Merge branch 'master' of git.vanoverloop.xyz:Danacus/university-stuff

master
Daan Vanoverloop 3 months ago
commit db403d3f74
Signed by: Danacus
GPG Key ID: F2272B50E129FC5C
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- "Ze beschouwen de universele turingmachine als het meest passende model
voor allerlei door ingenieurs ontworpen producten (zoals iPhones, laptops, desktops...).".
Hoe moet ik dit interpreteren? Beweert men dat je een computer
moet ontwerpen zoals turingmachine (dus met een tape en een automaat)?
Of heeft men het puur over de functies die zo een machines kunnen berekenen?
- Is uw voorkeur voor tilde-notatie beïnvloed door uw filosofische zienswijze?
- Stel dat je een perfecte kloon van een mens zou kunnen maken, atoom voor atoom, heb je dan een intelligente computer gemaakt?
Is deze computer dan krachtiger dan een turingmachine?
- Programmeerbare machines, te vaag?
- Waarom Turing bewust in kamp A gepubliceerd terwijl hij in kamp B zat? -> Hilbertprogramma, beslisbaarheidsprobleem "oplossen"
- Hobson in kamp B maar toch pleitte hij voor een wetmatigheid? -> op constructieve wijze kan je enkel een deel van de wiskunde beschrijven
- Kunnen we veronderstellen of er toeval is of dat er gewoon ongekende waarden zijn? Bijvoorbeeld bij een dobbelsteen hangt dat niet af van de hoek waarmee we een dobbelsteen gooien.
- Waarom noemt Turing het een choice machine? Ik zie de choice niet in de machine.
- Is de choice machine van Turing een niet-deteministische turingmachine?
- ACE machines hebben een meester nodig (die een mens is), dus kunnen ze geen mens simuleren? Kan de meester zelf een ACE machine of turingmachine zijn?
- "Ze beschouwen de universele turingmachine als het meest passende model voor allerlei door ingenieurs ontworpen producten (zoals iPhones, laptops, desktops...).".
Hoe moet ik dit interpreteren? Beweert men dat je een computer moet ontwerpen zoals turingmachine (dus met een tape en een automaat)?
Of heeft men het puur over de functies die zo een machines kunnen berekenen?
- Wat is toch het voordeel om alles te bestempelen als zijnde een turingmachine? Kan je voorbeelden aanhalen waarin dat zo gebleken is? Wat vond Turing er zelf van?

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vocabulary V {
type Time isa nat
partial Next(Time) : Time
Start : Time
type Person
type Book
owns(Time, Person, Book)
I_owns(Person, Book) // Initial predicate for owns
C_owns(Time, Person, Book)
Cn_owns(Time, Person, Book)
give(Time, Person, Book, Person)
}
structure S : V {
Time = {0..5}
Person = { Bob; John; Mary; }
Book = { B1; B2; B3; }
// Initially
I_owns = { Bob,B1; John,B2; Mary,B3; }
}
theory timeTheo : V {
{
Start = MIN[:Time].
! t: Next(t) = t + 1 <- Time(t + 1).
}
}
theory T : V {
{
// Successor State Action Axiom & Inertia
! p[Person] b[Book]: owns(Start, p, b) <- I_owns(p, b).
! t[Time] p[Person] b[Book]: owns(Next(t), p, b) <- C_owns(t, p, b).
! t[Time] p[Person] b[Book]: owns(Next(t), p, b) <- owns(t, p, b) & ~Cn_owns(t, p, b).
}
{
// Expressing causations
// TODO: when is owns caused?
! t[Time] p[Person] b[Book]: C_owns(t, p, b) <- ?p2[Person]: give(t, p2, b, p).
// TODO: when is owns uncaused?
! t[Time] p[Person] b[Book]: Cn_owns(t, p, b) <- ?p2[Person]: give(t, p, b, p2).
}
// Preconditions
// - A person needs to own a book if he wants to give it away
! t[Time] p[Person] b[Book] p2[Person]: give(t, p, b, p2) => owns(t, p, b).
// - A book can be given to only one person
! t[Time] p[Person] b[Book] p2[Person] p3[Person]: give(t, p, b, p2) & give(t, p, b, p3) => p2 = p3.
// - A person cannot give a book to himself
!t [Time] p[Person] b[Book] p2[Person]: give(t, p, b, p2) => p ~= p2.
}
procedure findmodels() {
local timeExpanded = calculatedefinitions(timeTheo, S)
stdoptions.nbmodels = 5
printmodels(modelexpand(T, timeExpanded))
}
procedure simulate() {
stdoptions.nbmodels = 5
print("----> Starting interactive simulation")
local states = initialise(T, S)
// use one of those initial states to continue with (again and again and ...)
while true do
printmodels(states)
print("Please enter which state to use")
local num = tonumber(io.read("*line"))
if num == nil then
break
elseif num < 1 or num > 5 then
break
end
local chosen = states[num]
states = progress(T, chosen)
end
}
procedure main() {
// This method lets you walk through your solutions interactively
//simulate()
// This method (defined above) simply searches models of your theory
findmodels()
}

