Authorea

Joe Corneli chase through alison's comments again over 8 years ago

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observed, take an interest in them, and transform the observations into artefacts with lasting value. %% In this section, we will show how the model allows for more precise %% thinking than other existing work touching on this area. We then %% discuss implications from our findings for future research. %\input{12a-recommendations} %\input{12b-future-work-intro} %\input{12c-future-work-conclusion} \input{11related} %\input{11related} \subsection{Related work} \label{sec:related} Paul Andr{\'e} et al.~\citeyear{andre2009discovery} previously proposed a two-part model of serendipity encompassing ``the chance encountering of information, and the sagacity to derive insight from the encounter.'' The first phase has been automated more frequently -- but these authors suggest that computational systems should be developed that support both aspects. They specifically suggest to pursue this work by developing systems with better representational features: \emph{domain expertise} and a \emph{common language model}. These features seem to exemplify aspects of the \emph{prepared mind}. However, the \emph{bridge} is a distinct step in the process that preparation can support, but that it does not always fully determine. Domain understanding is not always a precondition; it can be emergent. For instance, persons involved in a dialogue may understand each other quite poorly, while nevertheless finding the conversation interesting and ultimately rewarding. Misunderstandings can present learning opportunities, and can develop \emph{new} shared language. %% Various social strategies, ranging from Writers %% Workshops, to open source software, to community-based approaches in %% psychological counselling have been developed to exploit similar %% emergent effects and to develop \emph{new} shared language %% \cite{gabriel2002writer,seikkula2014open}. Inspired by social systems that capitalise on this effect, we have investigated the feasibility of building multi-agent systems that learn by sharing and discussing partial understandings \cite{corneli2015computational,corneli2015feedback}. As we touched on in Section \ref{sec:nextgenrec}, serendipity in the field of recommendation systems is understood to imply that the system suggests \emph{unexpected} items, which the user considers to be \emph{useful}, \emph{interesting}, \emph{attractive} or \emph{relevant} \cite{foster2003serendipity,Toms2000}. \citeA{Herlocker2004} and \citeA{McNee2006} view serendipity to be important component of recommendation quality, alongside accuracy and diversity. % \cite{Herlocker2004} \cite{Lu2012},\cite{Ge2010}. Definitions differ as to the requirement of \emph{novelty}; \citeA{Adamopoulos2011}, for example, describe systems that suggest items that may already be known, but which are still unexpected in the current context. While standardised measures such as the $F_1$-score or the (R)MSE are used to determine the \emph{accuracy} of a recommendation (i.e.~whether the recommended item is very close to what the user is already known to prefer), as yet there is no common agreement on a measure for serendipity, although there are several proposals \cite{Murakami2008,Adamopoulos2011,McCay-Peet2011,iaquinta2010can}. In terms of the framework from Section \ref{sec:by-example}, these systems focus mainly on generating a \emph{trigger} to be processed by the user, and prepare the ground for serendipitous \emph{discovery}. Intelligent modelling approaches could potentially bring additional aspects of serendipity into play in future systems, as discussed in Section \ref{sec:nextgenrec}. Recent work has examined the related topics of \emph{curiosity} \cite{wu2013curiosity} and \emph{surprise} \cite{grace2014using} in computing. The latter example seeks to ``adopt methods from the field of computational creativity [$\ldots$] to the generation of scientific hypotheses.'' This provides a useful example of an effort focused on computational \emph{invention}. As we indicated earlier, creativity and serendipity are often discussed in related ways. A further terminological clarification is warranted. The word \emph{creative} can be used to describe a ``creative product'', a ``creative person'', a ``creative process'' and even the broader ``creative milieu.'' Computational creativity must take acount all of these aspects \cite{jordanous2015four}. In contrast, the model we have presented focuses only on serendipity as an attribute of a particular kind of process. Most often, we speak of a system's \emph{potential} for serendipity. In the current work, we do not use the term to describe an artefactual property (like novelty or usefulness), or a system trait (like skill). Figueiredo and Campos \citeyear{Figueiredo2001} describe serendipitous ``moves'' from one problem to another, which transform a problem that cannot be solved into one that can. However, it is important to notice that progress with problems does not always mean transforming a problem that cannot be solved into one that can. Progress may also apply to growth in the ability to \emph{posit} problems. In keeping track of progress, it would be useful for system designers to record (or get their systems to record) what problem a given system solves, and the degree to which the computer was responsible for coming up with this problem. % As Pease et al. \citeyearpar[p. 69]{pease2013discussion} remark, anomaly detection and outlier analysis are part of the standard machine learning toolkit -- but recognising \emph{new} patterns and defining \emph{new} problems is more ambitious (compare von Foerster's \citeyearpar{von2003cybernetics} second-order cybernetics). Establishing complex analogies between evolving problems and solutions is one of the key strategies used by teams of human designers \cite{Analogical-problem-evolution-DCC}. In computational research to date, the creation of new patterns and higher-order analogies is typically restricted to a simple and fairly abstract ``microdomain'' \cite{hofstadter1994copycat,DBLP:journals/jetai/Marshall06}. % Turning over increased responsibility to the machine will be important if we want to foster the possibility of genuine surprises. The {\sf SerenA} system developed by Deborah Maxwell et al.~\citeyear{maxwell2012designing} offers a case study in some of these concepts. This system is designed to support serendipitous discovery for its (human) users \cite{forth2013serena}. The authors rely on a process-based model of serendipity \cite{Makri2012,Makri2012a} that is derived from user studies which draw on interviews with 28 researchers. Study participants were asked to look for instances of serendipity from both their personal and professional lives. The research aims to support the formation of bridging connections from an unexpected encounter to a previously unanticipated but valuable outcome. The theory focuses on the acts of reflection that support both the creation of a bridge, and the estimation of the potential value of the result. % While this description touches on all of the features of our model, {\sf SerenA} largely matches the description offered by Andr{\'e} et al.