Commit 9c6236ff authored by Markus Klinik's avatar Markus Klinik

coactive design definition

parent 01b16cbe
\section{Conclusion and Discussion}
\paragraph{Conclusion}
We have presented an algorithm that calculates solutions to the C2 scheduling problem.
The C2 scheduling problem expresses the scheduling requirements in damage control scenarios on board of navy ships.
It is a variant of the MSRCPSP, and extends it with resource affinity constraints and user-defined quality functions.
......@@ -10,6 +11,22 @@ Evolutionary algorithms calculate sets of solutions, which as a by-product conta
Furthermore, evolutionary algorithms do not need knowledge about the function they are trying to optimize.
This is useful for our purpose, because user-defined quality functions can be arbitrary Turing-complete calculations, and there can be no general heuristic that could take advantage of them to guide the search.
\paragraph{Coactive design}
We envision our algorithm to be part of an integrated mission management system, where the computer is part of the team.
Humans and machines should contribute to the joint activity of mission management with their respective strengths.
In his Ph.D. thesis, \citet{Johnson2014} argues that for interdependent teamwork, the computer must not be a black box.
Its internal process must be exposed.
In order to trust and rely on the computer, it must have three key properties: \emph{observability}, \emph{predictability}, and \emph{directability}.
He defines them as follows.
Observability is making relevant aspects of one the machine's status, knowledge and environment available to others.
Predictability means that others can rely on their prediction about the machine's behaviour.
Directability is the ability to be influenced by others.
This can be for example through direct commands, guidance, preferences, or suggestions.
\begin{itemize}[noitemsep]
\item Why no machine learning AI?
\item Johnson, coactive design: computer must be no black box (Fong)!
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......@@ -86,4 +86,4 @@ The best we can do is guarantee that populations do not get worse over time, by
Another problem with evolutionary algorithms is that their effectiveness depends on the input parameters, like chosen selection strategy, population size and mutation probability.
Which parameters work well can change with the problem instance, and may require some adjustments until satisfying results can be reliably produced.
End users, who are only interested in the results and not the inner workings of the algorithm should not be bothered with such technicalities.
We need further research and computer experiments with realistic scenarios to develop a parameter set that performs reasonably reliable in practice for our problem.
We need further research and computer with realistic scenarios to develop a parameter set that performs reasonably reliable in practice for our problem.
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