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--git a/mystyle.sty b/mystyle.sty index d98ad7e..e191908 100644 --- a/mystyle.sty +++ b/mystyle.sty @@ -1,6 +1,6 @@ \usepackage{geometry} \geometry{ hmargin=2cm, vmargin=3cm } -\linespread{1.5} +\linespread{1.4} \usepackage{multirow} \usepackage{array} @@ -44,13 +44,13 @@ % \titleformat{⟨command⟩}[⟨shape⟩]{⟨format⟩}{⟨label⟩}{⟨sep⟩}{⟨before⟩}[⟨after⟩] \titleformat{\chapter} - {\Huge\sffamily\scshape} + {\fontsize{23}{25}\sffamily\scshape} {\color{chaptercolor}\fontsize{50pt}{50pt}\selectfont\textbf{\thechapter}} {0em} {\hfill} [\color{gray95}{\titlerule[2mm]}] \titleformat{name=\chapter,numberless} - {\Huge\sffamily\scshape} + {\fontsize{23}{25}\sffamily\scshape} {} {0em} {\hfill} @@ -98,7 +98,7 @@ {} %\titlespacing*{hcommandi}{hlefti}{hbeforesepi}{haftersepi}[hrighti] -\titlespacing\section{0pt}{10pt plus 4pt minus 2pt}{0pt plus 2pt minus 2pt} +\titlespacing\section{0pt}{10pt plus 4pt minus 2pt}{10pt plus 2pt minus 2pt} \titlespacing\subsection{0pt}{10pt plus 4pt minus 2pt}{0pt plus 2pt minus 2pt} \titlespacing\subsubsection{0pt}{10pt plus 4pt minus 2pt}{0pt plus 2pt minus 2pt} \titlespacing\subsubsection{0pt}{10pt plus 4pt minus 0pt}{0pt} @@ -341,3 +341,21 @@ runcolor=., urlcolor=linkcolor } + +\usepackage{tikz} +\newcommand{\modk}[1]{\tikz[baseline=(X.base)]\node [draw=gray!50,fill=gray!10,semithick,rectangle,inner sep=2pt, rounded corners=2pt] (X) {\tt\small#1};} + +%\newcommand{\modk}[1]{{\tt\small#1}} +%\newcommand{\ctrl}{{\tt\small Ctrl}\xspace} +\newcommand{\ctrl}{\modk{Ctrl}\xspace} +%\newcommand{\cmd}{{\tt\small Cmd}\xspace} +\newcommand{\cmd}{\modk{Cmd}\xspace} +%\newcommand{\shift}{{\tt\small Shift}\xspace} +\newcommand{\shift}{\modk{Shift}\xspace} +%\newcommand{\alt}{{\tt\small Alt}\xspace} +\newcommand{\alt}{\modk{Alt}\xspace} +%\newcommand{\caps}{{\tt\small Caps lock}\xspace} +\newcommand{\caps}{\modk{Caps lock}\xspace} +%\newcommand{\tab}{{\tt\small Tab}\xspace} +\newcommand{\tab}{\modk{Tab}\xspace} + diff --git a/plain-withurls.bst b/plain-withurls.bst index e0aaed7..f375ab3 100644 --- a/plain-withurls.bst +++ b/plain-withurls.bst @@ -581,6 +581,40 @@ FUNCTION {article} fin.entry } +% TOM +FUNCTION {patent} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + new.block + + % NICOLAS + doi empty$ + { url empty$ + { format.btitle } + { "\href{" url * "}{" * format.btitle * "}" * } + if$ + } + { "\href{http://doi.org/" doi * "}{" * format.btitle * "}" * } + if$ + "title" output.check + %format.btitle "title" output.check + + number output + format.date "year" output.check + new.block + note output + fin.entry +} + + FUNCTION {book} { output.bibitem author empty$ diff --git a/tex/evolutions.tex b/tex/evolutions.tex index 6dbe321..9d30f6c 100644 --- a/tex/evolutions.tex +++ b/tex/evolutions.tex @@ -1,34 +1,130 @@ %!TEX root = ../hdrmain.tex -\chapter{Evolutions of interaction} +\chapter{Reshaping foundations of interaction} \epigraph{\lorem}{Auteur} \begin{Abstract} \loremipsum \end{Abstract} +\section{Introduction} + +\section{Revisiting the desktop} + +Desktop interaction initiated personal computing. +Used for work, education, entertainment. +Most of the concepts established before it became mainstream. +\begin{itemize} + \item screen + \item keyboard + \item mouse + \item windows + \item menus + \item undo/redo + \item copy/paste +\end{itemize} + + +\subsection{CtrlMouse and TouchCtrl: duplicating mode switchers on the mouse} + +Command selection and mode switching are fundamental interactions that existed even before GUIs. +The first keyboard used as a direct input on a computer was a \defword{Friden Flexowriter}\footnote{\url{https://en.wikipedia.org/wiki/Friden_Flexowriter}} teleprinter, connected to MIT's \defword{Whirlwind I}\footnote{\url{https://en.wikipedia.org/wiki/Whirlwind_I}} computer in 1956~\cite{bardini00}. +This is eight years after the computer construction started. +The \ctrl key was initially used to reset the first two bits of ASCII characters, giving access to commands encoded in the first 32 ASCII characters. +For example, one can type \ctrl~+~\modk{h} for a backspace, or \ctrl~+~\modk{j} for a line feed. +These commands are still used in terminal communications, represented \verb+^H+ or \verb+^J+ for example. +However the use of the \ctrl key in GUIs evolved to other commands that do not exist in the ASCII table. +The cut/copy/paste commands is a typical set of commands, usually mapped to \ctrl~+~\modk{x}/\modk{c}/\modk{v}. +We remark tough that some keyboards, such as those of Sun workstations had dedicated keys for typical commands such as cut/copy/paste. +However it is obviously not possible to have dedicated keys for all possible commands. +Therefore in text-based applications, in which the keyboard is primarily used for typing text, the \ctrl key behaves as a \defword{mode switcher}. +It enables switching between the text entry mode, and the command selection mode. +We assume that the fact that such mode switchers are located on the keyboard is essentially due to historical reasons described above. + +The computer mouse was first publicly demonstrated by Englebart in 1968, commercialized with the \defword{Xerox Alto} in 1973. +At this time, the mouse behavior essentially consisted in selecting pointed objects or invoking \defword{contextual menus} (called pop-up menus back then). +Scrolling only came up about ten years later~\cite{ohno85,venolia93,venolia94}. +Command selection with the mouse usually relies on actions associated to selected objects: icons, menu items, or manipulated objects. +Even advanced interaction techniques such as Toolglasses~\cite{bier93} consist in selecting menu items. +The UNIA\footnote{\url{https://www.microsoft.com/buxtoncollection/detail.aspx?id=150}} mouse was likely the first one to enable direct command selection, without mediator. +It has a numeric keypad, as well as modifier keys under a removable cover. +This disposition makes it difficult to both use the device as a mouse, and use the additional keys at the same time. + + + +The PadMouse + +~\cite{balakrishnan98} +It consisted of a combination of a mouse and a touchpad. +With this device, the user could activate modifiers or select command through a marking menu with its touchpad. +In the last decades, commercial mice included additional mouse buttons located at the vicinity of the thumb. +These additional buttons are typically used for navigating to the previous/next page in a browser, or trigger actions in video games. + +\begin{figure}[htb] + \centering + \setlength{\fboxsep}{0pt}% + \setlength{\fboxrule}{1pt}% + \fbox{\includegraphics[height=4.1cm]{touchctrl}} + \hspace{0.3cm} + \fbox{\includegraphics[height=4.1cm]{ctrlmouseOn} + \includegraphics[height=4.1cm]{ctrlmouseOff}} + \caption{We explore the impact of duplicating modifier keys on the mouse. \emph{CtrlMouse} (left) mirrors the \protect\ctrl and \protect\shift keys on the mouse to delimit text entry and command selection and reduce the needs of coordination of the left hand. \emph{TouchCtrl} (right) implicitly triggers the command mode when the hand hovers the mouse.} + \label{figure:ctrlmouse} +\end{figure} + +In the CtrlMouse project, we studied the idea of duplicating the \ctrl and \shift keys on a mouse~\cite{pietrzak14}. +By doing so, we would like to reconsider the position of mode switchers. + +\begin{figure}[htb] +\centering +\includegraphics[height=3.2cm]{_nopress.pdf} +\includegraphics[height=3.2cm]{_singlepress.pdf} +\includegraphics[height=3.2cm]{_dualpress.pdf} +%\thomas{We can make this smaller} + \caption{\emph{CtrlMouse} mirrors modifiers on the mouse (left). Users can either press \protect\ctrl (middle) or \protect\ctrl+\protect\shift (right) thanks to a simple small amplitude thumb movement. Users don't have to home to keyboard anymore to perform complicated keyboard shortcuts.