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vocabulary V {
type Time isa nat
type Position isa nat
type Remote constructed from { A, B }
partial Next(Time) : Time
Start : Time
// Actions
open(Time, Remote)
close(Time, Remote)
// Fluents
position(Time, Position)
I_position(Position)
C_position(Time, Position)
Cn_position(Time, Position)
opening(Time)
I_opening
C_opening(Time)
Cn_opening(Time)
closing(Time)
I_closing
C_closing(Time)
Cn_closing(Time)
}
theory timeTheo : V {
{
Start = MIN[:Time].
! t[Time]: Next(t) = t + 1 <- Time(t + 1).
}
}
structure S : V {
Time = {0..20}
Position = {0..5}
I_position = { 1 }
I_opening = { }
I_closing = { }
open = { 1, A; 12, B }
close = { 10, A }
}
theory T : V {
// Preconditions
// none?
{ // Successor State Axiom: position
!p [Position]: position(Start, p) <- I_position(p).
!t [Time] p [Position]: position(Next(t), p) <- C_position(t, p).
!t [Time] p [Position]: position(Next(t), p) <- position(t, p) & ~Cn_position(t, p).
}
{ // Sucessor State Axiom: opening
opening(Start) <- I_opening.
!t [Time]: opening(Next(t)) <- C_opening(t).
!t [Time]: opening(Next(t)) <- opening(t) & ~Cn_opening(t).
}
{ // Sucessor State Axiom: closing
closing(Start) <- I_closing.
!t [Time]: closing(Next(t)) <- C_closing(t).
!t [Time]: closing(Next(t)) <- closing(t) & ~Cn_closing(t) & ~C_opening(t).
}
{ // Causes
!t [Time] p [Position]: C_position(t, p + 1) <- position(t, p) & opening(t).
!t [Time] p [Position]: C_position(t, p - 1) <- position(t, p) & closing(t).
!t [Time] p [Position] p2 [Position]: Cn_position(t, p) <- C_position(t, p2) & p2 ~= p.
!t [Time]: C_opening(t) <- ?r [Remote]: open(t, r).
!t [Time]: Cn_opening(t) <- position(t, 5).
!t [Time]: C_closing(t) <- ?r [Remote]: close(t, r) & ~C_opening(t).
!t [Time]: Cn_closing(t) <- position(t, 1).
}
}
procedure simulate() {
stdoptions.nbmodels = 20
print("----> Starting interactive simulation")
local states = initialise(T, S)
// use one of those initial states to continue with (again and again and ...)
while true do
printmodels(states)
print("Please enter which state to use")
local num = tonumber(io.read("*line"))
if num == nil then
break
elseif num < 1 or num > 5 then
break
end
local chosen = states[num]
states = progress(T, chosen)
end
}
procedure main() {
//simulate()
print(onemodel(merge(T, timeTheo), S))
}

