from start to end going through all the points only once.

This problem is difficult for conventional (serial logic) computers because they must try each path one at a time. It is like having a whole bunch of keys and trying to see which fits a lock.

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Conventional computers are very good at math, but poor at "key into lock" problems. DNA based computers can try all the keys at the same time (massively parallel) and thus are very good at key-into-lock problems, but much slower at simple mathematical problems like multiplication.

The Hamiltonian Path problem was chosen because every key-into-lock problem can be solved as a Hamiltonian Path problem.

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Solving the Hamiltonian Path Problem

1. Generate random paths through the graph.

2. Keep only those paths that begin with the start city (A) and conclude with the end city (G).

3. Because the graph has 7 cities, keep only those paths with 7 cities.

4. Keep only those paths that enter all cities at least once.

5. Any remaining paths are solutions.

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Solving the Hamiltonian Path Problem

The key to solving the problem was using DNA to perform the five steps in the above algorithm.

These interconnecting blocks can be used to model DNA:

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DNA tends to form long double helices:

The two helices are joined by "bases", represented here by coloured blocks. Each base binds only one other specific base. In our example, we will say that each coloured block will only bind with the same colour. For example, if we only had red blocks, they would form a long chain like this:

Any other colour will not bind with red:

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Programming with DNAStep 1: Create a unique DNA sequence for each city A through G. For each path, for example, from A to B, create a linking piece of DNA that matches the last half of A and first half of B:

Here the red block represents city A, while the orange block represents city B. The half-red half-orange block connecting the two other blocks represents the path from A to B.

In a test tube, all the different pieces of DNA will randomly link with each other, forming paths through the graph.

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Step 2: Because it is difficult to "remove" DNA from the solution, the target DNA, the DNA which started at A and ended at G was copied over and over again until the test tube contained a lot of it relative to the other random sequences.

This is essentially the same as removing all the other pieces. Imagine a sock drawer which initially contains one or two coloured socks. If you put in a hundred black socks, chances are that all you will get if you reach in is black socks!

Programming with DNA

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Step 3: Going by weight, the DNA sequences which were 7 "cities" long were separated from the rest.

A "sieve" was used which allows smaller pieces of DNA to pass through quickly, while larger segments are slowed down. The procedure used actually allows you to isolate the pieces which are precisely 7 cities long from any shorter or longer paths.

Programming with DNA

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Step 4: To ensure that the remaining sequences went through each of the cities, "sticky" pieces of DNA attached to magnets were used to separate the DNA.

The magnets were used to ensure that the target DNA remained in the test tube, while the unwanted DNA was washed away. First, the magnets kept all the DNA which went through city A in the test tube, then B, then C, and D, and so on. In the end, the only DNA which remained in the tube was that which went through all seven cities.

Programming with DNA

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Step 5: All that was left was to sequence the DNA, revealing the path from A to B to C to D to E to F to G.

Programming with DNA

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Advantages of DNA computing

The above procedure took approximately one week to perform. Although this particular problem could be solved on a piece of paper in under an hour, when the number of cities is increased to 70, the problem becomes too complex for even a supercomputer.

While a DNA computer takes much longer than a normal computer to perform each individual calculation, it performs an enormous number of operations at a time (massively parallel).

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DNA computers also require less energy and space than normal computers. 1000 litres of water could contain DNA with more memory than all the computers ever made, and a pound of DNA would have more computing power than all the computers ever made.

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The Future of DNA computing

DNA computing was initially developed by Leonard Adleman of the University of Southern California, in 1994 and it is an research area under intense development. It is too early for either great optimism or great pessimism.

Early computers such as ENIAC filled entire rooms, and had to be programmed by punch cards. Since that time, computers have become much smaller and easier to use.

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DNA computers will probably become more common in the future for solving very complex problems.

Just as DNA cloning and sequencing were once manual tasks, DNA computers will also become automated.

