Picat The Planner Module Questions and Answers

Question 1

A developer is using Picat's planner module to find a path in a maze.
There are multiple paths from the start to the goal, with costs of 12, 15, and 18.
The developer uses the call `plan(StartState, 20, Plan, Cost)`.
Which of the following outcomes is guaranteed?

Accepted Answer

The planner will find one of the valid paths, but not necessarily the one with the cost of 12.

Answer

The planner will fail because an optimal plan was not explicitly requested.

Answer

The planner will deterministically find the path with the cost of 12.

Answer

The planner will find all possible paths with a cost less than or equal to 20.

Question 2

When defining a planning problem, what is the primary purpose of the optional `heuristic(State) = H` function?

Accepted Answer

To guide the search by providing an estimated cost from the current `State` to a final state, which is used to prune search paths.

Answer

To define an alternative, more efficient cost for actions.

Answer

To replace the `final/1` predicate with a more complex goal condition.

Answer

To store and retrieve memoized results for frequently encountered states.

Question 3

A developer is implementing the `action/4` predicate for a planning problem where the state is represented by a list of numbers. Which of the following rules inside the `action/4` predicate is invalid and will cause an error?

Accepted Answer

action(S, NextS, move, 1) => S[1] := S[1] + 1, NextS = S.

Answer

action(S, NextS, move, 1) => S = [H|T], NextS = [H+1|T].

Answer

action(S, NextS, move, 1) ?=> select(X, S, S_rem), NextS = [X+1|S_rem].

Answer

action(S, S, no_op, 0) => true.

Question 4

What is the core mechanism within Picat's planner module that prevents infinite loops when searching a state space that contains cycles?

Accepted Answer

The automatic tabling of every state encountered during the search.

Answer

The use of a resource limit in predicates like `plan/4`.

Answer

The requirement that all action costs must be positive integers.

Answer

The mandatory definition of a `final/1` predicate.

Question 5

In addition to the common `final(State)` predicate, the planner module also supports `final(State, Plan, Cost)`. What is the purpose of this alternative predicate?

Accepted Answer

It allows defining a condition where a final state can be reached from the current `State` by a known sequence of actions (`Plan`) with a given `Cost`.

Answer

It forces the final state to be reached with a specific `Plan` and `Cost`, otherwise the entire search fails.

Answer

It is used exclusively for debugging to log the final plan and cost found by the planner.

Answer

It calculates the final cost of the plan by applying a bonus or penalty based on the `State`.

Question 6

A planner model has two `action/4` rules that can potentially match the same state: one for moving a block (`move`), and another for painting it (`paint`). The developer wants the planner to be able to try painting a block if a `move` action from that state ultimately fails to find a solution. How should the `action/4` rules be defined?

Accepted Answer

Both rules must be defined using the backtrackable operator `?=>`.

Answer

Both rules must be defined using the non-backtrackable operator `=>`.

Answer

The `move` rule should use `=>` and the `paint` rule should use `?=>`.

Answer

The order of the rules does not matter, the planner automatically tries all valid actions.