Approximate computation involves trading solution quality for reduced computational cost or time.
Important research issues in flexible computation include development of anytime algorithms that can provide useful solutions at any time during their execution, and construction of performance profiles of these anytime algorithms, which specify a tradeoff between performance quality and computation cost. The goal of this research is to develop general and efficient methods for flexible and approximate computation using state-space search. Our approach is called state-space reduction. State-space reduction restricts search effort and focuses attention to the area of problem space that most likely contains the best solutions that can be found with available computation resources. Specifically, state-space reduction is a process of reducing a state space that is difficult to search into a less complex state space that is easier to explore.