Read more at:

http://research.microsoft.com/en-us/projects/dna/

http://www.casi.net/D.BioInformatics1/D.Fall2000ClassPage/DC2/dc2.htm

http://www.alexpetty.com/2010/09/11/vortex-math-based-computing/

http://news.bbc.co.uk/2/hi/technology/8184033.stm

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Quantum Computing

This part of the lecture can also be made into a whole course, see courses in Quantum Computing:

http://www.qubit.org/courses.html

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Today: fraction of micron (10-6 m) wide logic gates and wires on the surface of silicon chips.

Soon they will yield even smaller parts and inevitably reach a point where logic gates are so small that they are made out of only a handful of atoms.

1 nm = 10-9 m Helium atom 10-10 m

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On the atomic scale matter obeys the rules of quantum mechanics, which are quite different from the classical rules that determine the properties of conventional logic gates.

So if computers are to become smaller in the future, new, quantum technology must replace or supplement what we have now.

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What is quantum mechanics?

The deepest theory of physics; the framework within which all other current theories, except the general theory of relativity, are formulated. Some of its features are:

Quantisation (which means that observable quantities do not vary continuously but come in discrete chunks or 'quanta'). This is the one that makes computation, classical or quantum, possible at all.

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Interference (which means that the outcome of a quantum process in general depends on all the possible histories of that process).

This is the feature that makes quantum computers qualitatively more powerful than classical ones.

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Entanglement (Two spatially separated and non-interacting quantum systems that have interacted in the past may still have some locally inaccessible information in common – information which cannot be accessed in any experiment performed on either of them alone.)

This is the one that makes quantum cryptography possible.

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The discovery that quantum physics allows fundamentally new modes of information processing has required the existing theories of computation, information and cryptography to be superseded by their quantum generalisations.

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Let us try to reflect a single photon off a half-silvered mirror i.e. a mirror which reflects exactly half of the light which impinges upon it, while the remaining half is transmitted directly through it.

It seems that it would be sensible to say that the photon is either in the transmitted or in the reflected beam with the same probability.

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Indeed, if we place two photodetectors behind the half-silvered mirror in direct lines of the two beams, the photon will be registered with the same probability either in the detector 1 or in the detector 2.

Does it really mean that after the half-silvered mirror the photon travels in either reflected or transmitted beam with the same probability 50%?

No, it does not ! In fact the photon takes `two paths at once'.

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This can be demonstrated by recombining the two beams with the help of two fully silvered mirrors and placing another half-silvered mirror at their meeting point, with two photodectors in direct lines of the two beams.

With this set up we can observe a truly amazing quantum interference phenomenon.

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If it were merely the case that there were a 50% chance that the photon followed one path and a 50% chance that it followed the other, then we should find a 50% probability that one of the detectors registers the photon and a 50% probability that the other one does.

However, that is not what happens. If the two possible paths are exactly equal in length, then it turns out that there is a 100% probability that the photon reaches the detector 1 and 0% probability that it reaches the other detector 2. Thus the photon is certain to strike the detector 1!

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It seems inescapable that the photon must, in some sense, have actually travelled both routes at once for if an absorbing screen is placed in the way of either of the two routes, then it becomes equally probable that detector 1 or 2 is reached.

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Blocking off one of the paths actually allows detector 2 to be reached. With both routes open, the photon somehow knows that it is not permitted to reach detector 2, so it must have actually “felt out” both routes.

It is therefore perfectly legitimate to say that between the two half-silvered mirrors the photon took both the transmitted and the reflected paths.

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Using more technical language, we can say that the photon is in a coherent superposition of being in the transmitted beam and in the reflected beam.

In much the same way an atom can be prepared in a superposition of two different electronic states, and in general a quantum two state system, called a quantum bit or a qubit, can be prepared in a superposition of its two logical states 0 and 1. Thus one qubit can encode at a given moment of time both 0 and 1.

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In principle we know how to build a quantum computer; we can start with simple quantum logic gates and try to integrate them together into quantum circuits.

A quantum logic gate, like a classical gate, is a simple computing device that performs one elementary quantum operation, usually on two qubits, in a given period of time.

Of course, quantum logic gates are different from their classical counterparts because they can create and perform operations on quantum superpositions.