} + \label{fig:ctrlmouse} +\end{figure} -\section{Extending the computer desktop} -\subsection{CtrlMouse: cuplicating mode switchers on the mouse} - -Ctrl Mouse \cite{pietrzak14} \subsection{Métamorphe: a keyboard with actuated keys} Métamorphe \cite{bailly13} +Keyboard keys have a limited vocabulary. +They are either pressed or released. +We actuated keys so that they can be up or down. +In addition to the additional output modality, in enables new interactions with the sides of the keys: pushes and pinches. + \subsection{Living Desktop: actuated peripherals} Living Desktop \cite{bailly16} +Beyond the digital desktop, we extend the physical desktop. +Besides being input and output devices, computer peripherals are tangible objects. +We not only manipulate them the way they were designed to be used, but also in other ways. +We explored other uses, and how actuating computer peripherals could ease such tasks. -\section{Extending interaction paradigms} +\section{Revisiting direct manipulation} \subsection{Direct Manipulation in tactile displays} Direct Manipulation~\cite{gupta16} +Direct manipulation is one of the most fundamental foundation of GUIs~\cite{schneiderman83}. +It provides valuable usability benefits that highly contributed to the success of GUIs over command line interfaces. +Yet, the description of this paradigm relied essentially on visual cues. +In this project we explained how to adapt the concept of direct manipulation to tactile displays. + \subsection{Summon interactions} Summon interactions~\cite{gupta17} + +3D gestural interaction in the air faces difficult challenges due to the contact-less nature of this interaction style. +The absence of physical buttons, or other unambiguous activation action makes it difficult to segment gestures. +As a consequence, the system constantly interprets the users' actions. +Therefore the user is always potentially interacting with the environment. +This is known as the Midas touch problem, as a reference to the Midas king in the greek mythology, who turned everything he was touching to gold. + + +\section{Conclusion} diff --git a/tex/introduction.tex b/tex/introduction.tex index cdb84c1..cca40fa 100644 --- a/tex/introduction.tex +++ b/tex/introduction.tex @@ -6,411 +6,7 @@ \addcontentsline{lof}{chapter}{\nameref{chap:intro}} \epigraph{\lorem \lorem \lorem}{Auteur} -When I was a teenager, I was fascinated by computers, much more than I am today. I started to program early, and at this time I thought it was possible to create a program for everything. I believed the only limit of computer capabilities was the programmer's ability to code it. My limit was… pointers at this time. I wanted to create a program that was able to have a relevant conversation with me. I learned later that many people were working on this for decades. I tried the ELIZA system~\cite{weizenbaum66}, and I was not satisfied with it. It was just repeating what I was saying, and used a small set of expressions to ask me to make the conversation. Most of the time it spotted the right word in my sentence, but sometimes not and what it was saying just did not make any sense. It clearly failed the famous Turing test~\cite{turing50}, I knew about many years later. I think I wanted the program to understand what I write, without having hard coded key sentences or all grammar rules. Nobody speaks like a grammar book anyway. Naively, I wanted to write a better program. However I did not want to code associations between concepts, words, metaphors and so on. I knew this was a huge work, that would take me several lives to achieve, and that when I will be finished, half of it will be obsolete. In fact I had no idea how to start. -\section{Artificial Intelligence} - -Later, when I was a computer science student, I followed \defword{Artificial Intelligence} (AI) courses. I learned about classical AI techniques, such as \defword{expert systems}~\cite{lindsay80}, the \defword{Perceptron}~\cite{rosenblatt57} and \defword{neural networks}~\cite{mcculloch43}. It reminded my teenager days. These were of course more advanced than anything I made myself. However I felt disappointed because none of these techniques seemed to be sufficient for creating a system that passes the Turing Test. In fact seemed to be the general opinion in the scientific community, mostly due to overstatements from pioneer researchers in AI: - -\myquote{Within ten years a digital computer will be the world’s chess champion.}{Herbert Simon and Allen Newell, 1958} - -The first time a chess-playing computer won a game against a world champion was Deep Blue against Garry Kasparov in 1996\footnote{\url{https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)}}. -However Kasparov won the match 4-2. -The computer won the next year $3\frac{1}{2}$ - $2\frac{1}{2}$. -It is however only recently that computers clearly beat world-class chess or go players, with AlphaZero~\cite{silver18}, thanks to the recent advances in Deep learning~\cite{lecun15}, which resurrected neural networks techniques. - -\myquote{Machines will be capable, within twenty years, of doing any work a man can do.}{Herbert Simon, 1965} - -This is still far from being true today. Machines still have to be programmed for specific tasks, and they have little knowledge and no understanding of their environment. This makes machines hardly adaptable to “any work a man can do”. - -\myquote{Within a generation, the problem of creating ‘artificial intelligence’ will substantially be solved.}{Marvin Minsky, 1967} - -The definition of “intelligence” or “artificial intelligence” is far from being consensual. -Despite the tremendous progress of Machine learning in the past years, I would argue that computers are still far from being intelligent as we would measure intelligence of a human being. -Dessalles says that despite their capabilities, computers do not even know they are intelligent~\cite{dessalles19}. -Neural networks are still tied to a particular task, and are unable to evolve in order to solve different problems. -They still require humans to design them and train them. - -%\myquote{In from 3 to 8 years, we will have a machine with the general intelligence of an average human being.}{Marvin Minsky, 1970} - -%Not only it did not happen in the worst case scenario predicted by Minsky. But it is still not the case today.\\ - -%http://biology.kenyon.edu/slonc/bio3/AI/TIMELINE/timeline.html -%1943: McCulloch and Pitt propose neural-network architectures for intelligence. -%1950: Turing Test proposed (Turing's "Computing Machinery and Intelligence") -%1959: Rosenblatt introduces Perceptron. -%1965: Simon predicts, "by 1985 machines will be capable of doing any work a man can do" -%1965: Buchanan, Feigenbaum & Lederberg begin DENDRAL expert system project. - -\section{Logics} - -Later on in my studies, I became fascinated by logics and software proofs. -I learned $\lambda$-calculus~\cite{church32} and Turing machines~\cite{turing38}, which are the foundations of computing. -These concepts define the notions of computation, calculability and programming language. -In the late 30', Turing defined a \defword{computable function} as follows: - -\begin{definition}{Computable function} -We shall use the expression \defword{computable function} to mean a function calculable by a machine, and let “effectively calculable” refer to the intuitive idea without particular identification with any one of these definitions.~\cite{turing38} -\end{definition} - -Therefore, computable functions refer to programs (\defword{Turing machines} (TM), \defword{$\lambda$-calculus} ($\lambda$-c) or any \defword{Turing-complete} formalism) and effectively calculable functions are algorithms. -The Church-Turing thesis states that every effectively calculable function is a computable function. -To put it simply, there is a computer program for every algorithm. -%This may seem straightforward now that we have billions of examples useful and working computer programs. -%However it states an equivalence between theoretical models such as Turing machines or $\lambda$-calculus and what can be computer on a physical computer. -%It enables the theoretical analysis of computing problems with one of these models. -Turing showed the limits of calculability though. -He proved there is no algorithm that can predict if running any program on any input finishes or runs forever~\cite{turing36}. -This is known as the halting problem, and was defined by Hilbert~\cite{hilbert28}. -The idea of the proof is that such an algorithm would take a program and an input as an input, and return 1 if this program finishes, 0 otherwise. -This program would need to emulate this program on this input. -If the program finishes, this algorithm would effectively return 1. -Otherwise the emulation would run forever, and the algorithm would never return 0. -In the 50', Tarski \etal describe other undecidable theories, such as the first-order theory of natural numbers~\cite{tarski53}. -This is a strong reason why still today, difficult proofs are made by humans, not computers. -When the theorem to be proved or hypotheses have quantified variables, substitution possibilities are infinite. -With thinking, observation of available hypothesis, instinct, and sometimes luck, humans can make progress in the proof. -The question arises: since there are problems no algorithm can solve, is there another way to solve these problems? - -%If this is the case, there is something interesting happening beyond the Church-Turing thesis. -% Church and Turing showed these two models are equivalent in the sense they compute the same things, which (in short) leaded to the Church-Turing thesis. - -\section{Human-Computer Interaction} - -After such a theoretical background, I made a $90^\circ$ turn, and started research with a master internship, then a PhD, in \defword{Human-Computer Interaction} (HCI). -My