assumptions about user actions. They
should be able to accept uncertain or
“fuzzy” design knowledge.
Challenge 5: Interactive
optimization. Given the challenges
we have outlined, it is clear that the
design of menus cannot yet be entirely
delegated to the system. To overcome
this limitation, we recently proposed an
optimization method for interactively
assisting menu designers with an
optimizer in the loop [ 1].
We argued that menu design
depends on human judgment, because
current predictive models cover only a
small subset of decision variables and
objectives. Additionally, the constraints
that govern the design process may be ill
defined or subjective.
The key challenge is how to
support designers’ abilities to cope
with uncertainty, define constraints,
and recognize good solutions while
menu performance. Only a handful of
models have been proposed over the
past few years, and they mainly focus
on a single response variable, such as
menu selection time. Moreover, not all
models are suitable for optimization.
Optimization relies on very fast
evaluation of a menu, as there is an
immense design space to explore.
This rules out full-fledged cognitive
simulations such as EPIC, GOMS, or
ACT-R, although there are linearized
simplifications such as KLM.
Challenge 4: Optimization methods.
We have argued so far that optimization
critically depends on a well-defined
design space, realistic objective
formulation, and accurate predictive
Only two optimization methods have
been proposed specifically for menu
systems. The first [ 6] used a genetic
algorithm and considers item frequency
with strong assumptions about the
hierarchy. We recently proposed a
hybrid algorithm [ 1] that first uses a
local search algorithm but with more
time also uses an ant colony optimizer.
On the one hand, the local search
algorithm can produce reasonable
local optima very quickly, helping the
designer to get an idea of the search
space. The ant colony algorithm, on
the other hand, operates in the scale
of minutes to hours. It makes fewer
assumptions about the search space and
thus has a chance of finding the global
optimum. But it is significantly slower.
Our solution was tested with designs of
up to 50 commands, and solution times
were as long as 15 minutes.
A key challenge will be to develop
methods that scale up to thousands
of commands and can handle more
complex multi-objective tasks. The
present methods also depend on explicit
Figure 3. Advanced menu
techniques use different
layouts and input: Flower
menu (left) and multitouch menu (right).
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Figure 4. MenuOptimizer assists in the design of menus: While the designer edits the menu, a model-based optimizer updates itself to provide
feedback and suggestions.
Item feedback indicates the frequency (line width)
and user performance over time (color gradient) Hotkeys and separators are
Item placement to improve
performance is suggested
Designers can lock items together
to accelerate optimization
Designers can normaly edit items
(move, delete, rename, etc.)