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\item \textbf{Learning}: Each of the case studies considered in  Section \ref{sec:computational-serendipity} describes a system that  is able, in one way or another, to learn from experience. As we  considered ways to enhance measures the measure  of serendipity in these examples, we were led to consider computational agents that  participate more  meaningfully in ``our world'' rather than in a circumscribed microdomain. Learning more about what fosters Knowledge-intensive development work may  often be unavoidable, but understanding how to foster  serendipity isparticularly important, because serendipity itself  plays such an  important role in because it points to  the growth potential  of knowledge. systems learning on  their own.  \emph{A second challenge is for computational agents to learn more and more about the world we live in.} \end{itemize}  \begin{itemize}  diff --git a/8cc.tex b/8cc.tex  index 4e2309e..7ce6de6 100644  --- a/8cc.tex  +++ b/8cc.tex 
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obvious criterion is that short-term value should be allowed to  suffer as long as expected value is still higher.  \item Theclearest way to enhance the serendipity of results from the  flowchart assembly process would be to impose need  more stringent, and more meaningful, criteria for value. value before third-party observers  would be likely to attribute serendipity to the system.  In addition to raising challenges for autonomous evaluation (as in the evolutionary music system case), this requirement would impose more sophisticated constaints on processing in earlier steps, which would require the system to become be  more sagacious. \end{enumerate}