Many other derived I-spaces extend directly to continuous spaces, such as the limited-memory models of Section 11.2.4 and Examples 11.11 and 11.12. In the present context, it is extremely useful to try to collapse the I-space as much as possible because it tends to be unmanageable in most practical applications. Recall that an I-map, , partitions into sets over which a constant action must be applied. The main concern is that restricting plans to does not inhibit solutions.

Consider making derived I-spaces that approximate nondeterministic or
probabilistic I-states. Approximations make sense because is
usually a metric space in the continuous setting. The aim is to
dramatically simplify the I-space while trying to avoid the loss of
critical information. A trade-off occurs in which the quality of the
approximation is traded against the size of the resulting derived
I-space. For the case of nondeterministic I-states, *conservative
approximations* are formulated, which are sets that are guaranteed to
contain the nondeterministic I-state. For the probabilistic case,
*moment-based approximations* are presented, which are based on
general techniques from probability and statistics to approximate
probability densities. To avoid unnecessary complications, the
presentation will be confined to the discrete-stage model.