work consisted in finding how to use haptics to encode information for non-visual interaction~\cite{pietrzak05b,pietrzak08}. -There was unfortunately no relation whatsoever with theoretical computer science at that point. -However a first contact was made in 2006, when I attended a talk by Beaudouin-Lafon, entitled “Interaction is the future of computing”\footnote{\href{https://rjc06.afihm.org/}{RJC IHM 2006}, \url{https://rjc06.afihm.org/supports/MBLRJC-AFIHM2006.pdf}}. -As I still knew little about HCI, I was struck by thunder. -He exposed three fascinating visions: -\begin{itemize} - \item Wegner's \defword{interactive computation}~\cite{wegner97}. - \item Engelbart's vision of computers that extend human capabilities~\cite{engelbart68}. - \item His own \defword{instrumental interaction}~\cite{mbl00}. -\end{itemize} - -\subsection{Wegner} - -Wegner refutes the strong Church-Turing thesis, which states that TMs can do anything a computer can do~\cite{goldin08}. -He proves it by showing that interaction has a greater expressiveness than algorithms, and extends the original Church-Turing thesis to \defword{interactive computation}~\cite{goldin06}. -Knuth specifies \defword{algorithms} as as follows~\cite{knuth68}:%return a specified result given predefined inputs. - -\begin{itemize} - \item They always terminate after a finite number of steps. - \item Every of their steps must be precisely defined. - \item They have zero or more inputs taken from specified sets of objects, specified before the algorithm begins, or dynamically as it runs. - \item They provide one or more outputs, which have a specified relation to the inputs. - \item All of its operations can be done in a finite length of time using a pen and paper. -\end{itemize} - -With this definition, we observe that algorithms cannot deal with infinite input streams. -They operate on predefined specifications that cannot evolve during computation. -%This means algorithms are isolated from the outside world during computation. -At the opposite, interaction is a paradigm that observes the environment continuously. -Wegner formalizes the concept of \defword{interaction machines} and proves it is more expressive than TMs~\cite{wegner99}. - -\myquote{The equivalent expressiveness of TMs, algorithms, computable functions, and formal systems is due not to their maximal expressiveness but to their common reliance on induction for computation and reasoning.}{Peter Wegner~\cite{wegner99}} - -Instead of finite set of inputs, interaction machines are connected to single (SIM) or multiple (MIM) data streams. -Their expressiveness in not defined by induction, but respectively by non-well founded sets and co-induction. -Induction (hence algorithms, TMs, $\lambda$-c, \dots) outputs are recursively enumerable while non-well founded sets and co-induction outputs are non-enumerable~\cite{gordon94,chlipala13}. -This proves that interaction has a greater expressiveness than algorithms, allowing Wegner to extend the Church-Turing thesis for interaction machines. Turing himself defined other paradigms with more capabilities than TM: choice machines~\cite{turing36} and oracle machines~\cite{turing38}: - -\begin{definition}{Choice Machine} -For some purpose we might use machines (choice machines or \defword{c-machines}) whose motion is only partially determined by the configuration. When such a machine reaches one of these ambitious configurations, it cannot go on until some arbitrary choice has been made by an external operator.~\cite{turing36} -\end{definition} - - -C-machines clearly define interaction, with the possibility for the environment to influence computation. -However Turing does not give further details, and focuses on automatic machines (a-machines, later dubbed TM). -Later, Turing introduced the concept of \defword{oracle}, an agent that can solve any problem, and o-machines, which are TM that use oracles to perform part of their computation. -This is an evolution of c-machines, but there is not much details. - -\begin{definition}{Oracle Machine} -Let us suppose that we supplied with some unspecified means of solving number theoretic problems; a kind of oracle as it were. We will not go any further -into the nature of this oracle than to say that it cannot be a machine; with the -help of the oracle we could form a new kind of machine (call them \defword{o-machines}), -having as one of its fundamental processes that of solving a given number theoretic problem.~\cite{turing38} -\end{definition} - - -%Now, Interaction as described by Wegner includes every paradigm that can handle infinite streams, including Object Oriented programming or Neural Networks for example. -% is more powerful than algorithms\cite{wegner95,wegner97}. -As an example of his interaction machines, Wegner describes one that simply echoes an input stream to an output stream~\cite{wegner97}. -By doing so, this machine wins half of chess games by echoing two simultaneous games one to the other. -Such a performance is inaccessible to algorithms, because they only take into account the current configuration to compute the next play. -They are not aware of future plays, and therefore can only estimate the future as possibilities. -In fact, except for the last play, the algorithm will never see the end of the game, not even the next play, but only the current play. -Another execution of the algorithm, with the new configurations will handle next plays, ignoring this play even happened. -At the opposite, the interaction machine plays the whole game from start to finish, and relies on its ability to react to every play. - -%From the opponent point of view, this machine is playing chess, up to the best chess players in the world level. -By using this mechanism, interaction machines sort of include external behavior, just like Turing's o-machines. -By extending this example with conversations, it is obvious that Wegner's echo interaction machine passes the Turing Test, twice at the same time. -Therefore, more than just observing the environment with sensors or probes, it can absorb any external computing or thinking power. -It is only limited by what it observes, and how this observation is encoded. - -The trade-off is that interaction machines are not made for finding optimal solutions to well specified problems, but satisfying solutions to evolving problems. -This is precisely the difference between rationalism and empiricism. - -%Wegner presents interaction as a way to extend algorithms with external computation of some sort, with his interactive machines. - -\subsection{Englebart} - -Englebart's vision stems from Vannevar Bush's work on the Memex, % as he was coordinating scientific research in the United States during the second world war. -the idea of extending human memory with an electronic library~\cite{bush45}. -It comprises a mechanism to store books and notes, an indexing system that allows searching documents, and a way to bookmark documents. -Hypertext, computer networks and search engines are directly inspired by this vision. -Between 1962 and 1968, at a time computer were a room size, Englebart pushed this vision forward. -His vision was to use computers to extend human capabilities. -In his famous 1968 demo\footnote{The mother of all demos: \url{https://youtu.be/yJDv-zdhzMY}}, he showcased: a collaborative real-rime word processing editor, the computer mouse, a chorded keyboard, a windowing system, hypertext, file sharing, file annotating, video conferencing and revision control among others. -It was not only mesmerizing because it was a huge list of striking ideas that seem coming from another planet, but also because it was actually functioning live, contrary to the Memex which remained a concept. -The reason why it was not only a technology push forward, but the creation of a whole new scientific area is that computers were not anymore just computing machines, but interactive machines that help us in our everyday tasks. -This vision changed the way we work, or our entertainments. -Most of our activities today use an interactive system at some point. -Along with Sutherland's work on Sketchpad~\cite{sutherland63}, and of course Bush's Memex that inspired them, these are the foundations of Human-Computer Interaction. - -\subsection{Beaudouin-Lafon} - -Beaudouin-Lafon defined the Instrumented interaction paradigm enables users to interact with domain objects~\cite{mbl00}. -To do so, the user performs actions on the instrument, which provide the user an immediate feedback, and perform a command on the domain object. -As a consequence, the domain object returns a response to the user. -The immediate feedback provides high level of control thanks to a fast perception/action cycle. -The response to the command lets the user know the result of his actions, and allows him to continue or correct errors where