  In the real world, insufficient information, limited computation resources,
and complex problem structures often force an autonomous agent to make a
decision in time less than that required to solve the problem at hand
completely. Flexible and approximate computations are two approaches to
decision making under limited computation resources. Flexible computation helps
an agent to flexibly allocate limited computation resources so that the overall
system utility is maximized. Approximate computation enables an agent to find
the best satisfactory solution within a deadline. In this paper, we present two
state-space reduction methods for flexible and approximate computation:
quantitative reduction to deal with inaccurate heuristic information, and
structural reduction to handle complex problem structures. These two methods
can be applied successively to continuously improve solution quality if more
computation is available. Our results show that these reduction methods are
effective and efficient, finding better solutions with less computation than
some existing well-known methods.
