Planning Questions and Answers

Q: What does the Sussman anomaly demonstrate about goal ordering?

Achieving one conjunct may destroy the conditions needed for another, so a planner must be prepared to interleave the work rather than completing each in turn. That is exactly the positive interaction the additive heuristic ignores. Stated order, difficulty ordering and universal decomposability are all refuted by the example. An assumption that holds in easy cases and fails in a small counterexample is precisely the kind that needs an explicit test.

Q: What is goal interaction in planning?

Interactions may be positive, where one action serves several goals, or negative, where achieving one undoes another, and both defeat a planner that treats goals independently. Mutual exclusion is the extreme negative case. Precedence and listing order are different notions. Recognising that both kinds of interaction exist explains why the additive heuristic overestimates and why the Sussman anomaly is hard, in one idea rather than two. A limit that survives making every external component ideal is an intrinsic one, and worth knowing for that reason.

Q: What is a serialisable goal set?

If no goal's achievement undoes another, the problem decomposes and a planner may work through them in turn, which is why such domains are much easier. The Sussman anomaly is the standard example of a non-serialisable set. Fixed sequences, decomposition into separate problems and importance ordering are different notions. Identifying when decomposition is sound, rather than assuming it, is what separates a correct planner from a plausible one. Checking the units of each quantity catches a wrong formula before the arithmetic begins.

Q: What is the practical role of planning in deployed systems?

Planners schedule spacecraft observations, control autonomous vehicles, route deliveries and sequence manufacturing operations, all domains where the actions and their effects are known and a sequence must be chosen. Question answering, learning and verification are served by other techniques. Identifying the shape of problem a technology suits, rather than its list of successes, is what allows it to be reached for at the right moment. The quantity that varies and the quantity that does not are worth naming before any substitution is attempted.

Q: What characterises a problem suited to a planner?

Planning applies where the dynamics are known and written down, and the question is which sequence of known actions reaches a stated goal. Where the dynamics must be learned, reinforcement learning applies instead, and the other options describe verification and classification. Asking whether the model exists, before choosing between planning and learning, is the question that settles which family of techniques is appropriate.

Q: What is the relationship between planning and reinforcement learning?

Both seek a course of action maximising some objective, and the difference is whether the transition model is given or must be learned from experience. Model based reinforcement learning sits between them by learning a model and then planning with it. Neither contains the other, both handle discrete and continuous cases. Placing two apparently distinct fields on a single axis, according to what is known in advance, makes the relationship between them immediately clear.

Q: What is the frame axiom problem that planning representations avoid?

Writing an axiom for every combination of action and fluent that is not affected would require a quadratic number of statements, most of them trivial, and the planning representation simply stipulates that unmentioned fluents persist. That stipulation is what makes the formalism concise. Reference frames, fluent selection and domain boundaries are different modelling questions. Adopting a convention that removes a quadratic bookkeeping burden is the single most important design decision in the representation.

Q: What is the price of the persistence convention?

The convention requires the effects to be complete, so an action with unknown or context dependent consequences cannot be described without extending the formalism. Conditional effects are a standard such extension. Negation is expressible, states list only true fluents, and conditional effects are supported by most modern languages. Knowing the assumption behind a convenience, and which extensions relax it, is what allows the formalism to be stretched safely.

Q: What is a conditional effect of an action?

Writing that moving a container also moves its contents, when it has any, requires an effect guarded by a condition evaluated in the state where the action occurs. It is how the ramification problem is handled within the formalism. Success conditions, numeric effects and delays are different extensions. Adding guarded effects, rather than writing a separate action for every case, is what keeps a domain description manageable.

Q: Why do conditional effects complicate regression search?

Working backwards must consider both the case where the condition held and the case where it did not, which branches the search. Forward search simply evaluates the condition. Reversibility, action counts and unconditional requirements are different issues. A feature that is cheap in one search direction and expensive in the other is worth noting when choosing between them, since the choice is otherwise made on other grounds entirely. Checking the units of each quantity catches a wrong formula before the arithmetic begins.

71.

What does the Sussman anomaly demonstrate about goal ordering?

A.The planner should achieve the hardest goal first in every case
B.Goal conjunctions can always be decomposed into independent parts
C.Subgoals may interact and cannot always be achieved independently
D.Goals should always be achieved in the order they are stated

Answer: C

Achieving one conjunct may destroy the conditions needed for another, so a planner must be prepared to interleave the work rather than completing each in turn. That is exactly the positive interaction the additive heuristic ignores. Stated order, difficulty ordering and universal decomposability are all refuted by the example. An assumption that holds in easy cases and fails in a small counterexample is precisely the kind that needs an explicit test.

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72.

What is goal interaction in planning?