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vocabulary V {
type Time isa nat
partial Next(Time) : Time
Start : Time
type Jug
type Liters isa nat
maxContents(Jug) : Liters
// Actions
type Action constructed from { Fill(Jug), Transfer(Jug, Jug, Liters), Empty(Jug) }
do(Time, Action)
// Fluents
contents(Time, Jug) : Liters
I_contents(Jug) : Liters
C_contents(Time, Jug, Liters)
Cn_contents(Time, Jug, Liters)
/* Not the best idea...
C_add(Time, Jug, Liters)
Cn_add(Time, Jug, Liters)
C_sub(Time, Jug, Liters)
Cn_sub(Time, Jug, Liters)
*/
}
theory timeTheo : V {
{
Start = MIN[:Time].
! t[Time]: Next(t) = t + 1 <- Time(t + 1).
}
}
structure S : V {
Time = {0..6}
Jug = { J3;J5; }
Liters = {0..10}
maxContents = { J5->5; J3->3; }
I_contents = { J3->0; J5->0; }
}
theory T : V {
// Preconditions
// Do not fill self
! t[Time] j[Jug] k[Jug] l[Liters]: do(t, Transfer(j, k, l)) => j ~= k.
// Do not fill full jug (optional)
! t[Time] j[Jug]: do(t, Fill(j)) => contents(t, j) ~= maxContents(j).
// Do not empty empty jug (optional)
! t[Time] j[Jug] : do(t, Empty(j)) => contents(t, j) ~= 0.
// The number of liters should match the current situation
// Transfer no more than what's contained in the jug.
!t[Time] j[Jug] k[Jug] l[Liters]: do(t, Transfer(j, k, l)) => l =< contents(t, j).
// Transfer no more than what can be filled in the receiving jug.
! t[Time] j[Jug] k[Jug] l[Liters]: do(t, Transfer(j, k, l)) => l =< maxContents(k) - contents(t, k).
// You can only transfer l liters from a jug if that jug currently has l liters of content.
! t[Time] j1[Jug] j2[Jug] l[Liters]: do(t, Transfer(j1, j2, l)) => contents(t, j1) = l | l = maxContents(j2) - contents(t, j2).
{ // Successor State Axiom
!j [Jug]: contents(Start, j) = I_contents(j).
!t [Time] j [Jug] l [Liters]: contents(Next(t), j) = l <- C_contents(t, j, l).
!t [Time] j [Jug] l [Liters]: contents(Next(t), j) = l <- contents(t, j) = l & ~Cn_contents(t, j, l).
}
{ // Causes
! t[Time] j[Jug] l[Liters] m[Liters]: Cn_contents(t, j, l) <- C_contents(t, j, m) & l ~= m.
!t [Time] j [Jug]: C_contents(t, j, maxContents(j)) <- do(t, Fill(j)).
!t [Time] j [Jug]: C_contents(t, j, 0) <- do(t, Empty(j)).
!t [Time] j [Jug] l [Liters]: C_contents(t, j, contents(t, j) + l) <- ?j2 [Jug] m [Liters]: do(t, Transfer(j2, j, m)) & l = contents(t, j) + m.
!t [Time] j [Jug] l [Liters]: C_contents(t, j, contents(t, j) + l) <- ?j2 [Jug] m [Liters]: do(t, Transfer(j, j2, m)) & l = contents(t, j) - m.
}
! t[Time]: ?=1 a[Action]: do(t, a).
//!t [Time] j [Jug]: contents(t, j) =< maxContents(j).
?t [Time] j [Jug]: contents(t, j) = 4.
}
procedure simulate() {
stdoptions.nbmodels = 20
print("----> Starting interactive simulation")
local states = initialise(T, S)
// use one of those initial states to continue with (again and again and ...)
while true do
printmodels(states)
print("Please enter which state to use")
local num = tonumber(io.read("*line"))
if num == nil then
break
elseif num < 1 or num > 5 then
break
end
local chosen = states[num]
states = progress(T, chosen)
end
}
procedure main() {
//simulate()
print(onemodel(merge(T, timeTheo), S))
}

