deletions | additions       diff --git a/8cc-intro.tex b/8cc-intro.tex  index 5f24ed4..8a84a1e 100644  --- a/8cc-intro.tex  +++ b/8cc-intro.tex 
...
conditions and preconditions that are conducive to serendipitous  discovery. Section \ref{sec:our-model}  distilled these elements into a computational model,  culminating in a standards method  for evaluating computational serendipity in Section \ref{specs-overview}.  %%  \citeA{pease2013discussion} used a an earlier  variantof  theseSPECS  criteria to analyse three examples of potentially serendipitous behaviour: dynamic  investigation problems, model generation, and poetry flowcharts. Two Three  additional examples are discussed below using our revised criteria. As Campbell \citeyear{campbell2005serendipity} writes, ``serendipity presupposes a smart mind,'' and these examples suggest potential directions for further work in computational intelligence.We then  turn to a more elaborated thought experiment that describes a new  system design that has been created with serendipity in mind.  Before describing these examples, as a baseline, we introduce the  notion of \emph{minimally serendipitous systems}. According to our standards, there are various ways to achieve a result with little or no serendipity: if the observation was likely, if further developments happened with little skill, and if the the value of the result was  low, then we would not say the outcome was serendipitous. We would be  prepared to attribute ``minimal serendipity'' to cases where the 
...
%% However, the current generation of text completion tools are known  %% more for infelicities than for exceptional wit.  \subsection{Case Studies: Prior art}  \label{sec:priorart}  \paragraph{An evolutionary Study: Evolutionary  music improvisation system.} improvisation} \label{sec:priorart}  \citeA{jordanous10} reported a computational jazz improvisation system using genetic algorithms. Genetic algorithms, and evolutionary computing more generally, could encourage computational serendipity. We examine Jordanous's system (later given the name {\sf GAmprovising} \cite{jordanous:12}) as a case study for evolutionary computing in the context of our model of computational serendipity: to what extent does {\sf GAmprovising} model serendipity? 
...
Evolutionary systems such as {\sf GAmprovising} necessarily operate in a \textbf{dynamic world} which is evolving continuously and that must, in particular, take into account the evolution of the user's tastes. \textbf{Multiple contexts} arise from the user changing their preferences over time and through the possibility of having multiple users evaluate the musical output. This variant version of the system is not yet implemented, but would be occupied with the more complex problem of satisfying multiple different users' preferences simultaneously. Moving to a more complex problem domain would require the system to be curious about more than one user at a time, and require greater sagacity if the system is to successfully satisfy multiple tastes. \textbf{Multiple tasks} are carried out by the system including evolution of Improvisors, generation of music by individual Improvisors, capturing of user ratings of a sample of the Improvisors' output, and fitness calculations. \textbf{Multiple influences} are captured through the various combinations of parameters that could be set and the potential range of values for each parameter.   %% Table \ref{caseStudies} summarizes how serendipity in such a system can be described in terms of our model.  \paragraph{Recommender systems.} \subsection{Case Study: Next-generation recommender systems}  % Stress distinction between serendipity on the system- vs. serendipity on the user's side.  As discussed in Section \ref{sec:related}, recommender systems are one  of the primary contexts in computing where serendipity is considered. Most discussions of serendipity in recommender systems focus on suggesting items to a user that will be likely to introduce new ideas that are unexpected, but close to what the user is already interested in. A recommendation of this type will be called (possibly \mbox{pseudo-)serendipitous}. As we noted, these systems mostly focus on supporting discovery, but some architectures also seem to take account of invention, such as the Bayesian methods surveyed in Chapter 3 of \citeNP{shengbo-guo-thesis}. Recommender systems \emph{stimulate} serendipitous discovery, by \emph{simulating} when this is likely to occur. In respect to related work, we therefore have to distinguish serendipity on the the user side from serendipity in the system.  
...
