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%  The likelihood of serendipitous evolution is greatly enhanced by the use of mutation and crossover operations within the genetic algorithm, to increase the diversity of search space covered by the system during evolution. However the \textbf{chance} of any particular Improvisor being discovered is low, given the massive dimensions of the search space. Interesting developments in evolution would be guided by \textbf{curiosity} through the particular human user identifying Improvisors as interesting at that time. \textbf{Sagacity} is determined by how likely the user is to appreciate the same Improvisor's music (or similar music) over time, as tastes of the user may change. The \textbf{value} of the results are maximised through employing a fitness function.  Evolutionary systems such as GAmprovising necessarily operate in a \textbf{dynamic world} which is evolving continuously and may also be affected by changes in user tastes as they evaluate musical output from Improvisors. The \textbf{multiple contexts} arise from the possibility of having multiple users evaluate the musical output (though this is as yet not implemented formally) or through the user changing their preferences over time. \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. Tbl. \ref{caseStudies} summarizes how serendipity in such a system can be described in terms of our model.  \paragraph{Recommender systems.}  
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As discussed in Section \ref{sec:related}, recommender systems are one  of the primary contexts in computing where serendipity is addressed. 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. If the latter connection exists, such a system must be called pseudoserendipitous. As we noted, these systems mostly focus on 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 side of the user from serendipity in the system.   Current research mainly focusses on addresses  the first aspect. The present approaches focus on finding aspect and tries to find  and assessing assess  \textbf{serendipity triggers} by exploiting patterns in the search space. For example, \cite{Herlocker2004} as well as \cite{Lu2012} associate less popular items with a higher unexpectedness. Clustering was also frequently used to discover latent structures in the search space. For example, \cite{Kamahara2005} partition users into clusters of common interest, while \cite{Onuma2009} as well as \cite{Zhang2011} perform clustering on both users and items. In the work by \cite{Oku2011}, the user is allowed to select two items in order to mix their features in some sort of conceptual blending. Note that it is typically the system's \emph{developers} developers  who adapt the system; even in the Bayesian case, the system has limited autonomy. Nevertheless, the impetus to develop increasingly autonomous systems is present, especially in complex domains where hand-tuning is either very cost-intensive or infeasible. In the context of this paper, we therefore want to 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 these 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: 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 resemble a novel learnt approach that helps to address these goals. %%%  \begin{table}[Ht!] 
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\textbf{Result} & Music generated by fittest Improvisors& Dependent on organisation goals \\ \cline{2-3}  \multicolumn{1}{l}{\em Dimensions} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  \cline{2-3}  \textbf{Chance} & If discovered in huge search space & If Through imperfect knowledge/if  learning from user behaviour \\ % \cline{2-3}  \textbf{Curiosity} & If a particular user notes an Improvisor & Making unusual recommendations \\  % \cline{2-3} 
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\normalsize  %%%  The imperfect knowledge about the user's preferences and interests represents a main component of \emph{chance}. Furthermore, chance can play an important role if a system had the capacity to learn from user behaviour. Combined with the ability to learn, \textbf{curiosity} could be described as the urge to make recommendations specifically for the purposes of learning 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 inferences from user behaviour. For example, the system might do A/B testing to decide how novel recommendation strategies influence conversion.Again, currently this would typically be organised by the system's developers.  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 preference ratings and new items in the system. At the same time, such a dynamic environment which nevertheless exhibits some 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. Tbl. \ref{caseStudies} summarizes how the components, dimensions and factors of our model could be mapped to recommender systems, in comparison to evolutionary music systems from computational creativity.  % As a general comment, we would say that this is largely how  % \emph{research and development} of recommender systems works, but  diff --git a/ww-analysis.tex b/ww-analysis.tex  index 547b195..c359170 100644  --- a/ww-analysis.tex  +++ b/ww-analysis.tex 
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hypothetical discussions and exchange of views between computational  poetry systems as our example of a situation where social  circumstances could encourage serendipity. We note that similar ideas  would apply for prose and, with further adaptation, other arts. arts.\\  \paragraph{Thought Experiment: Prepared \noindent \textbf{Prepared  mind.} Participating systems need to be able to follow the protocol. This  means that participating systems will need components like those  listed above. The {\tt listening} and {\tt questions} components of 
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in the Workshop may have a catalogue of outstanding unresolved, or  partially resolved, problems (denoted ``X'' in the schematic above).  Embodied in code, they may drive comments, questions, and other  behaviour -- and they may be answered in unexpected ways.  \paragraph{Thought Experiment: Serendipity triggers.} ways.\\  \noindent \textbf{Serendipity triggers.}  Although the poem is under the control of the initial generative  subsystem, it is \emph{not} under control of the listening subsystem.  The listening subsystem expects some poem, but it does not know what 
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$T^\star$ may seen as an evolving vector with shared state, but viewed  and handled from different perspectives. With multiple agents  involved in the discussion, the ``comment generator'' module would  expand to contain its own feedback loops.  \paragraph{Thought Experiment: Bridge.} loops.\\  \noindent \textbf{Bridge.}  Feedback on portions of the poem may lead the system to identify new  problems, indeed, new \emph{types} of problems that it hadn't  considered before. The most immediately feasible case is one in which 
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where they come from. Although computers are currently nowhere close,  the reconstructive process may steadily approach the ideal case --  familiar to humans -- of relating to the sentiment expressed by the  poem as a whole \cite[p. 209]{bergson1983creative}. 209]{bergson1983creative}.\\  %% Several of us are involved with a contemporary project  %% \cite{coinvent14} to develop a formal theory of concept invention, 
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%% Counting Breathing Position Distribution Phonics Rhythm Repetition  %% Thematic Narrative Entropy  \paragraph{Thought Experiment: Result.} \noindent \textbf{Result.}  The final step is to take the problem or problems that were  identified, and write new code to solve them. Several strategies for  generating a result $R$, in the form of new code, were described 
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feedback modules, after reflecting on questions like: ``How might the  critic have detected that feature in my poem?''  \paragraph{Thought {Thought  Experiment: Likelihood scores and potential value.} Given most statements in natural language are new, we can assume that  most poems consumed by the system would never have been seen before,  and the chance of observing a given serendipity trigger would be very