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So the advantage of quantum computers arises from the way they encode a bit, the fundamental unit of information.

The state of a bit in a classical digital computer is specified by one number, 0 or 1.

An n-bit binary word in a typical computer is accordingly described by a string of n zeros and ones.

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A quantum bit, called a qubit, might be represented by an atom in one of two different states, which can also be denoted as 0 or 1.

Two qubits, like two classical bits, can attain four different well-defined states (0 and 0, 0 and 1, 1 and 0, or 1 and 1).

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But unlike classical bits, qubits can exist simultaneously as 0 and 1, with the probability for each state given by a numerical coefficient.

Describing a two-qubit quantum computer thus requires four coefficients. In general, n qubits demand 2n numbers, which rapidly becomes a sizable set for larger values of n.

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For example, if n equals 50, about 1015 numbers are required to describe all the probabilities for all the possible states of the quantum machine--a number that exceeds the capacity of the largest conventional computer.

A quantum computer promises to be immensely powerful because it can be in multiple states at once (superposition) -- and because it can act on all its possible states simultaneously.

Thus, a quantum computer could naturally perform myriad operations in parallel, using only a single processing unit.

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The most famous example of the extra power of a quantum computer is Peter Shor's algorithm for factoring large numbers.

Factoring is an important problem in cryptography; for instance, the security of RSA public key cryptography depends on factoring being a hard problem.

Despite much research, no efficient classical factoring algorithm is known.

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However if we keep on putting quantum gates together into circuits we will quickly run into some serious practical problems.

The more interacting qubits are involved the harder it tends to be to engineer the interaction that would display the quantum interference.

Apart from the technical difficulties of working at single-atom and single-photon scales, one of the most important problems is that of preventing the surrounding environment from being affected by the interactions that generate quantum superpositions.

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The more components the more likely it is that quantum computation will spread outside the computational unit and will irreversibly dissipate useful information to the environment.

This process is called decoherence. Thus the race is to engineer sub-microscopic systems in which qubits interact only with themselves but not not with the environment.

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But, the problem is not entirely new!

Remember STM? (Scanning Tunnelling Microscope )

STM was a Nobel Prize winning invention by Binning and Rohrer at IBM Zurich Laboratory in the early 1980s

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Title : Quantum Corral

Media : Iron on Copper (111)

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Scientists discovered a new method for confining electrons to artificial structures at the nanometer length scale.

Surface state electrons on Cu(111) were confined to closed structures (corrals) defined by barriers built from Fe atoms.

The barriers were assembled by individually positioning Fe atoms using the tip of a low temperature scanning tunnelling microscope (STM). A circular corral of radius 71.3 Angstrom was constructed in this way out of 48 Fe atoms.

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The standing-wave patterns in the local density of states of the Cu(111) surface. These spatial oscillations are quantum-mechanical interference patterns caused by scattering of the two-dimensional electron gas off the Fe atoms and point defects.

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What will quantum computers be good at?

The most important applications currently known:

• Cryptography: perfectly secure communication. • Searching, especially algorithmic searching (Grover's

algorithm). • Factorising large numbers very rapidly

(Shor's algorithm). • Simulating quantum-mechanical systems efficiently

Read more:http://www.howstuffworks.com/quantum-computer.htm (How stuff works)

http://www.sciencedaily.com/news/computers_math/quantum_computers/

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WHAT IS COMPUTATION?

Theoretical Computer Science 317 (2004)

Burgin, M., Super-Recursive Algorithms, Springer Monographs in Computer Science, 2005, ISBN: 0-387-95569-0

Minds and Machines (1994, 4, 4) “What is Computation?”

Journal of Logic, Language and Information (Volume 12 No 4 2003) What is information?

