This section defines the I-map from Figure 11.3, which converts each history I-state into a probability distribution over . A Markov, probabilistic model is assumed in the sense that the actions of nature only depend on the current state and action, as opposed to state or action histories. The set union and intersection of (11.30) and (11.31) are replaced in this section by marginalization and Bayes' rule, respectively. In a sense, these are the probabilistic equivalents of union and intersection. It will be very helpful to compare the expressions from this section to those of Section 11.2.2.
Rather than write , standard probability notation will be applied to obtain . Most expressions in this section of the form have an analogous expression in Section 11.2.2 of the form . It is helpful to recognize the similarities.
The first step is to construct probabilistic versions of and . These are and , respectively. The latter term was given in Section 10.1.1. To obtain , recall from Section 11.1.1 that is easily derived from . To obtain , Bayes' rule is applied:
Now consider defining probabilistic I-states. Each is a probability distribution over and is written as . The initial condition produces . As for the nondeterministic case, probabilistic I-states can be computed inductively. For the base case, the only new piece of information is . Thus, the probabilistic I-state, , is . This is computed by letting in (11.35) to yield
Now consider the inductive step by assuming that is given. The task is to determine , which is equivalent to . As in Section 11.2.2, this will proceed in two parts by first considering the effect of , followed by . The first step is to determine from . First, note that
The next step is to take into account the observation . This is accomplished by making a version of (11.35) that is conditioned on the information accumulated so far: and . Also, is replaced with . The result is
The probabilistic I-space (shown in Figure 11.3) is the set of all probability distributions over . The update expressions, (11.38) and (11.39), establish that the I-map is sufficient, which means that the planning problem can be expressed entirely in terms of , instead of maintaining histories. A goal region can be specified as constraints on the probabilities. For example, from some particular , the goal might be to reach any probabilistic I-state for which .
The triangular region in
is an uncountably infinite set, even
though the history I-space is countably infinite for a fixed initial
condition. This may seem strange, but there is no mistake because for
a fixed initial condition, it is generally impossible to reach all of
the points in
. If the initial condition can be any point
, then all of the probabilistic I-space is covered
, in which
is the initial condition
Steven M LaValle 2020-08-14