Planning Questions and Answers

Q: What is a precondition of an action?

Preconditions gate applicability, so a planner may apply an action only in states satisfying them, and achieving them becomes a subgoal in backward search. Effects describe what becomes true afterwards. Hopes and goal conditions are different notions. Separating what must hold before from what holds after is the whole structure of an action description, and every planning algorithm is organised around that division.

Q: What is an add effect of an action?

The add list names what becomes true and the delete list what becomes false, and everything unmentioned persists. The delete relaxation ignores the second list, which is what makes the relaxed problem monotonic. Preconditions and unchanged fluents are different categories. Knowing which list a heuristic ignores, and why that makes the relaxed problem easy, connects the representation directly to the heuristics computed from it.

Q: Which list of an action names the fluents it makes false?

The delete list names the fluents that cease to hold, and ignoring it gives the relaxation from which almost every modern planning heuristic is derived. Removing a fluent from the domain, negative preconditions and unknown values are different notions. That one list of a representation can be dropped to obtain a tractable relaxation is a happy accident of the formalism, and it is worth appreciating how much rests on it.

Q: Why does ignoring delete effects make planning easy?

Monotonic growth means an action applied now can never prevent one applied later, so a greedy fixed point computation finds every achievable fluent without search. Action counts, goal satisfaction and preconditions are unchanged. Recognising monotonicity as the property that removes the need for search, here as in forward chaining over definite clauses, is what makes the relaxation's tractability intelligible rather than surprising.

Q: What is the practical effect of good planning heuristics?

The relaxation heuristics turned greedy best first forward search into the dominant approach on standard benchmarks, displacing far more sophisticated algorithms. Complexity is unchanged, partial order planning remains correct and useful, and domain descriptions are still required. That better guidance rather than a better algorithm produced the advance is a lesson that applies to search generally and is worth carrying beyond this topic.

Q: What is the general shape of a modern classical planner?

The standard architecture grounds the problem, computes a relaxation based heuristic at each state and searches forward greedily, with enhancements for plateaux and dead ends. Satisfiability encodings and partial order planners remain in use for particular purposes. Exhaustive enumeration is hopeless. Knowing the default architecture, and the circumstances under which an alternative is preferred, is what allows a planner to be selected rather than assumed.

Q: What happens when a heuristic search planner reaches a plateau?

On a region where every successor has the same estimate, greedy search has no guidance, so an exhaustive local search is used until a strictly better state is found. Enforced hill climbing is the standard technique. Abandoning, restarting and accepting are all incorrect responses. Providing a defined fallback for the situation in which the primary mechanism gives no information is what distinguishes a robust implementation from a demonstration.

Q: What is a dead end in a planning search?

In domains with irreversible actions a state may be reached from which no plan exists, and a planner unable to detect that will search beneath it indefinitely. Detecting dead ends cheaply is an active research problem. Repeated states, inapplicability and thresholds are different conditions. A failure mode that arises only when actions cannot be undone is worth knowing about when choosing a planner for a domain in which they cannot.

Q: Why are irreversible actions difficult for a planner?

In a reversible domain any mistake can be undone, so greedy search recovers, whereas an irreversible action may commit the agent to a state with no plan. Breaking a fragile object is the standard example. Representation and heuristics are unaffected, and irreversibility does not in itself create a dead end. Asking whether mistakes are recoverable, before choosing a search strategy, determines how cautious that strategy must be.

Q: What is safe exploration in a planning or learning agent?

Where some actions cause permanent harm, an agent must recognise and avoid them while still exploring enough to learn, which is a genuinely difficult requirement. It matters most in physical systems where a mistake damages equipment or people. Visited states, bounded regions and spare capacity are different restrictions. A requirement arising from the consequences of action rather than from the algorithm is one that the algorithm must be extended to respect.

81.

What is a precondition of an action?

A.A condition that must hold for the action to be applicable
B.A condition that becomes true once the action has completed
C.A condition the planner hopes will hold when the action runs
D.A condition attached to the goal rather than to the action

Answer: A

Preconditions gate applicability, so a planner may apply an action only in states satisfying them, and achieving them becomes a subgoal in backward search. Effects describe what becomes true afterwards. Hopes and goal conditions are different notions. Separating what must hold before from what holds after is the whole structure of an action description, and every planning algorithm is organised around that division.

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82.

What is an add effect of an action?

A.A fluent whose value the action leaves unchanged
B.A fluent the action makes true
C.A fluent the action makes false
D.A fluent the action requires to be true beforehand

Answer: B

The add list names what becomes true and the delete list what becomes false, and everything unmentioned persists. The delete relaxation ignores the second list, which is what makes the relaxed problem monotonic. Preconditions and unchanged fluents are different categories. Knowing which list a heuristic ignores, and why that makes the relaxed problem easy, connects the representation directly to the heuristics computed from it.

Discuss in Forum

83.