necessary. -%Instruments are tools that -Instrumented interaction is an extension of Schneiderman's \defword{direct manipulation}~\cite{schneiderman83}. -\begin{definition}{Direct Manipulation} -The concept of \defword{direct manipulation} consists of design principles for user interfaces that favors usability. -Objects of interest must be visible and directly manipulable, actions must be rapid, reversible and incremental. -\end{definition} - -In the early 80's these principles made \defword{Graphical User Interfaces} (GUI) much more powerful than command-line interfaces in many situations. -Unsurprisingly, they are similar or related to Nielsen's heuristics for the evaluation of user interfaces~\cite{nielsen90}. -Instrumented interaction consist in the direct manipulation of instruments, which in turn execute commands on the objects of interest. -It is a direct inspiration of the use of instruments and tools in the real world. -% The link between instrumented interaction and direct manipulation is made clear through Beaudouin-Lafon and Mackay's principles of \defword{reification}, \defword{polymorphism} and \defword{reuse}~\cite{mbl00a}. - -% They define \defword{reification} as “the process by which concepts are turned into objects” -% %Instrumented Interaction is an instance of the principle of \defword{reification}, which Beaudouin-Lafon and Mackay define as “the process by which concepts are turned into objects”~\cite{mbl00a}. -% Instrumented interaction is the reification of commands, which enables users to run commands on objects by interacting with instruments; typically widgets on a GUI. -% Instruments are designed to be visible and directly manipulable. - -% %Most of objects of a GUI, if not all, are reifications of a real world concept. -% They also define the principle of \defword{polymorphism} in the context of HCI as “the property that enables a single command to be applicable to objects of different types”. -% This property is similar to the concept of polymorphism in programming languages. -% %Similarly to the same concept in programming languages, it is a powerful tool for the design of user interfaces. -% It enables reducing and grouping commands, which is useful in complex software that can propose thousands of commands. -% %%Something facilitate mapping -% [instruments and reversibility through polymorphism?] - -% , and \defword{reuse} “makes previously-provided input available for reuse in the current context”. - -% [instruments and rapid + incremental through reuse?] - -Instrumented interaction and direct manipulation are examples of the first of Beaudouin-Lafon's interaction paradigms: \emph{computer-as-a-tool}, the others being \emph{computer-as-a-partner} and \emph{computer-as-a-medium}~\cite{mbl04}. - -A partner system is the reification of ? - -\cite{mbl06} - -\section{My stuff} - -The idea of computers replacing humans for every task is futile. -The result is not always the only important thing, sometimes it is not even important at all. -The process, whether it is methodological, artistic, technical or athletic is often the reason why we perform these tasks. -We not only run for going somewhere, most of the time it is for keeping a healthy physical condition. -We do not play music or games in the quest of an eventual optimal performance, but for entertainment. - -Chess and Go playing computers are often cited as examples of the superiority of machines over humans. -In my opinion, Chess or Go are not convincing and fair example for this, because these games were designed to be challenging for humans, not for machines. -Rather, the point is about showing what is achievable thanks to computers. -Following Engelbart's vision, my objective is to use technology as a way to augment humans' intellect and capabilities. -My rationale is to study honestly the strengths and weaknesses of both humans and computers, and use the best of both to resolve problems. - -It is up to us (researchers, designers, developers, \dots) to decide what we delegate to machines that follow our instructions, and what we keep doing by ourselves with the help of interactive tools. -It is first of all a matter of purpose. -There is no point playing a game against a computer you cannot beat. -This is not entertaining, and it is unlikely you can reuse its strategy for learning how to play better. -Garry Kasparov proposed the concept of \defword{advanced chess}\footnote{\url{https://en.wikipedia.org/wiki/Advanced_Chess}}, a variation of chess in which players can use computers to get an overview of possible moves and their implication. Kasparov's idea was to increase chess level, eliminate bad moves, and give the public more understanding on players and computers strategies. He claims that such an advanced chess player is better than humans or computers alone. - -%\myquote{An advanced chess player is stronger than a human or computer alone}{G. Kasparov} - -Delegating tasks to computers is not a matter of computing power and methods, but also a matter of trust in the process and the outcome. -Whatever the automatic system capabilities, we need to know its status in real time, to be able to influence its behavior, and to take the control if necessary. -Roy \etal showed that [Automation Accuracy is Good, but High Controllability May Be Better]~\cite{roy19}. - - -Beaudouin-Lafon's instrumented interaction opposes the computer-as-a-tool and the computer-as-a-partner paradigms. I see a continuum between these two visions. Every system can act both as a tool and as a partner. - -Example? Semantic pointing~\cite{blanch04} and so on? - -instrumented interaction for partners? - -computers see oracles, users see partners. - -\begin{figure}[htb] -\centering -\begin{subfigure}[b]{.45\textwidth} -\includegraphics[width=\textwidth]{instrumentedinteraction} -\caption{Instrumented interaction} -\label{fig:instrumentedinteraction} -\end{subfigure} -\hfill -\begin{subfigure}[b]{.45\textwidth} -\includegraphics[width=\textwidth]{partner} -\caption{Delegated interaction} -\label{fig:delegatedinteraction} -\end{subfigure} -\label{fig:directmanipulationextensions} -\caption{Extensions to direct manipulations, for tools and partners.} -\end{figure} - -\myquote{Algorithms are but building blocks in larger interactive systems, like drops of computation in a sea of interaction}{Beaudouin-Lafon~\cite{mbl08}} - -On the Tool side the user has more control over the system, on the partner side the system is more autonomous. I describe this concept through the notion of \defword{fluidity} of an interactive system, the term being inspired by the quote above. - -\begin{definition}{Fluidity} -The \defword{fluidity} of an interactive systems refers to . -\end{definition} - -Semantic and articulatory distance\cite{norman88} -\begin{definition}{Compliance} -The \defword{compliance} of an interactive systems refers to its ability to executes the user's intended actions precisely. -\end{definition} - -bounded-machine : n authorized operations after reading the stream. fluidity depends on machine poser. s = op/s f = n / s - - -Rigid systems perform long calculations with rare notifications, therefore rare occasions for the user to see the result, and change the partner's behavior. Fluid systems are direct manipulation/tools: calculations are fast (at least real time), the user has a continuous view on the system's state, and can interact at any time. - -Evolution in time (large time scale) (ex. with machine learning). - -Don Norman action theory, computer side - -Plato's cavern: computer see a partial digitalized version of the world - -\begin{figure}[htb] -\centering -\includegraphics[width=.5\textwidth]{delegationevolution} -\label{fig:delegation} -\caption{The evolution of delegation over time.