%-*- Mode: Latex; Default-character-style: (:SWISS :ROMAN :NORMAL) -*-

\documentstyle[11pt]{article}
\newcommand{\mf}[2]{#1}
\setlength{\oddsidemargin}{0pt}
\setlength{\evensidemargin}{0pt}
\setlength{\topmargin}{-1.5cm}
\setlength{\textheight}{22.7cm}
\setlength{\textwidth}{6.5in}
\raggedbottom
\pagestyle{empty}

\begin{document}

\begin{center}
{\Large \bf Critiquing Therapy Plans by Incremental Improvement}\\[1mm]
{\large \bf (Extended Abstract)}\\[3mm]
Michael P. Wellman\footnote{Supported by National Institutes of Health Grant No.\
R01~LM04493 from the National Library of Medicine.}\\
MIT Laboratory for Computer Science\\
\end{center}

\begin{flushright}
Criticism comes easier than craftsmanship.\\
---Zeuxis, c.\ 400 B.\ C.\\[2mm]
% from Pliny the Elder, Natural History
Any jackass can kick down a barn, but it takes a good carpenter to build
one.\\
---Sam Rayburn
% quoted by Lyndon Johnson in 1952
\end{flushright}


Knowledge-based systems for medicine that interact with physicians in a
critiquing mode have received considerable attention in recent years.  Perry
Miller argues that medical consultation systems that critique rather than
dictate are likely to be more acceptable and useful to clinicians for a
variety of reasons~\cite[Sec.~1.3]{Miller-P86}.  Features of clinical
practice such as variability and subjectivity are cited as
motivations, as are the importance of explanation, the opportunity to inject
education into consultation, and other sociological factors of the
health-care-delivery environment.

These motivations are valid and perhaps sufficient to warrant a critiquing
approach.  However, there are also powerful {\it technical} advantages to
building critiquers rather than recommendation-generators, based on the
obvious fact that is simpler to criticize an idea than to generate one, and
easier to refine an existing design than to synthesize a better one from
scratch.  When examined computationally, this observation suggests
specific architectural features of critiquing systems.  In the rest of this
short note I discuss the nature of these technical considerations and sketch
an architecture designed to gain the most leverage from the critiquing mode
of interaction.

\section{The Critiquing Task}

The essential difference between the critiquing and straight recommendation
tasks is that in the former all discussion is relative to the therapy plan
proposed by the user.  It is this existence of a proposed therapy plan that
provides the advantage to the critiquer.  Though the user's plan might
represent creativity and knowledge well beyond the critiquer's scope and
capabilities, the critiquer may still be able to detect bugs, suggest
improvements, or otherwise contribute useful suggestions.  Without this
starting point, the recommendation-based program can only conceive of
plans deemed worthwhile with respect to its own limited knowledge and
creative capacity.

Adapting an existing recommendation-based program to perform critiquing
(the approach taken by Langlotz and Shortliffe with {\sc
oncocin}~\cite{Langlotz-Shortliffe83}, for example) fails to take advantage
of this inherent advantage of the critiquing task.  Although {\sc oncocin}
may have provided reasonable critiques, a program designed as a critiquer
from the start would probably have performed better.

\section{Critiquing by Incremental Refinement}

In a framework for constraint-posting planning described
elsewhere~\cite{Wellman87b,Wellman88a}, I show how planning can be
viewed as searching the space of admissible plans.  The planner applies a
dominance prover to prune this space based on its knowledge and ability to
deduce preferences among plans in specified classes.  The space is refined
by posting constraints on admissible plan classes and attempting to
determine admissibility of the new, restricted classes.  Unfocused, this
process may not lead to results of interest.  The planner is limited by the
power of its dominance prover and strength of its domain knowledge.  If an
unresolvable tradeoff is encountered, the planner will not get very far in
refining the subclasses of plans divided by this tradeoff issue.

Users will inevitably be able to make some choices unresolvable by the
planner.  This might be due to more domain knowledge, more information
about the case context, or greater willingness to make assumptions or
guesses.  A therapy plan proposed by the user will generally contain some
implicit tradeoff resolutions that would have caused the unfocused planner
to hang.

Thus, a user's proposed therapy serves to focus the dominance-proving
planner on a narrow slice of the search space that might have received only
fractional attention in unrestricted search.  Though the planner may not have
gotten there in the first place, it is possible that once there it can derive an
abundance of useful results.  Exploring the local region around the user's
plan, it might find dominating strategies or discover further refinements that
are justified by the tradeoff resolutions implicit in the nominated therapy.

\section{Discussion}

This informal description of the incremental improvement process serves
only to illustrate the {\em capacity} of this architecture to gain advantage
from the existence of a user's proposed plan.  Whether significant
advantages are achieved in practice is a matter for future empirical and
theoretical study.   However, similar concepts in other areas of AI suggest
that the phenomenon is a real one.

The idea of ``debugging almost-right plans'' has a long history in AI, perhaps
appearing first in Sussman's {\sc hacker}~\cite{Sussman75}.  More recently,
this paradigm has been generalized and discussed by Simmons and
Davis~\cite{Simmons87}.  Their experience indicates that in a variety of
domains there are significant advantages to debugging compared to
backtracking.

It appears that at least for the foreseeable future we are stuck with
consultation programs that have extremely narrow and incomplete
knowledge bases.  Using these systems to criticize experts based on the
program's own best guess is suboptimal and even unfair to users.  Starting
from the user's proposal permits the program to surmount its own lack of
creativity and work in a more constrained context where some assumptions
behind the user's plan hold.  In this environment, the program is more likely
to produce results of interest to those seeking its help.

% \begin{flushright}
% The critical power\ldots tends to make an intellectual situation of which the
% creative\\
% power can profitably avail itself\ldots to make the best ideas prevail.\\
% -- Matthew Arnold (1864)
% \end{flushright}

\small

%\bibliographystyle{custom}
\bibliographystyle{plain}
\bibliography{/u/medg/bibcite/abbrevs,/u/medg/bibcite/bib}

\end{document}

% One becomes a critic when one cannot be an artist, just as a man becomes
% a stool pigeon when he cannot be a soldier.
% -- Gustave Flaubert (1846)