~\citeyear{andre2009discovery} of discovery-focused systems, in which the user experiences an ``aha'' moment and takes the creative steps to realise the result. {\sf SerenA}'s primary computational method is to search outside of the normal search parameters in order to engineer potentially serendipitous (or at least pseudo-serendipitous) encounters. In sum, computer-supported serendipity has been well-studied, but purely computational serendipity has been much more constrained. This may partly be due to the absence of clear criteria for serendipity, which we address in the current paper. Another issue is the widespread reliance on microdomains. However, there are other underlying factors. Existing standards for assessing computational creativity have historically focused on product evaluations. \citeA{ritchie07} uses metrics that depend on properties that a reasonably sophisticated judge can ascribe to generated artifacts: ``typicality'', i.e., the extent to which an artifact belongs to a certain genre, and ``quality'' as atomic measures for more complex metrics, including ``novelty.'' Most often, the judge is assumed to be a human. % In recent years, artefact-centred evaluations are increasingly complemented by methods that consider process \cite{colton2008creativity} or a combination of product and process \cite{jordanous:12,colton-assessingprogress}. However, processes that arise outside of the control of the system (and ultimately, outside of the control of the researcher) may still be deemed out of scope for computational creativity \emph{per se}. Unexpected external effects may even be seen to ``invalidate'' research into computational creativity. We would argue that the concept of serendipity brings autonomous creative systems into clearer focus: not as an abstract notion of creativity \emph{sui generis}, but creativity in interaction with the world. This often requires a different mindset, and a different approach to system building and evaluation. %% \begin{quote} %% ``\emph{Tinkering is a process of serendipity-seeking that does not %% just tolerate uncertainty and ambiguity, it requires it. When %% conditions for it are right, the result is a snowballing effect %% where pleasant surprises lead to more pleasant surprises.}'' %% \cite[``Tinkering versus Goals'']{rao2015breaking} %% %% What makes this a problem-solving mechanism is diversity of individual perspectives coupled with the law of large numbers (the statistical idea that rare events can become highly probable if there are enough trials going on). If an increasing number of highly diverse individuals operate this way, the chances of any given problem getting solved via a serendipitous new idea slowly rises. This is the luck of networks. %% %% Serendipitous solutions are not just cheaper than goal-directed ones. They are typically more creative and elegant, and require much less conflict. Sometimes they are so creative, the fact that they even solve a particular problem becomes hard to recognize. For example, telecommuting and video-conferencing do more to “solve” the problem of fossil-fuel dependence than many alternative energy technologies, but are usually understood as technologies for flex-work rather than energy savings. %% %% Ideas born of tinkering are not targeted solutions aimed at specific problems, such as “climate change” or “save the middle class,” so they can be applied more broadly. As a result, not only do current problems get solved in unexpected ways, but new value is created through surplus and spillover. The clearest early sign of such serendipity at work is unexpectedly rapid growth in the adoption of a new capability. This indicates that it is being used in many unanticipated ways, solving both seen and unseen problems, by both design and “luck”. %% \end{quote} %% If we control the system, at bottom the best we can hope for is %% ``pleasant unsurprises.'' At the same time, understanding serendipity %% may help build autonomous systems that produce fewer ``unpleasant surprises,'' a %% serious contemporary concern %% \cite{philosophy-machine-morality,machine-ethics-status}. Thus, serendipity is particularly relevant for thinking about \emph{autonomous systems}. There is a certain amount of apprehension and concern in circulation around the idea of autonomous systems. \citeA{machine-ethics-status} suggest that these concerns ultimately come back to the question: will these systems behave in an ethical manner? The more we constrain the system's operation, the less chance there is of it ``running off the rails.'' However, constraints come with a serious downside. Highly constained systems will not be able to \emph{learn} anything very new while they operate. If this means that the system's ethical judgement is fixed once and for all, then we cannot trust it to behave ethically when circumstances change \cite{powers2005deontological}. Highly constrained systems are unlikely to be convincingly \emph{social}, inasmuch as the constraints rule out emergent behaviour in advance. Systems that only act normatively (that is, pursuing purposes for which they have been pre-programmed) serve as proxies for their creator's judgements, and do not make \emph{evaluations} that are in any way ``their own.'' Adapting qualitative artefact-oriented measures (like Ritchie's \citeyearpar{ritchie07}) may be necessary in order to build systems that are capable of carrying out the necessary formative evaluation steps that effect a focus shift -- as well as a final summative evaluation of the result. We return to this constellation of issues related to system autonomy below. % % Ritchie initially bases his metrics on human judgment, but points out different ways to compute them automatically, arising from practical study. For instance, quality could be computed using a fitness score of the assessed artifacts, which should highly correlate with human-perceived quality. The typicality of produced artifacts was calculated as their similarity to the artifacts inspiring the generative process. Nevertheless, this requires a good distant metric. Both fitness functions and distance metrics are subject to an ongoing debate in computational aesthetics. %% Although the notion of serendipity that we have developed is %% process-focused, value is a crucial dimension of serendipity, and %% evaluations of an outcome (often an artefact) continue to be relevant. %% Furthermore, \subsection{Challenges for future research} \label{sec:recommendations}