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vocabulary V {
type Time isa nat
partial Next(Time) : Time
Start : Time
type Player constructed from { A, B }
type Matches isa int
type Heap constructed from { P1, P2, P3, P4 }
// Actions
takes(Time, Player, Heap, Matches)
// Fluents
nb(Time, Heap, Matches)
I_nb(Heap, Matches)
C_nb(Time, Heap, Matches)
Cn_nb(Time, Heap, Matches)
turn(Time, Player)
winner(Time, Player)
gameOver(Time)
}
theory timeTheo : V {
{
Start = MIN[:Time].
! t[Time]: Next(t) = t + 1 <- Time(t + 1).
}
}
structure S : V {
Time = {0..20}
I_nb = { P1, 1; P2, 3; P3, 5; P4, 7 }
}
theory T : V {
// Preconditions
// The players take a number of matches away from a heap:
// at least one and at most as much as there are in the heap.
!t [Time] p [Player] h [Heap] m [Matches]: takes(t, p, h, m) => m >= 1 & ?m1 [Matches]: nb(t, h, m1) & m1 >= m.
// If it is your turn, you need to take some amount of matches from some heap
!t [Time] p [Player]: turn(t, p) => ?h [Heap] m [Matches]: takes(t, p, h, m).
// Only take matches when it is your turn
!t [Time] p [Player] h [Heap] m [Matches]: takes(t, p, h, m) => turn(t, p).
// Only take matches from one heap at a time
!t [Time] p [Player] h [Heap] m [Matches]: takes(t, p, h, m) =>
~?h2 [Heap] m2 [Matches]: h2 ~= h & takes(t, p, h2, m2).
!t [Time] p1 [Player] p2 [Player]: turn(Next(t), p1) & turn(t, p2) => p1 ~= p2.
!t [Time] p1 [Player] p2 [Player]: turn(t, p1) & turn(t, p2) => p1 = p2.
{ // Successor State Axiom
!m [Matches] h [Heap]: nb(Start, h, m) <- I_nb(h, m).
!t [Time] m [Matches] h [Heap]: nb(Next(t), h, m) <- C_nb(t, h, m).
!t [Time] m [Matches] h [Heap]: nb(Next(t), h, m) <- nb(t, h, m) & ~Cn_nb(t, h, m).
}
{ // Causes
!t [Time] h [Heap] m [Matches] d [Matches]: C_nb(t, h, m) <-
nb(t, h, m + d) & ?p [Player]: takes(t, p, h, d).
!t [Time] h [Heap] m [Matches]: Cn_nb(t, h, m) <-
?m2 [Matches]: C_nb(t, h, m2) & m ~= m2.
!t [Time] p [Player]: winner(t, p) <-
?o [Player] h [Heap] m [Matches]: o ~= p & takes(t, o, h, m) & C_nb(t, h, 0).
!t [Time]: gameOver(t) <- ?p [Player]: winner(t, p).
}
turn(Start, A).
}
procedure simulate() {
stdoptions.nbmodels = 20
print("----> Starting interactive simulation")
local states = initialise(T, S)
// use one of those initial states to continue with (again and again and ...)
while true do
printmodels(states)
print("Please enter which state to use")
local num = tonumber(io.read("*line"))
if num == nil then
break
elseif num < 1 or num > 5 then
break
end
local chosen = states[num]
states = progress(T, chosen)
end
}
procedure main() {
//simulate()
print(onemodel(merge(T, timeTheo), S))
}

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vocabulary V {
type Time isa nat
Start : Time
partial Next(Time) : Time
type Plate
// Fluents
clean(Time, Plate)
I_clean(Plate)
C_clean(Time, Plate)
Cn_clean(Time, Plate)
// Actions
cleanPlate(Time, Plate)
usePlate(Time, Plate)
}
structure S : V {
Time = {0..5}
Plate = { P1; P2; P3; P4; }
I_clean = { P1; P2; P3; P4; }
}
theory timeTheo : V {
{
Start = MIN[:Time].
! t[Time]: Next(t) = t + 1 <- Time(t + 1).
}
}
theory T : V {
// Preconditions
! t[Time] p [Plate]: cleanPlate(t, p) => ~clean(t, p).
! t[Time] p [Plate]: usePlate(t, p) => clean(t, p).
{ // Successor State Axiom
!p [Plate]: clean(Start, p) <- I_clean(p).
!t [Time] p [Plate]: clean(Next(t), p) <- C_clean(t, p).
!t [Time] p [Plate]: clean(Next(t), p) <- clean(t, p) & ~Cn_clean(t, p).
}
{ // Causes
!t [Time] p [Plate]: C_clean(t, p) <- cleanPlate(t, p).
!t [Time] p [Plate]: Cn_clean(t, p) <- usePlate(t, p).
}
// (Maybe, you can only clean one plate at a time?)
!t [Time] p1 [Plate] p2 [Plate]: cleanPlate(t, p1) & cleanPlate(t, p2) => p1 = p2.
!t [Time] p1 [Plate] p2 [Plate]: usePlate(t, p1) & usePlate(t, p2) => p1 = p2.
// There is always an extra clean plate
!t [Time]: ?p [Plate]: clean(t, p).
}
theory allDirty : V {
// Is it possible to make all your plates dirty?
?t [Time]: !p [Plate]: ~clean(t, p).
}
include <mx>
procedure main() {
S = calculatedefinitions(timeTheo, S)
local allconstraints = merge(T, allDirty)
print("This is the merged theory:")
print(allconstraints)
print("Searching for a model")
print(onemodel(allconstraints, S))
}