} -\end{figure} - -Discussion on initiative? -Mixed-Initiative~\cite{horvitz99} - -co-adaptation \cite{mackay90} - -Crowdsourcing: tasks not doable by a computer - -\begin{table}[htb] -\centering -\begin{tabular}{c m{2cm}|m{2cm}|m{2cm}|m{2cm}|m{2cm}} -\toprule -\multicolumn{2}{c}{} & \multicolumn{4}{c}{\textbf{Authority}}\\ -\multicolumn{2}{c}{} & \multicolumn{2}{c|}{Without} & \multicolumn{2}{c}{With}\\ -\cmidrule{3-6} -\multicolumn{2}{c}{} & \multicolumn{2}{c|}{\textbf{Trust}} & \multicolumn{2}{c}{\textbf{Trust}}\\ -\cmidrule{3-6} -\multicolumn{2}{c}{} & Without & With & Without & With\\ -\midrule -%\multirow{2}{*}[-0.8cm]{\rotatebox[origin=c]{90}{\textbf{Fluidity}}} & Fluid & & & & \\[1cm] -\multirow{2}{*}{\textbf{Fluidity}} & Fluid & & & & \\[1cm] -\cmidrule{2-6} - & Rigid & & & & \\[1cm] -\bottomrule -\end{tabular} -\end{table} - -\subsection{Technology incompleteness} - -Every time a new technology is out, people predict the end of older technology. -Voice recognition was said to replace keyboards. -Gesture recognition was supposed to replace the mouse. -Some even claimed that tablets will replace desktop computers. -In reality, even if these new technologies proved to be useful in some contexts, they were unable to replace them in all contexts. - -\myquote{Everything the best for something and the worst for something else}{W.A.S. Buxton} - -GPS: -humans create streams - -\subsection{Designer as part of problem solving} - - -designeering\cite{huot13} - -ever running systems ~\cite{black10} - -adjacent possibles~\cite{kauffman95} - -%At some point I realized that this idea of a computer able to have a conversation is futile in the sense that this is something humans do very well. -%The point of playing chess or any other game is to have fun, or challenging other individuals. - - - -% Algorithms are not designed for knowing how their inputs were built. -% Humans take care of this information. -% When reading a sentence, one may interpret it differently depending on who wrote it, where, and who was supposed to read it. -% The context is a complex combination of background knowledge, alternative inputs, … -% Interpretation of input must evolve as the context changes. -% Algorithms must be modified. -% Humans just deal with it. - -% Algorithms are made to compute an exact (sometimes approached) solution to a well specified problem. Humans (hopefully) find a reasonable solution for non-formalized and evolving problems. - -% Humans started building computers as an exercise of thought. -% Computers quickly could perform operations organized in \defword{algorithms}, such as calculus on large numbers, that humans could never achieve with their own skills alone. -% Nowadays, computers use advanced methods such as \defword{machine learning}, and clearly have capabilities beyond human competences. -% Computers can beat the best chess or go players in the world, with highly trained neural networks~\cite{silver18}. -% This example is often cited as a success of \defword{Artificial Intelligence} (AI). -% This is undoubtedly due to the fact they are considered as difficult games, and requiring high mental skills. -% In reality these games were designed to be challenging for humans, not to be challenging for machines. -% Their rules are fully specified, and the exploration of possibilities is easy for a computer. -%Things get more complicated with variants such as Real-Time chess\footnote{http://rtchess.deapthoughts.com/}, in which players may move any piece at any time. -%To balance the game, there is a delay between moving a piece and the ability to move this piece again. - - -%\begin{idee} - -%(examples). Go, Chess? Turn based games with fully specified rules are easy for computers. What about real time games with random events and multi-parameter environment changing in real time (SimCity example)? Tetris \url{https://arxiv.org/pdf/cs/0210020.pdf} -%However human still have expertise in plenty of areas long way off computers grasp, to begin with the design, implementation, maintenance and evolution of software. -%(other examples, creative activities). - -% The field of \defword{Human-Computer Interaction} (HCI) was built on Engelbart's vision of computers that extend human capabilities rather than replacing them~\cite{engelbart68}. - -%\end{idee} - -\begin{definition}{Interactive system} -An \defword{interactive system} is… -\end{definition} - - -\section{Outline} - -Elements of perception/action - -\begin{itemize} - \item Lagmeter \cite{casiez17} - \item Friction textures \cite{potier12,potier16} - \item Activibe \cite{cauchard16} -\end{itemize} - -Direct manipulation - -\begin{itemize} - \item Fingercuts \cite{goguey14,goguey14a,goguey17} - \item FlexStylus \cite{fellion17} - \item Haptic Direct Manipulation \cite{pietrzak15,gupta16} - \item Summon \& Select \cite{gupta17} -\end{itemize} - -Control with automation - -\begin{itemize} - \item Métamorphe \cite{bailly13} - \item Living Desktop \cite{bailly16} - \item Ctrl Mouse \cite{pietrzak14} - \item RayCursor \cite{baloup18,baloup19} -\end{itemize} +Mjolnir: Computing tools to empower users +Loki: Technology and knowledge for Interaction \ No newline at end of file diff --git a/tex/old/introduction.tex b/tex/old/introduction.tex new file mode 100644 index 0000000..cdb84c1 --- /dev/null +++ b/tex/old/introduction.tex @@ -0,0 +1,416 @@ +%!TEX root = ../hdrmain.tex + +\chapter*{Introduction} +\label{chap:intro} +\addcontentsline{toc}{chapter}{\nameref{chap:intro}} +\addcontentsline{lof}{chapter}{\nameref{chap:intro}} + \epigraph{\lorem \lorem \lorem}{Auteur} + +When I was a teenager, I was fascinated by computers, much more than I am today. I started to program early, and at this time I thought it was possible to create a program for everything. I believed the only limit of computer capabilities was the programmer's ability to code it. My limit was… pointers at this time. I wanted to create a program that was able to have a relevant conversation with me. I learned later that many people were working on this for decades. I tried the ELIZA system~\cite{weizenbaum66}, and I was not satisfied with it. It was just repeating what I was saying, and used a small set of expressions to ask me to make the conversation. Most of the time it spotted the right word in my sentence, but sometimes not and what it was saying just did not make any sense. It clearly failed the famous Turing test~\cite{turing50}, I knew about many years later. I think I wanted the program to understand what I write, without having hard coded key sentences or all grammar rules. Nobody speaks like a grammar book anyway. Naively, I wanted to write a better program. However I did not want to code associations between concepts, words, metaphors and so on. I knew this was a huge work, that would take me several lives to achieve, and that when I will be finished, half of it will be obsolete. In fact I had no idea how to start. + +\section{Artificial Intelligence} + +Later, when I was a computer science student, I followed \defword{Artificial Intelligence} (AI) courses. I learned about classical AI techniques, such as \defword{expert systems}~\cite{lindsay80}, the \defword{Perceptron}~\cite{rosenblatt57} and \defword{neural networks}~\cite{mcculloch43}. It reminded my teenager days. These were of course more advanced than anything I made myself. However I felt disappointed because none of these techniques seemed to be sufficient for creating a system that passes the Turing Test. In fact seemed to be the general opinion in the scientific community, mostly due to overstatements from pioneer researchers in AI: + +\myquote{Within ten years a digital computer will be the world’s chess champion.}{Herbert Simon and Allen Newell, 1958} + +The first time a chess-playing computer won a game against a world champion was Deep Blue against Garry Kasparov in 1996\footnote{\url{https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)}}. +However Kasparov won the match 4-2. +The computer won the next year $3\frac{1}{2}$ - $2\frac{1}{2}$. +It is however only recently that computers clearly beat world-class chess or go players, with AlphaZero~\cite{silver18}, thanks to the recent advances in Deep learning~\cite{lecun15}, which resurrected neural networks techniques. + +\myquote{Machines will be capable, within twenty years, of doing any work a man can do.}{Herbert Simon, 1965} + +This is still far from being true today. Machines still have to be programmed for specific tasks, and they have little knowledge and no understanding of their environment. This makes machines hardly adaptable to “any work a man can do”. + +\myquote{Within a generation, the problem of creating ‘artificial intelligence’ will substantially be solved.}{Marvin Minsky, 1967} + +The definition of “intelligence” or “artificial intelligence” is far from being consensual. +Despite the tremendous progress of Machine learning in the past years, I would argue that computers are still far from being intelligent as we would measure intelligence of a human being. +Dessalles says that despite their capabilities, computers do not even know they are intelligent~\cite{dessalles19}. +Neural networks are still tied to a particular task, and are unable to evolve in order to solve different problems. +They still require humans to design them and train them. + +%\myquote{In from 3 to 8 years, we will have a machine with the general intelligence of an average human being.}{Marvin Minsky, 1970} + +%Not only it did not happen in the worst case scenario predicted by Minsky. But it is still not the case today.