\vspace{2mm} \textbf{\emph{Successful error}}~ \begin{description}[leftmargin=0\parindent,labelindent=0em,itemsep=10pt] \item[{Context.}] You run an organisation with different divisions and contributors with{\em varied expertise}. expertise. People routinely discover interesting things that no one knows how to{\em turn into a product}. product. \item[{Problem.}] How can you get the most value from this sort of discovery? \item[{Solution.}] Allow people to work on pet projects, and encourage interaction between people in different divisions. Set aside time

% We used this model to analyse the potential for serendipity in case studies of evolutionary computing, recommender systems, and automated programming. We saw that the proposed framework can be used both retrospectively, as an evaluation tool, and prospectively, as a design tool. In every case, the model surfaced themes that can help to guide implementation. % We then reviewed related work: like \citeA{andre2009discovery}, we propose a two-part definition of serendipity: \emph{discovery} followed by \emph{invention}. % We then reflected back over our definition and analyses, and outlined a programme for serendipitous computing in the pursuit of \emph{autonomy}, \emph{learning}, \emph{sociality}, and \emph{embedded evaluation} that would tackle the following challenges: % \begin{itemize} \item \emph{A primary challenge for the serendipitous operation Our process-focused model of computers is developing computational agents that specify their own problems.} \item \emph{A second challenge is for computational agents to learn more serendipity elaborates both stages, and more about the world we live in.} \item \emph{A third challenge is for computational agents our case studies show how to interact in a recognisably social way with us and with each other, resulting in emergent effects.} \item \emph{A fourth challenge is for computational agents use this model to evaluate their own creative process existing and products.} \end{itemize} % hypothetical computer systems. In the current work, we have limited ourselves contrast to clarifying conceptual issues surrounding serendipty, and examining their implications for computational systems. % We indicate several possible further directions for implementation most prior work in each \emph{computational creativity}, for \emph{computational serendipity}, evaluation of various forms needs to be embedded inside of computational systems. In our discussion, we reflected back over the proposed model and case studies. studies, and outlined a programme of research into computational serendipity. We have also drawn attention to broader considerations in system design. Our examples show that serendipity is not foreign to computing practice. There are further gains to be had for research in computing by planning -- and programming -- for serendipity.%