Note that in the course of evolution of these and other systems it is generally the system's developers who plan and perform adaptations: even in the Bayesian case, the system has limited autonomy. Nevertheless, the impetus to develop increasingly autonomous recommender systems is present, especially in complex domains where hand-tuning is either very cost-intensive or infeasible. With this challenge in mind, we investigate how serendipity could be achieved on the system side, and potentially be reflected back to the user. In terms of our model, current systems have at least the makings of a \textbf{prepared mind}, comprising both a user- and a domain model, both of which can be updated dynamically. User behaviour (e.g.~following up on certain recommendations) may serve as a \textbf{serendipity trigger} for the system, and change the way it makes recommendations in the future. A \textbf{bridge} to a new kind of recommendation may be found by pattern matching, and especially by looking for exceptional cases: when new elements are introduced into the domain which do not cluster well, or different clusters appear in the user model that do not have obvious connections between them. The intended outcome of recommendations depends on the organisational mission, and can in most cases be situated between making money and empowering the user. The serendipitous \textbf{result} on the system side would be learning a new approach that helps to address these goals.  %%%  \begin{table}[Ht!] The imperfect knowledge about the user's preferences and interests represents a main source of \textbf{chance}. Combined with the ability to learn, \textbf{curiosity} could be described as the urge to make recommendations specifically for the purposes of finding out more about users, possibly to the detriment of other metrics over the short term. Measures of \textbf{sagacity} would relate to the system's ability to draw useful inferences from user behaviour. For example, the system might decide to initiate an A/B test to decide how a novel recommendation strategy influences conversion. The \textbf{value} of recommendation strategies can be measured in terms of traditional business metrics or other organisational objectives.  Recommender systems have to cope with a \textbf{dynamic world} of changing user preferences and a changing collection of items to recommend. A dynamic environment which nevertheless exhibits some degree of regularity represents a precondition for useful A/B testing. As mentioned above the primary \textbf{(multiple) contexts} are the user model and the domain model. A system matching the description here would have \textbf{multiple tasks}: making useful recommendations, generating new experiments to learn about users, and building new models. Such a system could avail itself of \textbf{multiple influences} related to  experimental design, psychology, and domain understanding.  \subsection{Case Study: Automated flowchart assembly}  \textbf{serendipity triggers} ...  \textbf{prepared mind} ...  \textbf{bridge} ...  \textbf{result} ...  \textbf{chance} ...  \textbf{curiosity} ...  \textbf{sagacity} ...  \textbf{value} ...  \textbf{dynamic world} ...  \textbf{multiple contexts} ...  \textbf{multiple tasks} ...  \textbf{multiple influences} ...  \afterpage{\clearpage}  \begin{table}[p]  {\centering \renewcommand{\arraystretch}{1.5}  \footnotesize  \begin{tabular}{p{.7in}@{\hspace{.1in}}p{1.9in}@{\hspace{.1in}}p{1.9in}}  \multicolumn{1}{c}{} & \multicolumn{1}{c}{\textbf{Evolutionary music systems}} & \multicolumn{1}{c}{\textbf{Recommender \multicolumn{1}{c}{\textbf{Next Gen.~Recommender  systems}} \\[-.1in] \multicolumn{1}{l}{\em Components} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  \cline{2-3}  \textbf{Serendipity trigger} & Previous evolutionary operations together with user input & Input from user behaviour \\ 
...
\cline{2-3}  \end{tabular}  \par}  \normalsize  \bigskip  \caption{Summary: applying our computational serendipity model to two case studies\label{caseStudies}}  \end{table}%  \normalsize  %%%  The imperfect knowledge about the user's preferences and interests represents a main source of \textbf{chance}. Combined with the ability to learn, \textbf{curiosity} could be described as the urge to make recommendations specifically for the purposes of finding out more about users, possibly to the detriment of other metrics over the short term. Measures of \textbf{sagacity} would relate to the system's ability to draw useful inferences from user behaviour. For example, the system might decide to initiate an A/B test to decide how a novel recommendation strategy influences conversion. The \textbf{value} of recommendation strategies can be measured in terms of traditional business metrics or other organisational objectives.  Recommender systems have to cope with a \textbf{dynamic world} of changing user preferences and a changing collection of items to recommend. A dynamic environment which nevertheless exhibits some degree of regularity represents a precondition for useful A/B testing. As mentioned above the primary \textbf{(multiple) contexts} are the user model and the domain model. A system matching the description here would have \textbf{multiple tasks}: making useful recommendations, generating new experiments to learn about users, and building new models. Such a system could avail itself of \textbf{multiple influences} related to  experimental design, psychology, and domain understanding.  \medskip \subsection{Summary}  Table \ref{caseStudies} summarises how the components, dimensions and  factors of our model of serendipity can be mapped to evolutionary 
...
discussed above. These case studies have shown how our model can be  used to highlight the aspects of existing systems that would need to  be developed further to enhance measures of the system's serendipity.  \clearpage  %%%  %%%  % As a general comment, we would say that this is largely how  % \emph{research and development} of recommender systems works, but