\\ + +%http://biology.kenyon.edu/slonc/bio3/AI/TIMELINE/timeline.html +%1943: McCulloch and Pitt propose neural-network architectures for intelligence. +%1950: Turing Test proposed (Turing's "Computing Machinery and Intelligence") +%1959: Rosenblatt introduces Perceptron. +%1965: Simon predicts, "by 1985 machines will be capable of doing any work a man can do" +%1965: Buchanan, Feigenbaum & Lederberg begin DENDRAL expert system project. + +\section{Logics} + +Later on in my studies, I became fascinated by logics and software proofs. +I learned $\lambda$-calculus~\cite{church32} and Turing machines~\cite{turing38}, which are the foundations of computing. +These concepts define the notions of computation, calculability and programming language. +In the late 30', Turing defined a \defword{computable function} as follows: + +\begin{definition}{Computable function} +We shall use the expression \defword{computable function} to mean a function calculable by a machine, and let “effectively calculable” refer to the intuitive idea without particular identification with any one of these definitions.~\cite{turing38} +\end{definition} + +Therefore, computable functions refer to programs (\defword{Turing machines} (TM), \defword{$\lambda$-calculus} ($\lambda$-c) or any \defword{Turing-complete} formalism) and effectively calculable functions are algorithms. +The Church-Turing thesis states that every effectively calculable function is a computable function. +To put it simply, there is a computer program for every algorithm. +%This may seem straightforward now that we have billions of examples useful and working computer programs. +%However it states an equivalence between theoretical models such as Turing machines or $\lambda$-calculus and what can be computer on a physical computer. +%It enables the theoretical analysis of computing problems with one of these models. +Turing showed the limits of calculability though. +He proved there is no algorithm that can predict if running any program on any input finishes or runs forever~\cite{turing36}. +This is known as the halting problem, and was defined by Hilbert~\cite{hilbert28}. +The idea of the proof is that such an algorithm would take a program and an input as an input, and return 1 if this program finishes, 0 otherwise. +This program would need to emulate this program on this input. +If the program finishes, this algorithm would effectively return 1. +Otherwise the emulation would run forever, and the algorithm would never return 0. +In the 50', Tarski \etal describe other undecidable theories, such as the first-order theory of natural numbers~\cite{tarski53}. +This is a strong reason why still today, difficult proofs are made by humans, not computers. +When the theorem to be proved or hypotheses have quantified variables, substitution possibilities are infinite. +With thinking, observation of available hypothesis, instinct, and sometimes luck, humans can make progress in the proof. +The question arises: since there are problems no algorithm can solve, is there another way to solve these problems? + +%If this is the case, there is something interesting happening beyond the Church-Turing thesis. +% Church and Turing showed these two models are equivalent in the sense they compute the same things, which (in short) leaded to the Church-Turing thesis. + +\section{Human-Computer Interaction} + +After such a theoretical background, I made a $90^\circ$ turn, and started research with a master internship, then a PhD, in \defword{Human-Computer Interaction} (HCI). +My work consisted in finding how to use haptics to encode information for non-visual interaction~\cite{pietrzak05b,pietrzak08}. +There was unfortunately no relation whatsoever with theoretical computer science at that point. +However a first contact was made in 2006, when I attended a talk by Beaudouin-Lafon, entitled “Interaction is the future of computing”\footnote{\href{https://rjc06.afihm.org/}{RJC IHM 2006}, \url{https://rjc06.afihm.org/supports/MBLRJC-AFIHM2006.pdf}}. +As I still knew little about HCI, I was struck by thunder. +He exposed three fascinating visions: +\begin{itemize} + \item Wegner's \defword{interactive computation}~\cite{wegner97}. + \item Engelbart's vision of computers that extend human capabilities~\cite{engelbart68}. + \item His own \defword{instrumental interaction}~\cite{mbl00}. +\end{itemize} + +\subsection{Wegner} + +Wegner refutes the strong Church-Turing thesis, which states that TMs can do anything a computer can do~\cite{goldin08}. +He proves it by showing that interaction has a greater expressiveness than algorithms, and extends the original Church-Turing thesis to \defword{interactive computation}~\cite{goldin06}. +Knuth specifies \defword{algorithms} as as follows~\cite{knuth68}:%return a specified result given predefined inputs. + +\begin{itemize} + \item They always terminate after a finite number of steps. + \item Every of their steps must be precisely defined. + \item They have zero or more inputs taken from specified sets of objects, specified before the algorithm begins, or dynamically as it runs. + \item They provide one or more outputs, which have a specified relation to the inputs. + \item All of its operations can be done in a finite length of time using a pen and paper. +\end{itemize} + +With this definition, we observe that algorithms cannot deal with infinite input streams. +They operate on predefined specifications that cannot evolve during computation. +%This means algorithms are isolated from the outside world during computation. +At the opposite, interaction is a paradigm that observes the environment continuously. +Wegner formalizes the concept of \defword{interaction machines} and proves it is more expressive than TMs~\cite{wegner99}. + +\myquote{The equivalent expressiveness of TMs, algorithms, computable functions, and formal systems is due not to their maximal expressiveness but to their common reliance on induction for computation and reasoning.}{Peter Wegner~\cite{wegner99}} + +Instead of finite set of inputs, interaction machines are connected to single (SIM) or multiple (MIM) data streams. +Their expressiveness in not defined by induction, but respectively by non-well founded sets and co-induction. +Induction (hence algorithms, TMs, $\lambda$-c, \dots) outputs are recursively enumerable while non-well founded sets and co-induction outputs are non-enumerable~\cite{gordon94,chlipala13}. +This proves that interaction has a greater expressiveness than algorithms, allowing Wegner to extend the Church-Turing thesis for interaction machines. Turing himself defined other paradigms with more capabilities than TM: choice machines~\cite{turing36} and oracle machines~\cite{turing38}: + +\begin{definition}{Choice Machine} +For some purpose we might use machines (choice machines or \defword{c-machines}) whose motion is only partially determined by the configuration. When such a machine reaches one of these ambitious configurations, it cannot go on until some arbitrary choice has been made by an external operator.~\cite{turing36} +\end{definition} + + +C-machines clearly define interaction, with the possibility for the environment to influence computation. +However Turing does not give further details, and focuses on automatic machines (a-machines, later dubbed TM). +Later, Turing introduced the concept of \defword{oracle}, an agent that can solve any problem, and o-machines, which are TM that use oracles to perform part of their computation. +This is an evolution of c-machines, but there is not much details. + +\begin{definition}{Oracle Machine} +Let us suppose that we supplied with some unspecified means of solving number theoretic problems; a kind of oracle as it were. We will not go any further +into the nature of this oracle than to say that it cannot be a machine; with the +help of the oracle we could form a new kind of machine (call them \defword{o-machines}), +having as one of its fundamental processes that of solving a given number theoretic problem.