``\emph{Like all intuitive operating, pure serendipity is not amenable to generation by a computer. The very moment I can plan or programme `serendipity' it cannot be called serendipity anymore}.'' \citep{van1994anatomy} \cite[p.~646]{van1994anatomy} \end{quote} We believe that serendipity is not so mystical as such statements might seem to imply. In Section \ref{sec:discussion} we will show that ``patterns of serendipity'' like those collected by van Andel can be applied in the design of computational systems. Purposive acts can have unintended consequences \cite{merton1936unanticipated}. Similarly, even if we cannot plan or program serendipity, we can prepare for it. If serendipity was ruled out as a matter of principle, computing would be restricted to happy or unhappy \emph{unsurprises} -- preprogrammed, preunderstood occurrences behaviour -- that would be interspersed periodically, perhaps, with an \emph{unhappy} surprise. (Perhaps this latter case would ultimately reduce to ``programmer oversight''.) Venkatesh Rao \citeyearpar{rao2015breaking} uses the term \emph{zemblanity} -- after William Boyd \citeyearpar{boyd2010armadillo}: ``zemblanity, the opposite of serendipity, the faculty of making unhappy, unlucky and

prerequisites for the development of autonomous systems that are worthy of our trust. While not the same as ``Serendipity as a Service'', such systems should at least be able to recognise serendipity when it happens. is happening. Over time a system might also come to recognise previous missed opportunities and false realisations -- what van Andel \citeyearpar[p.~639]{van1994anatomy} terms \emph{negative serendipity} -- and learn from them. Less controversial than ``programmed serendipity'', but no less worthy of study, is serendipity that arises in the course of user interaction. Indeed, it could be argued that everyday social media already offers something approaching ``Serendipity as a Service''. The user logs in on hoping, but with no without any guarantee, that they will find something interesting, charming, or entertaining, and ultimately potentially relevant to whatever is going on in their life at the moment. However, it should not be assumed that a any system that can accommodate user interaction can lead to generate serendipity; take for example the use of a calculator, where the potential for serendipity through user interaction is minimal. The frameworks introduced in this paper are broad enough to be used in the design and evaluation of sociotechnical systems, and we will touch on some examples, however we focus on modelling serendipity in a computational context. Section \ref{sec:literature-review} surveys the broad literature on serendipity including the etymology of the term itself, and itself. Section \ref{sec:by-example} assembles a novel catalog of historical examples of serendipity that we will use to scaffold our model. In Section \ref{sec:our-model} we present our own definition of serendipity, which synthesises the understanding gained from these historical examples, and prepares the way for evaluation of serendipity in

\section{Literature review} \section{Background} \label{sec:literature-review} % \subsection{Etymology and selected definitions}\label{sec:overview-serendipity} The English term ``serendipity'' derives from the 1302 long poem \emph{Eight Paradises}, written in Persian by the Sufi poet Am\={\i}r Khusrow in Uttar Pradesh, India. After translations into Italian, French, and finally English, its first chapter was known as ``The Three Princes of Serendip'', where ``Serendip'' ultimately corresponds to the Old Tamil-Malayalam word for Sri Lanka (%{\tam சேரன்தீவு}, \emph{Cerantivu}, island of the Ceran kings). %

