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\subsection{Related work} \label{sec:related}  An active research community investigating computational models of serendipity exists in the field of information retrieval, and specifically, in recommender systems \cite{Toms2000}. In this domain, \citeA{Herlocker2004} and \citeA{McNee2006} view serendipity as an important factor for user satisfaction, next to accuracy and diversity. Serendipity in recommendations variously require the system to deliver an unexpected \emph{unexpected}  and useful \emph{useful}  \cite{Lu2012}, interesting \emph{interesting}  \cite{Herlocker2004}, attractive \emph{attractive}  or relevant \emph{relevant}  item \cite{Ge2010}. %% Recommendations are typically meant to help address the user's difficulty in finding items that meet his or her interests or demands within a large and potentially unobservable search space. The end user can also be passive, and items are suggested to support other stakeholder's goals, e.g. to increase sells.   Definitions differ as to the requirement of novelty; \emph{novelty};  \citeA{Adamopoulos2011}, for example, describe systems that suggest items that may already be known, but are still unexpected in the current context. In terms of our model, these systems focus mainly on producing a serendipity trigger, but they include aspects of user modeling which could bring other elements into play. Paul Andr{\'e} et al.~\citeyear{andre2009discovery} have examined  serendipity from a design perspective. These authors also propose a 
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Various social strategies, ranging from Writers Workshops to open  source software, pair programming, and design charettes  \cite[p. 11]{gabriel2002writer} have been developed to exploit similar  emergent effectsto develop new insights,  and to develop \emph{new} shared language. In \cite{poetry-workshop}, we investigate the feasibility of using designs of this sort in multi-agent systems that learn by sharing and discussing partial understandings. This earlier paper remains broadly indicative, however, and the ideas it describes can see considerable benefit from the more formal thinking we develop in the current work. \citeA{robot-rendezvous} develop a discussion of serendipitous  rendezvous in a multi-agent system for a graph exploration problem, in  which ``[h]aving more data about their colleagues, better decisions  are made about the potential serendipity path.'' This has some  similarity to the discursive scenario described above. above, and shows that  \emph{asymmetric partial knowledge} can support serendipitious  findings. The distinction between knowledge of other actors and  knowledge about an underlying domain is useful.  The issue of designing for serendipity has been taken up recently by  Deborah Maxwell et al.~\citeyear{maxwell2012designing}, in their 
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Although 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, and a  case study of a recommender system focused on serendipity. serendipity, in which  %%  Here, the %%  user is the primary agent with a prepared mind. Accordingly it isthe  user that undergoes experiences  an ``aha'' moment and takes the creative steps to realise the result; the computer is mainly used to facilitate this. 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. %% Another  %% earlier related example of this sort of system is {\sf Max}, created  %% by Figueiredo and Campos \citeyear{Campos2002}. The user emailed {\sf 
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Hofstadter and Mitchell's {\sf Copycat} and the subsequent {\sf  Metacat}, but these systems operated in a simple and fairly abstract  ``microdomain''  \cite{hofstadter1994copycat,DBLP:journals/jetai/Marshall06}.%%  More %% recently, similar architectures have been used recent work in this tradition is surveyed  inmusic  %%  \cite{eric-nichols-thesis}. The relationship between serendipity and novel problems receives  considerable attention in the current work, since we want to