~\cite{turing38} +\end{definition} + + +%Now, Interaction as described by Wegner includes every paradigm that can handle infinite streams, including Object Oriented programming or Neural Networks for example. +% is more powerful than algorithms\cite{wegner95,wegner97}. +As an example of his interaction machines, Wegner describes one that simply echoes an input stream to an output stream~\cite{wegner97}. +By doing so, this machine wins half of chess games by echoing two simultaneous games one to the other. +Such a performance is inaccessible to algorithms, because they only take into account the current configuration to compute the next play. +They are not aware of future plays, and therefore can only estimate the future as possibilities. +In fact, except for the last play, the algorithm will never see the end of the game, not even the next play, but only the current play. +Another execution of the algorithm, with the new configurations will handle next plays, ignoring this play even happened. +At the opposite, the interaction machine plays the whole game from start to finish, and relies on its ability to react to every play. + +%From the opponent point of view, this machine is playing chess, up to the best chess players in the world level. +By using this mechanism, interaction machines sort of include external behavior, just like Turing's o-machines. +By extending this example with conversations, it is obvious that Wegner's echo interaction machine passes the Turing Test, twice at the same time. +Therefore, more than just observing the environment with sensors or probes, it can absorb any external computing or thinking power. +It is only limited by what it observes, and how this observation is encoded. + +The trade-off is that interaction machines are not made for finding optimal solutions to well specified problems, but satisfying solutions to evolving problems. +This is precisely the difference between rationalism and empiricism. + +%Wegner presents interaction as a way to extend algorithms with external computation of some sort, with his interactive machines. + +\subsection{Englebart} + +Englebart's vision stems from Vannevar Bush's work on the Memex, % as he was coordinating scientific research in the United States during the second world war. +the idea of extending human memory with an electronic library~\cite{bush45}. +It comprises a mechanism to store books and notes, an indexing system that allows searching documents, and a way to bookmark documents. +Hypertext, computer networks and search engines are directly inspired by this vision. +Between 1962 and 1968, at a time computer were a room size, Englebart pushed this vision forward. +His vision was to use computers to extend human capabilities. +In his famous 1968 demo\footnote{The mother of all demos: \url{https://youtu.be/yJDv-zdhzMY}}, he showcased: a collaborative real-rime word processing editor, the computer mouse, a chorded keyboard, a windowing system, hypertext, file sharing, file annotating, video conferencing and revision control among others. +It was not only mesmerizing because it was a huge list of striking ideas that seem coming from another planet, but also because it was actually functioning live, contrary to the Memex which remained a concept. +The reason why it was not only a technology push forward, but the creation of a whole new scientific area is that computers were not anymore just computing machines, but interactive machines that help us in our everyday tasks. +This vision changed the way we work, or our entertainments. +Most of our activities today use an interactive system at some point. +Along with Sutherland's work on Sketchpad~\cite{sutherland63}, and of course Bush's Memex that inspired them, these are the foundations of Human-Computer Interaction. + +\subsection{Beaudouin-Lafon} + +Beaudouin-Lafon defined the Instrumented interaction paradigm enables users to interact with domain objects~\cite{mbl00}. +To do so, the user performs actions on the instrument, which provide the user an immediate feedback, and perform a command on the domain object. +As a consequence, the domain object returns a response to the user. +The immediate feedback provides high level of control thanks to a fast perception/action cycle. +The response to the command lets the user know the result of his actions, and allows him to continue or correct errors where necessary. +%Instruments are tools that +Instrumented interaction is an extension of Schneiderman's \defword{direct manipulation}~\cite{schneiderman83}. +\begin{definition}{Direct Manipulation} +The concept of \defword{direct manipulation} consists of design principles for user interfaces that favors usability. +Objects of interest must be visible and directly manipulable, actions must be rapid, reversible and incremental. +\end{definition} + +In the early 80's these principles made \defword{Graphical User Interfaces} (GUI) much more powerful than command-line interfaces in many situations. +Unsurprisingly, they are similar or related to Nielsen's heuristics for the evaluation of user interfaces~\cite{nielsen90}. +Instrumented interaction consist in the direct manipulation of instruments, which in turn execute commands on the objects of interest. +It is a direct inspiration of the use of instruments and tools in the real world. +% The link between instrumented interaction and direct manipulation is made clear through Beaudouin-Lafon and Mackay's principles of \defword{reification}, \defword{polymorphism} and \defword{reuse}~\cite{mbl00a}. + +% They define \defword{reification} as “the process by which concepts are turned into objects” +% %Instrumented Interaction is an instance of the principle of \defword{reification}, which Beaudouin-Lafon and Mackay define as “the process by which concepts are turned into objects”~\cite{mbl00a}. +% Instrumented interaction is the reification of commands, which enables users to run commands on objects by interacting with instruments; typically widgets on a GUI. +% Instruments are designed to be visible and directly manipulable. + +% %Most of objects of a GUI, if not all, are reifications of a real world concept. +% They also define the principle of \defword{polymorphism} in the context of HCI as “the property that enables a single command to be applicable to objects of different types”. +% This property is similar to the concept of polymorphism in programming languages. +% %Similarly to the same concept in programming languages, it is a powerful tool for the design of user interfaces. +% It enables reducing and grouping commands, which is useful in complex software that can propose thousands of commands. +% %%Something facilitate mapping +% [instruments and reversibility through polymorphism?] + +% , and \defword{reuse} “makes previously-provided input available for reuse in the current context”. + +% [instruments and rapid + incremental through reuse?] + +Instrumented interaction and direct manipulation are examples of the first of Beaudouin-Lafon's interaction paradigms: \emph{computer-as-a-tool}, the others being \emph{computer-as-a-partner} and \emph{computer-as-a-medium}~\cite{mbl04}. + +A partner system is the reification of ? + +\cite{mbl06} + +\section{My stuff} + +The idea of computers replacing humans for every task is futile. +The result is not always the only important thing, sometimes it is not even important at all. +The process, whether it is methodological, artistic, technical or athletic is often the reason why we perform these tasks. +We not only run for going somewhere, most of the time it is for keeping a healthy physical condition. +We do not play music or games in the quest of an eventual optimal performance, but for entertainment. + +Chess and Go playing computers are often cited as examples of the superiority of machines over humans. +In my opinion, Chess or Go are not convincing and fair example for this, because these games were designed to be challenging for humans, not for machines. +Rather, the point is about showing what is achievable thanks to computers. +Following Engelbart's vision, my objective is to use technology as a way to augment humans' intellect and capabilities. +My rationale is to study honestly the strengths and weaknesses of both humans and computers, and use the best of both to resolve problems. + +It is up to us (researchers, designers, developers, \dots) to decide what we delegate to machines that follow our instructions, and what we keep doing by ourselves with the help of interactive tools. +It is first of all a matter of purpose. +There is no point playing a game against a computer you cannot beat. +This is not entertaining, and it is unlikely you can reuse its strategy for learning how to play better. +Garry Kasparov proposed the concept of \defword{advanced chess}\footnote{\url{https://en.wikipedia.org/wiki/Advanced_Chess}}, a variation of chess in which players can use computers to get an overview of possible moves and their implication. Kasparov's idea was to increase chess level, eliminate bad moves, and give the public more understanding on players and computers strategies. He claims that such an advanced chess player is better than humans or computers alone. + +%\myquote{An advanced chess player is stronger than a human or computer alone}{G. Kasparov} + +Delegating tasks to computers is not a matter of computing power and methods, but also a matter of trust in the process and the outcome. +Whatever the automatic system capabilities, we need to know its status in real time, to be able to influence its behavior, and to take the control if necessary. +Roy \etal showed that [Automation Accuracy is Good, but High Controllability May Be Better]~\cite{roy19}. + + +Beaudouin-Lafon's instrumented interaction opposes the computer-as-a-tool and the computer-as-a-partner paradigms. I see a continuum between these two visions. Every system can act both as a tool and as a partner. + +Example? Semantic pointing~\cite{blanch04} and so on? + +instrumented interaction for partners? + +computers see oracles, users see partners. + +\begin{figure}[htb] +\centering +\begin{subfigure}[b]{.45\textwidth} +\includegraphics[width=\textwidth]{instrumentedinteraction} +\caption{Instrumented interaction} +\label{fig:instrumentedinteraction} +\end{subfigure} +\hfill +\begin{subfigure}[b]{.45\textwidth} +\includegraphics[width=\textwidth]{partner} +\caption{Delegated interaction} +\label{fig:delegatedinteraction} +\end{subfigure} +\label{fig:directmanipulationextensions} +\caption{Extensions to direct manipulations, for tools and partners.