%\input{2b-by-example.tex} \subsection{Serendipity by example: the condition, components, dimensions, and factors of serendiptious occurrences} \label{sec:by-example} % This section introduces key concepts for understanding serendipitous occurrences, and illustrates them by means of historical examples. %% We adapt the conceptual framework proposed by %% \citeA{pease2013discussion}. \subsubsection{Key condition for serendipity.} Serendipity relies on a reassessment or reevaluation -- a \emph{focus shift} in which something that was previously uninteresting, of neutral, or even negative value, becomes interesting. \begin{itemize} \item \textbf{Focus shift}: George de Mestral, an electrical engineer by training, and an experienced inventor, returned from a hunting trip in the Alps. He removed several burdock burrs from his clothes and his dog's fur and became curious about how they worked. After examining them under a microscope, he realised the possibility of creating a new kind of fastener that worked in a similar fashion, laying the foundations for the hook-and-loop mechanism in Velcro\texttrademark. % \cite[p. x]{roberts} \end{itemize} \subsubsection{Components of serendipity.} A focus shift is brought about by the meeting of a \emph{prepared mind} and a \emph{trigger}. The next step involves building a \emph{bridge} to a valuable \emph{result}. \begin{itemize} \item \textbf{Prepared mind}: Alexander Fleming's ``prepared mind'' included his focus on carrying out experiments to investigate influenza as well as his previous experience that showed that foreign substances in petri dishes can kill bacteria. He was concerned above all with the question ``Is there a substance which is harmful to harmful bacteria but harmless to human tissue?'' \cite[p. 161]{roberts}. \end{itemize} \begin{itemize} \item \textbf{Trigger}: The trigger does not directly cause the outcome, but rather, inspires a new insight. It was long known by Quechua medics that cinchona bark stops shivering. In particular, it worked well to stop shivering in malaria patients, as was observed when malarial Europeans first arrived in Peru. The joint appearance of shivering Europeans and a South American remedy was the trigger. That an extract from cinchona bark can cure and can even prevent malaria was learned subsequently. \end{itemize} \begin{itemize} \item \textbf{Bridge}: The bridge often includes reasoning techniques, such as abductive inference (what might cause a clear patch in a petri dish?), analogical reasoning (de Mestral constructed a target domain from the source domain of burrs hooked onto fabric), or conceptual blending (Kekul\'e, discoverer of the benzene ring structure, blended his knowledge of molecule structure with his dream image of a snake biting its tail). The bridge may be non-conceptual, relying on new social arrangements, or physical prototypes. It may have many steps, and may itself feature chance elements. Several serendipitous episodes may be chained together in sequence, on the way to an unprecedented result. C\'edric Villani \citeyear[pp.~15--16]{birth-of-a-theorem} describes two hallway conversations that happened in one day, the first with Freddy Bouchet, about the way galaxies stabilise -- ``I was thrilled to see Landau damping suddenly make another appearance, scarcely more than a week after my discussion with Cl\'ement [Mouhot]'' -- and the second with his colleague \'Etienne Ghys, who offered an unexpected link to Komolgorov-Arnold-Moser theory: ``I didn't really want to say anything, C\'edric, but those figures there on the board -- I've seen them before.'' \end{itemize} \begin{itemize} \item \textbf{Result}: This is the new product, artefact, process, theory, use for a material substance, or other outcome. The outcome may contribute evidence in support of a known hypothesis, or a solution to a known problem. Alternatively, the result may itself {\em be} a new hypothesis or problem. The result may be ``pseudoserendipitous'' in the sense that it was {\em sought}, while nevertheless arising from an unknown, unlikely, coincidental or unexpected source. More classically, it is an \emph{unsought} finding, such as the discovery of the Rosetta stone. \end{itemize} \subsubsection{Dimensions of serendipity.} The four components described above have attributes that may be present to a greater or lesser degree. These are: \emph{Chance} -- how likely was the trigger to appear?; \emph{Curiosity} -- how likely was this trigger to be identified as interesting?; \emph{Sagacity} -- how likely was it that the interesting trigger would be turned into a result?; -- and \emph{Value} (how valuable is the result that is ultimately produced?). \begin{itemize} \item \textbf{Chance}: Fleming \citeyear{fleming} noted: ``There are thousands of different moulds'' -- and ``that chance put the mould in the right spot at the right time was like winning the Irish sweep.'' It is important to notice that \emph{he} was in the right spot at the right time as well -- and that this was not a complete coincidence. The chance events we're interested in always include at least one observer. \end{itemize} \begin{itemize} \item \textbf{Curiosity}: Curiosity can dispose a creative person to begin or to continue a search into unfamiliar territory. We use this word to describe both simple curiosity and related deeper drives. Charles Goodyear \citeyear{goodyear1855gum} discoverer of the process for vulcanising rubber, first noticed that when the compound he was working with ``being carelessly brought into contact with a hot stove, charred like leather'' and in subsequent experiments observed that ``upon the edge of the charred portion appeared a line or border, that was not charred, but perfectly cured.'' In his autobiography he reflects on the role curiosity played in shaping his career: ``[F]rom the time his attention was first given to the subject, a strong and abiding impression was made upon his mind, that an object so desirable and important, and so necessary to man's comfort, as the making of gum-elastic available to his use, was most certainly placed within his reach. Having this presentiment, of which he could not divest himself, under the most trying adversity, he was stimulated with the hope of ultimately attaining this object.'' \end{itemize} \begin{itemize} \item \textbf{Sagacity}: This old-fashioned word is related to ``wisdom,'' ``insight,'' and especially to ``taste'' -- and describes the attributes, or skill, of the discoverer that contribute to forming the bridge between the trigger and the result. Merton \citeyearpar[p.