Which list of an action names the fluents it makes false?

A.A fluent that must be false for the action to apply
B.A fluent that the action leaves in an unknown state
C.A fluent the action makes false
D.A fluent the action removes from the domain entirely

Answer: C

The delete list names the fluents that cease to hold, and ignoring it gives the relaxation from which almost every modern planning heuristic is derived. Removing a fluent from the domain, negative preconditions and unknown values are different notions. That one list of a representation can be dropped to obtain a tractable relaxation is a happy accident of the formalism, and it is worth appreciating how much rests on it.

Discuss in Forum

84.

Why does ignoring delete effects make planning easy?

A.The resulting problem has fewer actions to consider
B.The goal becomes true immediately in the relaxed problem
C.Ignoring deletions removes all the action preconditions
D.The set of true fluents only grows, so no choice is ever regretted

Answer: D

Monotonic growth means an action applied now can never prevent one applied later, so a greedy fixed point computation finds every achievable fluent without search. Action counts, goal satisfaction and preconditions are unchanged. Recognising monotonicity as the property that removes the need for search, here as in forward chaining over definite clauses, is what makes the relaxation's tractability intelligible rather than surprising.

Discuss in Forum

85.

What is the practical effect of good planning heuristics?

A.Simple forward search became competitive with elaborate planners
B.Planning problems became solvable in polynomial time
C.Partial order planning was shown to be incorrect
D.Domain descriptions became unnecessary for planning

Answer: A

The relaxation heuristics turned greedy best first forward search into the dominant approach on standard benchmarks, displacing far more sophisticated algorithms. Complexity is unchanged, partial order planning remains correct and useful, and domain descriptions are still required. That better guidance rather than a better algorithm produced the advance is a lesson that applies to search generally and is worth carrying beyond this topic.

Discuss in Forum

86.

What is the general shape of a modern classical planner?

A.Encoding into satisfiability and solving with a general solver
B.Forward search guided by a heuristic from the delete relaxation
C.Exhaustive enumeration of every possible action sequence
D.Partial order search with least commitment throughout

Answer: B

The standard architecture grounds the problem, computes a relaxation based heuristic at each state and searches forward greedily, with enhancements for plateaux and dead ends. Satisfiability encodings and partial order planners remain in use for particular purposes. Exhaustive enumeration is hopeless. Knowing the default architecture, and the circumstances under which an alternative is preferred, is what allows a planner to be selected rather than assumed.

Discuss in Forum

87.

What happens when a heuristic search planner reaches a plateau?

A.It restarts from the initial state with a different heuristic
B.It accepts the current state as a solution to the problem
C.It performs a breadth first search until the heuristic improves
D.It abandons the search and reports that no plan exists

Answer: C

On a region where every successor has the same estimate, greedy search has no guidance, so an exhaustive local search is used until a strictly better state is found. Enforced hill climbing is the standard technique. Abandoning, restarting and accepting are all incorrect responses. Providing a defined fallback for the situation in which the primary mechanism gives no information is what distinguishes a robust implementation from a demonstration.

Discuss in Forum

88.

What is a dead end in a planning search?

A.A state the planner has already visited earlier in the search
B.A state in which no action is applicable whatever
C.A state whose heuristic estimate exceeds a chosen threshold
D.A state from which the goal cannot be reached at all

Answer: D

In domains with irreversible actions a state may be reached from which no plan exists, and a planner unable to detect that will search beneath it indefinitely. Detecting dead ends cheaply is an active research problem. Repeated states, inapplicability and thresholds are different conditions. A failure mode that arises only when actions cannot be undone is worth knowing about when choosing a planner for a domain in which they cannot.

Discuss in Forum

89.

Why are irreversible actions difficult for a planner?

A.A wrong choice may make the goal permanently unreachable
B.Irreversible actions cannot be represented in the formalism
C.The planner cannot compute a heuristic for such actions
D.Every irreversible action creates a dead end immediately

Answer: A

In a reversible domain any mistake can be undone, so greedy search recovers, whereas an irreversible action may commit the agent to a state with no plan. Breaking a fragile object is the standard example. Representation and heuristics are unaffected, and irreversibility does not in itself create a dead end. Asking whether mistakes are recoverable, before choosing a search strategy, determines how cautious that strategy must be.

Discuss in Forum

90.

What is safe exploration in a planning or learning agent?

A.Exploring only when the agent has spare computation available
B.Avoiding actions that could lead to unrecoverable states
C.Exploring only states the agent has visited before
D.Restricting exploration to a bounded region of the space

Answer: B

Where some actions cause permanent harm, an agent must recognise and avoid them while still exploring enough to learn, which is a genuinely difficult requirement. It matters most in physical systems where a mistake damages equipment or people. Visited states, bounded regions and spare capacity are different restrictions. A requirement arising from the consequences of action rather than from the algorithm is one that the algorithm must be extended to respect.

Discuss in Forum

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