NATURAL COMPUTING RESOURCES

http://en.wikipedia.org/wiki/Natural_computing

http://nptel.iitm.ac.in/courses/106106049/39 Theory of Automata,

Formal Languages and Computation. Video Lectures by Prof. Kamala Krithivasan

http://nptel.iitm.ac.in/courses/106106049

http://www.nature.com/nature/journal/v419/n6905/full/419343a.html

Cellular abstractions: Cells as computation

http://www.wisdom.weizmann.ac.il/~biospi CONCURRENT BIOLOGICAL PROCESSES

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http://www.amazon.co.uk/Fundamentals-Natural-Computing-ApplicationsInformation/dp/1584886439/ref=sr_1_4?ie=UTF8&qid=1326058908&sr=8-4#reader_1584886439

Fundamentals of Natural Computing: Basic Concepts (book)

http://www.springerlink.com/content/1567-7818/10 Natural Computing Journal, Springer

http://www.oldcitypublishing.com/IJUC/IJUC.html International Journal of Unconventional Computing

http://www.gcn.us.es Research Group on Natural Computing

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NATURAL COMPUTING RESOURCES

http://www.amazon.co.uk/Fundamentals-Natural-Computing-Applications-Information/dp/1584886439/ref=sr_1_9?ie=UTF8&qid=1326119722&sr=8-9#reader_1584886439

Leandro Nunes de Castro, Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications (book)

http://web.eecs.utk.edu/~mclennan Bruce MacLennan

http://bio.informatics.indiana.edu/bio/index.php Bioinformatics Program at Indiana University

https://sites.google.com/site/naturalcomputingaisbiacap2012 NATURAL/UNCONVENTIONAL COMPUTING AND ITS PHILOSOPHICAL SIGNIFICANCE @ AISB/IACAP, 2nd - 6th July 2012

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NATURAL COMPUTING RESOURCES

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Theoretical Computer Science, 2004 Volume:317 issue:1-3 Hypercomputation

Three aspects of super-recursive algorithms and hypercomputation or finding black swans Burgin, Klinger

Toward a theory of intelligence Kugel

Algorithmic complexity of recursive and inductive algorithms Burgin

Characterizing the super-Turing computing power and efficiency of classical fuzzy Turing machines Wiedermann

Experience, generations, and limits in machine learning Burgin, Klinger

Hypercomputation with quantum adiabatic processes Kieu

Super-tasks, accelerating Turing machines and uncomputability Shagrir

Natural computation and non-Turing models of computation MacLennan

Continuous-time computation with restricted integration capabilities Campagnolo

The modal argument for hypercomputing minds Bringsjord, Arkoudas

Hypercomputation by definition Wells

The concept of computability Cleland

Uncomputability: the problem of induction internalized Kelly

Hypercomputation: philosophical issues Copeland

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COURSE WRAP-UPHighlights

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REGULAR LANGUAGES

Mathematical PreliminariesLanguages, Alphabets and StringsOperations on Strings Operations on Languages Regular ExpressionsFinite AutomataRegular Grammars

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CONTEXT-FREE LANGUAGES Context-Free Languages, CFLPushdown Automata, PDAPumping Lemma for CFLSelected CFL Problems

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TURING MACHINES AND DECIDABILITY

Unrestricted GrammarsTuring MachinesDecidability

OTHER MODELS OF COMPUTATION

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EXAM

1. DFA & RE...minimal DFA

2. REGULAR OR NOT?

3. PUSH DOWN AUTOMATON (PDA)

4. CONTEXT FREE LANGUAGES

5. PRIMITIVE RECURSION

6. TURING MASCHINES

7. DECIDABLE OR NOT? JUSTIFY!

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The final exam is open-book/open-notebook which means you can have one book or

notebook of your choice with you.

If you have passed all midterms, you do not need to write final.If you did not pass one of them (Regular, Context-free or Turing Machines) you write only that part.

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THAT’S ALL FOLKS!

…so you should now be well on your way to finishing the course… good luck!

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REFERENCES

1. Peter Linz, An Introduction to Formal Languages and Automata

2. http://www.cs.rpi.edu/courses/ Models of Computation, C Busch

3. Lenart Salling, Formella språk, automater och beräkningar

4. http://www.cs.duke.edu/~rodger Mathematical Foundations of Computer Science; Susan H. Rodger; Duke University

5. Rich Elaine, Automata, Computability and Complexity: Theory and Applications, Prentice Hall