~507]{merton1948bearing} writes: ``{[}M{]}en had for centuries noticed such `trivial' occurrences as slips of the tongue, slips of the pen, typographical errors, and lapses of memory, but it required the theoretic sensitivity of a Freud to see these as strategic data through which he could extend his theory of repression and symptomatic acts.'' The degree to which such data are \emph{prima facie} unanticipated and anomalous is clear. Merton seems prepared to accept without complaint that Freud's claims surrounding this data are part of ``an idealized story'' \cite{freudtheory}. For Merton ``what the observer brings to the datum'' is an essential aspect of strategy; his key criterion is that the result ``must permit of implications which bear upon generalised theory'' -- not that it be correct. The dimension of sagacity can also go some way towards describing, if not explaining, how it is this person rather than that person makes a particular societal contribution. For example, Edward Jenner was not the first person to observe that cowpox innoculation prevents smallpox contagion, but his experimental verification of this principle, and his development and promotion of the first ``vaccines'' were important scientific and social advances \cite{riedel2005edward}. \end{itemize} %% Note that the chance ``discovery'' of, say, a \pounds 10 note may %% be seen as happy by the person who finds it, whereas the loss of %% the same note would generally be regarded as unhappy. \begin{itemize} \item \textbf{Value}: Serendipity concerns happy surprises, but different parties may have different judgements as to whether a given situation is ``happy'' or ``surprising''. A third party judgement of value can help to discriminate between luck, sleight of hand, and bona fide value creation. Consider the difference between the two sayings ``One man's loss is another man's gain'' and ``One man's trash is another man's treasure.'' A literal example of the latter scenario is provided by the Swiss company Freitag, which was started by design students who built a business around ``upcycling'' used truck tarpaulins into bags and backpacks. Thanks in part to clever marketing \cite[pp. 54--55, 68--69]{russo2010companies}, their product is now a global brand. Wherever possible, we prefer to make use of independent judgements of value, which helps to capture a non-zero sum notion of value. \end{itemize} \subsubsection{Environmental factors.} Finally, serendipity seems to be more likely for agents who experience and participate in a \emph{dynamic world}, who are active in \emph{multiple contexts}, occupied with \emph{multiple tasks}, and who avail themselves of \emph{multiple influences}. \begin{itemize} \item \textbf{Dynamic world}: Information about the world develops over time, and is not presented as a complete, consistent whole. In particular, \emph{value} may come later. Van Andel \citeyear[p. 643]{van1994anatomy} estimates that in twenty percent of innovations ``something was discovered before there was a demand for it.'' To illustrate the role of this factor, it may be most revealing to consider a counterexample, in a case where dynamics were not attended to carefully and the outcome suffered as a result. Cropley \citeyear{cropley2006praise} describes the pathologist Eugen Semmer's failure to recognise the importance of the role of \emph{penicillium notatum} in restoring two unwell horses to health: ``Semmer saw the horses' return to good health as a problem that made it impossible for him to investigate the cause of their death, and reported \ldots\ on how he had succeeded in eliminating the mould from his laboratory!'' Whereas readiness to revise the approach as the situation changes is an important aspect of a prepared mind \cite{bereiter1997situated}, the changing situation itself clearly makes a contribution. \end{itemize} \begin{itemize} \item \textbf{Multiple contexts}: One of the dynamical aspects at play may be the discoverer/inventor going back and forth between different contexts with different stimuli. 3M employee Arthur Fry sang in a church choir and needed a good way to mark pages in his hymn book -- and happened to have been recently attending internal seminars offered by his colleague Spencer Silver about restickable glue. \end{itemize} \begin{itemize} \item \textbf{Multiple tasks}: Einstein's work at the patent office seems to have been fortuitous not because it gave him ideas, but because it gave him time to work on his ideas. Famously, this resulted in four fundamental papers in 1905. Two decades later, translating his correspondent Satyendra Nath Bose's paper from English to German, Einstein learned a calculation method that produced accurate physical results -- and that implicitly made nonstandard physical assumptions \cite{delbruck1980bose}. Subsequent examination of these ideas led to the discovery of Bose-Einstein statistics, which describes particles that do not obey the Pauli exclusion principle. The potential for interrelationships between the tasks is especially relevant, along with the ability to carry the tasks through to a satisfactory degree. In John Barth's \citeyearpar{barth1992last} novel \emph{The Last Voyage of Somebody the Sailor}, the author gives himself three main tasks: writing what reads as a straightforward semi-autobiographical novel, adapting a classic fairy tale, and interweaving (and finally merging) these two stories. The result includes numerous examples of what the text refers to as ``logistically assisted serendipity'' \cite[p.~311]{barth1992last}, through the use of images and plot points that repeat-with-variation. \end{itemize} \begin{itemize} \item \textbf{Multiple influences}: The bridge from trigger to result is often found by making use of a social network. For example, Arno Penzias and Robert Wilson, working at Bell Labs, used a large antenna to detect radio waves that were relayed by bouncing off satellites. After they had removed interference effects due to radar, radio, and heat, they found residual ambient noise that couldn't be eliminated. They were mystified, and only understood the significance of their work after a friend at MIT told them about a preprint by astrophysicists at near-by Princeton, who had hypothesised the possibility of measuring radiation released by the big bang. \end{itemize} \noindent In Sections \ref{sec:our-model} and \ref{sec:computational-serendipity}, we will show how the key condition, components, dimensions and environmental factors of serendipity discussed here can be modelled and assessed in computational systems. % \input{2c-related-work.tex}