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\documentclass{sareport}
\usepackage{saquestions}
\usepackage{hyperref}
\usepackage{cleveref}
\addAuthor{FirstName3}{LastName3}{r000000}
\addAuthor{FirstName1}{A LastName1}{m000000}
\addAuthor{FirstName2}{B LastName2}{s000000}
\casename{Patient Monitoring Service (\textsc{pms})}
\phasenumber{Part 2}
\phasename{Architecture evaluation}
%\academicyear{20xx--20xx}
\begin{document}
\maketitle
\chapter*{Analysis and Evaluation}
\answerQ{00}
{%
Your answer to the question.
\todoinline{answer}
}{%
Pointers to the evidence that you've used to answer the question (figure numbers, section numbers, etc.), as well as assumptions that you've made but that aren't in the architectural description.
}{%
Your opinion on how the aspect you've investigated is handled in the current architecture, and possible improvements you'd propose.
}
\section*{E1 Design Extension}
\subsection*{Design Decisions}
\subsection*{Discussion}
% Add the necessary diagrams, don't forget to refer to them in your text, use \ref{label} or \cref{label}.
\begin{figure}
\centering
\includegraphics[]{example-image-a}
\caption{\label{fig:diagram}DiagramType and name}
\end{figure}
\end{document}

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\NeedsTeXFormat{LaTeX2e}
\ProvidesPackage{saquestions}[2021/03/08 Answering SA Questions]
\RequirePackage{amssymb}
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\newcommand{\markdiff}[1]{\multido{}{#1}{$\bigstar$}\,}
\DeclareDocumentCommand{\answerQ}{ O{0} O{} m m m m}{%
\noindent\textbf{Q#3} \markdiff{#1} #2
\begin{itemize}[noitemsep]
\item \textbf{Answer:} #4
\item \textbf{Evidence and assumptions:} #5
\item \textbf{Opinion and improvements:} #6
\end{itemize}%
}

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\note{This section discusses \emph{all} your architectural decisions
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First, \emph{all} decisions related to the non-functionals are
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and discussed.
}
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\smallskip
\hint{No need to just repeat what we can see on the diagram.
Don't do this: \emph{As you can see on fig. x: comp A consists
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But, please do explain if there is anything non-trivial (e.g., a
custom mapping from actors to external components on the
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\hint{No need to just repeat what we can see on the diagram.
Don't do this: \emph{As you can see on fig. x: comp A consists
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But, please do explain if there is anything non-trivial
(e.g., a custom mapping from actors to external components on
the context diagram).
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\smallskip
\hint{No need to just repeat what we can see on the diagram.
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But, please do explain if there is anything non-trivial (e.g., a
custom mapping from actors to external components on the context
diagram).
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\hint{No need to just repeat what we can see on the diagram.
Don't do this: \emph{As you can see on fig. x: component A calls
operation b, next component C calls operation d}.
But, please do explain if there is anything non-trivial (e.g., a
custom mapping from actors to external components on the context
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}
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happens when a certain component fails (Av) or overloads (P)?
Use the sequence diagrams to illustrate how you have achieved
the qualities in your architecture.
}
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}
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You can use the \texttt{\textbackslash componentItem\{name\}
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Note that you cannot use newlines in the componentItem content,
but you can use \textbackslash\textbackslash.
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}
\smallskip
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so you don't have to repeat what they mean for each operation
that can throw them.
}
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\smallskip
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