- Forward projections in
:
- Starting from a nondeterministic I-state, , and applying an action , derive an expression for the nondeterministic one-stage forward projection by extending the presentation in Section 10.1.2.
- Determine an expression for the two-stage forward projection starting from and applying and .

- Forward projections in
:
- Starting from a probabilistic I-state, , and applying an action , derive an expression for the probabilistic one-stage forward projection.
- Determine an expression for the two-stage forward projection starting from and applying and .

- Determine the strong and weak backprojections on for a given history I-state, . These should give sets of possible .
- At the end of Section 11.3.2, it was mentioned that an equivalent DFA can be constructed from an NFA.
- This problem involves computing probabilistic
I-states for Example 11.14. Let the initial I-state be
(11.90)

in which the th entry in the vector indicates . Let . For each action, a state transition matrix can be specified, which gives the probabilities . For , let be

The th entry of the th row yields . For , let be

The sensing model is specified by three vectors:(11.93)

(11.94)

and(11.95)

in which the th component yields . Suppose that and the history I-state obtained so far is(11.96)

The task is to compute the probabilistic I-state. Starting from , compute the following distributions: , , , , . - Explain why it is not possible to reach every nondeterministic
I-state from every other one for Example 11.7. Give an
example of a nondeterministic I-state that cannot be reached from the
initial I-state. Completely characterize the reachability of
nondeterministic I-states from all possible initial conditions.
**Figure 11.33:**(a) A topological graph in which a point moves (note that two vertices are vertically aligned). (b) An exercise that is a variant of Example 11.17. - In the same spirit as Example 11.21, consider a point moving on the topological graph shown in Figure 11.33. Fully characterize the connectivity of (you may exploit symmetries to simplify the answer).
- Design an I-map for Example 11.17 that is not necessarily sufficient but leads to a solution plan defined over only three derived I-states.
- Consider the discrete problem in Figure 11.33b,
using the same sensing and motion model as in Example 11.17.
- Develop a sufficient I-map and a solution plan that uses as few derived I-states as possible.
- Develop an I-map that is not necessarily sufficient, and a solution plan that uses as few derived I-states as possible.

- Suppose that there are two I-maps, and , and it is given that is sufficient with respect to , and is sufficient with respect to . Determine whether the I-map is sufficient with respect to , and prove your claim.
- Propose a solution to Example 11.16 that uses fewer nondeterministic I-states.
- Suppose that a point robot moves in
and receives observations from three homing beacons that are not
collinear and originate from known locations. Assume that the robot
can calibrate the three observations on
.
- Prove that the robot can always recover its position in .
- What can the robot infer if there are only two beacons?

- Nondeterministic I-state problems:
- Prove that the nondeterministic I-states for Example 11.23 are always a single connected region whose boundary is composed only of circular arcs and line segments.
- Design an algorithm for efficiently computing the nondeterministic I-states from stage to stage.

- Design an algorithm that takes as input a simply connected rectilinear region (i.e., described by a polygon that has all right angles) and a goal state, and designs a sequence of tray tilts that guarantees the ball will come to rest at the goal. Example 11.24 provides an illustration.
- Extend the game-theoretic formulation from Section 11.7.2 of history I-spaces to continuous time.
- Consider the ``where did I come from?''
problem.
- Derive an expression for .
- Derive an expression for .

- In the game of Example 11.27, could there exist a point in the game at which one player has not yet observed every possible ``hit'' yet it knows the state of the game (i.e., the exact location of all ships)? Explain.
- When playing blackjack in casinos, many
*card-counting*strategies involve remembering simple statistics of the cards, rather than the entire history of cards seen so far. Define a game of blackjack and card counting as an example of history I-states and an I-map that dramatically reduces the size of the I-space, and an information-feedback plan.

**Implementations**

- Implement the Kalman filter for the case of a robot moving in the plane. Show the confidence ellipsoids obtained during execution. Be careful of numerical issues (see [564]).
- Implement probabilistic I-state computations for a point robot moving in a 2D polygonal environment. Compare the efficiency and accuracy of grid-based approximations to particle filtering.
- Design and implement an algorithm that uses nondeterministic
I-states to play a good game of Battleship, as explained in Example
11.27.

Steven M LaValle 2020-08-14