The constituent terms in this definition are purposefully general: for our purposes it is their relationship that matters. A trigger, for example, is not defined in terms of a specific data structure, nor is a bridge constrained to be drawn from a specific set of reasoning techniques. We view such generality as a strength, but it does leave further work for anyone who aims to apply the definition in practice.The Section \ref{specs-overview} presents further structure that helps to make such that work more routine.

How can we we estimate the chance of the trigger appearing, if every trigger is unique? Consider de Mestral's encounter with burrs. The probability chance of encountering burrs while out walking is high: many people have had that experience. The unique features of de Mestral's experience are that he had the curiosity to investigate the burrs under a microscope, and the sagacity (and tenacity) to turn what he discovered into a successful product. The details of the particular burrs that were encountered effectively are essentially irrelevant. This shows that it is not essential for all factors contributing to the likelihood score to be ``low'' in order for a given process of discovery and invention to be deemed serendipitous. In the genera general case, we are not interested in the chance of encountering a particular object or set of data, which may be vanishingly small. data. Rather, we are interested the chance of encountering a some trigger that could precipitate an interested response. The trigger itself may be a complex object or event that takes place over a period of time; in other words, it may be a pattern, rather than a fact. Noticing that a pattern exists patterns is a key aspect of sagacity. sagacity, as well. Although it is in no way required by the SPECS methodology outlined above, many systems (including all of the examples below) have an iterative aspect. This means that a result may serve as a trigger for further discovery. In such a case it may be important for case, further indeterminacy may need to be introduced to the system, lest the results be convergent, and therefor, non-fallible. infallible. In applying the critera to such systems, we consider long-term behaviour.

between serendipity on the user side, and serendipity on the system side might be exploited. Current systems seek to induce serendipity by making use of implicit connections between clusters, resulting in an update to the user's conception of the item space. As in the example of {\sf SerenA} discussed in Section \ref{sec:related}, in In current systems recommender systems, the user shares a significant part of is given the workload when forming responsibility to form the bridge, even when triggered by the system. Users As a preliminary step towards building an artificially-intelligent recommender system, users might be explicitly given the explicit task of triggering tasks that are designed to trigger serendipity on the system-side, as well. system-side. \item The flowchart assembly process would need more stringent, and more meaningful, criteria for value before third-party observers

title={Four {PPPP}erspectives on {C}omputational {C}reativity}, author={Jordanous, Anna}, booktitle={Procs. of the AISB Symposium on Computational Creativity}, year={2015} year={2015}} @incollection{keller2014ubimus, title={Ubimus Through the Lens of Creativity Theories}, author={Keller, Dami{\'a}n and Lazzarini, Victor and Pimenta, Marcelo S}, booktitle={Ubiquitous Music}, pages={3--23}, year={2014}, publisher={Springer} }

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%% \noindent \textbf{Acknowledgement.} %% We appreciate the effort of our anonymous reviewers. \bibliographystyle{spbasic} \bibliography{./bibliography/biblio}