} +\end{figure} + +\myquote{Algorithms are but building blocks in larger interactive systems, like drops of computation in a sea of interaction}{Beaudouin-Lafon~\cite{mbl08}} + +On the Tool side the user has more control over the system, on the partner side the system is more autonomous. I describe this concept through the notion of \defword{fluidity} of an interactive system, the term being inspired by the quote above. + +\begin{definition}{Fluidity} +The \defword{fluidity} of an interactive systems refers to . +\end{definition} + +Semantic and articulatory distance\cite{norman88} +\begin{definition}{Compliance} +The \defword{compliance} of an interactive systems refers to its ability to executes the user's intended actions precisely. +\end{definition} + +bounded-machine : n authorized operations after reading the stream. fluidity depends on machine poser. s = op/s f = n / s + + +Rigid systems perform long calculations with rare notifications, therefore rare occasions for the user to see the result, and change the partner's behavior. Fluid systems are direct manipulation/tools: calculations are fast (at least real time), the user has a continuous view on the system's state, and can interact at any time. + +Evolution in time (large time scale) (ex. with machine learning). + +Don Norman action theory, computer side + +Plato's cavern: computer see a partial digitalized version of the world + +\begin{figure}[htb] +\centering +\includegraphics[width=.5\textwidth]{delegationevolution} +\label{fig:delegation} +\caption{The evolution of delegation over time.} +\end{figure} + +Discussion on initiative? +Mixed-Initiative~\cite{horvitz99} + +co-adaptation \cite{mackay90} + +Crowdsourcing: tasks not doable by a computer + +\begin{table}[htb] +\centering +\begin{tabular}{c m{2cm}|m{2cm}|m{2cm}|m{2cm}|m{2cm}} +\toprule +\multicolumn{2}{c}{} & \multicolumn{4}{c}{\textbf{Authority}}\\ +\multicolumn{2}{c}{} & \multicolumn{2}{c|}{Without} & \multicolumn{2}{c}{With}\\ +\cmidrule{3-6} +\multicolumn{2}{c}{} & \multicolumn{2}{c|}{\textbf{Trust}} & \multicolumn{2}{c}{\textbf{Trust}}\\ +\cmidrule{3-6} +\multicolumn{2}{c}{} & Without & With & Without & With\\ +\midrule +%\multirow{2}{*}[-0.8cm]{\rotatebox[origin=c]{90}{\textbf{Fluidity}}} & Fluid & & & & \\[1cm] +\multirow{2}{*}{\textbf{Fluidity}} & Fluid & & & & \\[1cm] +\cmidrule{2-6} + & Rigid & & & & \\[1cm] +\bottomrule +\end{tabular} +\end{table} + +\subsection{Technology incompleteness} + +Every time a new technology is out, people predict the end of older technology. +Voice recognition was said to replace keyboards. +Gesture recognition was supposed to replace the mouse. +Some even claimed that tablets will replace desktop computers. +In reality, even if these new technologies proved to be useful in some contexts, they were unable to replace them in all contexts. + +\myquote{Everything the best for something and the worst for something else}{W.A.S. Buxton} + +GPS: +humans create streams + +\subsection{Designer as part of problem solving} + + +designeering\cite{huot13} + +ever running systems ~\cite{black10} + +adjacent possibles~\cite{kauffman95} + +%At some point I realized that this idea of a computer able to have a conversation is futile in the sense that this is something humans do very well. +%The point of playing chess or any other game is to have fun, or challenging other individuals. + + + +% Algorithms are not designed for knowing how their inputs were built. +% Humans take care of this information. +% When reading a sentence, one may interpret it differently depending on who wrote it, where, and who was supposed to read it. +% The context is a complex combination of background knowledge, alternative inputs, … +% Interpretation of input must evolve as the context changes. +% Algorithms must be modified. +% Humans just deal with it. + +% Algorithms are made to compute an exact (sometimes approached) solution to a well specified problem. Humans (hopefully) find a reasonable solution for non-formalized and evolving problems. + +% Humans started building computers as an exercise of thought. +% Computers quickly could perform operations organized in \defword{algorithms}, such as calculus on large numbers, that humans could never achieve with their own skills alone. +% Nowadays, computers use advanced methods such as \defword{machine learning}, and clearly have capabilities beyond human competences. +% Computers can beat the best chess or go players in the world, with highly trained neural networks~\cite{silver18}. +% This example is often cited as a success of \defword{Artificial Intelligence} (AI). +% This is undoubtedly due to the fact they are considered as difficult games, and requiring high mental skills. +% In reality these games were designed to be challenging for humans, not to be challenging for machines. +% Their rules are fully specified, and the exploration of possibilities is easy for a computer. +%Things get more complicated with variants such as Real-Time chess\footnote{http://rtchess.deapthoughts.com/}, in which players may move any piece at any time. +%To balance the game, there is a delay between moving a piece and the ability to move this piece again. + + +%\begin{idee} + +%(examples). Go, Chess? Turn based games with fully specified rules are easy for computers. What about real time games with random events and multi-parameter environment changing in real time (SimCity example)? Tetris \url{https://arxiv.org/pdf/cs/0210020.pdf} +%However human still have expertise in plenty of areas long way off computers grasp, to begin with the design, implementation, maintenance and evolution of software. +%(other examples, creative activities). + +% The field of \defword{Human-Computer Interaction} (HCI) was built on Engelbart's vision of computers that extend human capabilities rather than replacing them~\cite{engelbart68}. + +%\end{idee} + +\begin{definition}{Interactive system} +An \defword{interactive system} is… +\end{definition} + + +\section{Outline} + +Elements of perception/action + +\begin{itemize} + \item Lagmeter \cite{casiez17} + \item Friction textures \cite{potier12,potier16} + \item Activibe \cite{cauchard16} +\end{itemize} + +Direct manipulation + +\begin{itemize} + \item Fingercuts \cite{goguey14,goguey14a,goguey17} + \item FlexStylus \cite{fellion17} + \item Haptic Direct Manipulation \cite{pietrzak15,gupta16} + \item Summon \& Select \cite{gupta17} +\end{itemize} + +Control with automation + +\begin{itemize} + \item Métamorphe \cite{bailly13} + \item Living Desktop \cite{bailly16} + \item Ctrl Mouse \cite{pietrzak14} + \item RayCursor \cite{baloup18,baloup19} +\end{itemize} + diff --git a/tex/vocabulary.tex b/tex/vocabulary.tex index b8c5a11..dbaecf0 100644 --- a/tex/vocabulary.tex +++ b/tex/vocabulary.tex @@ -1,38 +1,47 @@ %!TEX root = ../hdrmain.tex -\chapter{Evolution o} +\chapter{Extention of interaction modalities} \epigraph{\lorem}{Auteur} \begin{Abstract} \loremipsum \end{Abstract} +\section{Introduction} -\section{Extending the input vocabulary} +\section{Extention of interaction vocabularies} \subsection{FlexStylus: a flexible digital pen} FlexStylus \cite{fellion17} -\subsection{RayCursor: a 3D pointing technique in VR} +\subsection{Activibe: vibrotactile feedback for activity monitoring} -RayCursor \cite{baloup19} +Activibe~\cite{cauchard16} -\subsection{FingerCuts: leveraging finger identification for multi-touch interaction} +\subsection{Tactile textures with programmable friction} -FingerCuts \cite{goguey14,goguey14a,goguey17} +Tactile Textures~\cite{potier12,potier16} -\section{Leveraging haptic feedback} +\section{New interaction techniques with additional vocabulary} -\subsection{Activibe: vibrotactile feedback for activity monitoring} +\subsection{RayCursor: a 3D pointing technique in VR} -Activibe~\cite{cauchard16} +RayCursor \cite{baloup19} -\subsection{Tactile textures with programmable friction} +Leverages 3D input and combines with 3D motion for more efficient pointing -Tactile Textures~\cite{potier12,potier16} +\subsection{FingerCuts: leveraging finger identification for multi-touch interaction} + +FingerCuts \cite{goguey14,goguey14a,goguey17} + +Leverages finger identification for command selection and direct manipulation of parameters. \subsection{Vibrotactile widgets} Vibrotactile widgets~\cite{frisson17} + +Leverages vibrotactile feedback for touch surfaces. + +\section{Conclusion} -- 2.30.2