Select all true statements: For a system with a linear motion model and a nonlinear but differentiable observation likelihood function, a state estimator can be implemented using:

What is the optimal value function for a one-step horizon for s24?

The total number of possible policies in an MDP with discrete states and actions is finite.

For the following questions, what is the optimal value function for a two-step horizon with a discount factor of 1? [Note that the optimal value function for a two-step horizon is optimal sum of the (discounted) rewards after taking two steps]

What is the optimal value function for a one-step horizon for s14?

Is the L1 norm an admissible heuristic on this graph?

Breadth-first search:

Consider the game search tree with true utilities indicated at each leaf:

Which of the following vertices might A* with the provided heuristic explore in the third step?  Select all possible options.

Let’s work through an example of the robot localizing on this map. Let’s say that the robot has no idea of where it is initially. What would be the starting Belief? (Please round to the nearest hundredth)  p(xt=x1) = [beliefx1] p(xt=x2) = [beliefx2] p(xt=x3) = [beliefx3] p(xt=x4) = [beliefx4]