A.Two goals that cannot both be true at the same time
B.A goal that must be achieved before another may be attempted
C.The order in which the goals are listed in the problem
D.Achieving one goal helps or hinders achieving another

Answer: D

Interactions may be positive, where one action serves several goals, or negative, where achieving one undoes another, and both defeat a planner that treats goals independently. Mutual exclusion is the extreme negative case. Precedence and listing order are different notions. Recognising that both kinds of interaction exist explains why the additive heuristic overestimates and why the Sussman anomaly is hard, in one idea rather than two. A limit that survives making every external component ideal is an intrinsic one, and worth knowing for that reason.

Discuss in Forum

73.

What is a serialisable goal set?

A.One whose goals may be achieved one after another without interference
B.One in which the goals must be achieved in a particular sequence
C.One that can be written as a sequence of separate planning problems
D.One whose goals are listed in the order of their importance

Answer: A

If no goal's achievement undoes another, the problem decomposes and a planner may work through them in turn, which is why such domains are much easier. The Sussman anomaly is the standard example of a non-serialisable set. Fixed sequences, decomposition into separate problems and importance ordering are different notions. Identifying when decomposition is sound, rather than assuming it, is what separates a correct planner from a plausible one. Checking the units of each quantity catches a wrong formula before the arithmetic begins.

Discuss in Forum

74.

What is the practical role of planning in deployed systems?

A.Verifying that a program meets its formal specification
B.Robotics, logistics, spacecraft operations and workflow
C.Answering natural language questions from a knowledge base
D.Learning the effects of actions from observational data

Answer: B

Planners schedule spacecraft observations, control autonomous vehicles, route deliveries and sequence manufacturing operations, all domains where the actions and their effects are known and a sequence must be chosen. Question answering, learning and verification are served by other techniques. Identifying the shape of problem a technology suits, rather than its list of successes, is what allows it to be reached for at the right moment. The quantity that varies and the quantity that does not are worth naming before any substitution is attempted.

Discuss in Forum

75.

What characterises a problem suited to a planner?

A.A requirement to prove a property of a system formally
B.A need to classify observations into known categories
C.Known actions with known effects and a goal to be achieved
D.A large data set from which behaviour must be learned

Answer: C

Planning applies where the dynamics are known and written down, and the question is which sequence of known actions reaches a stated goal. Where the dynamics must be learned, reinforcement learning applies instead, and the other options describe verification and classification. Asking whether the model exists, before choosing between planning and learning, is the question that settles which family of techniques is appropriate.

Discuss in Forum

76.

What is the relationship between planning and reinforcement learning?

A.Reinforcement learning is a special case of classical planning
B.The two solve entirely unrelated classes of problem
C.Planning applies to discrete and learning to continuous problems
D.Planning assumes a known model; learning acquires one by acting

Answer: D

Both seek a course of action maximising some objective, and the difference is whether the transition model is given or must be learned from experience. Model based reinforcement learning sits between them by learning a model and then planning with it. Neither contains the other, both handle discrete and continuous cases. Placing two apparently distinct fields on a single axis, according to what is known in advance, makes the relationship between them immediately clear.

Discuss in Forum

77.

What is the frame axiom problem that planning representations avoid?

A.Stating for every action what it leaves unchanged
B.Deciding which frame of reference to describe the world in
C.Choosing which fluents to include in the state description
D.Determining the boundary of the domain being modelled

Answer: A

Writing an axiom for every combination of action and fluent that is not affected would require a quadratic number of statements, most of them trivial, and the planning representation simply stipulates that unmentioned fluents persist. That stipulation is what makes the formalism concise. Reference frames, fluent selection and domain boundaries are different modelling questions. Adopting a convention that removes a quadratic bookkeeping burden is the single most important design decision in the representation.

Discuss in Forum

78.

What is the price of the persistence convention?

A.It prevents actions from having conditional effects
B.It only works when actions have fully specified effects
C.It makes the representation unable to express any negation
D.It forces every fluent to be listed in every state

Answer: B

The convention requires the effects to be complete, so an action with unknown or context dependent consequences cannot be described without extending the formalism. Conditional effects are a standard such extension. Negation is expressible, states list only true fluents, and conditional effects are supported by most modern languages. Knowing the assumption behind a convenience, and which extensions relax it, is what allows the formalism to be stretched safely.

Discuss in Forum

79.

What is a conditional effect of an action?

A.An effect whose value depends on a numeric computation
B.An effect that takes place after a delay from the action
C.An effect that occurs only when a further condition holds
D.An effect that occurs only if the action succeeds at all

Answer: C

Writing that moving a container also moves its contents, when it has any, requires an effect guarded by a condition evaluated in the state where the action occurs. It is how the ramification problem is handled within the formalism. Success conditions, numeric effects and delays are different extensions. Adding guarded effects, rather than writing a separate action for every case, is what keeps a domain description manageable.

Discuss in Forum

80.

Why do conditional effects complicate regression search?

A.Conditional effects cannot be reversed in any circumstances
B.The number of actions grows with the number of conditions
C.Regression requires every effect to be unconditional
D.Whether an effect occurred depends on the state before the action

Answer: D

Working backwards must consider both the case where the condition held and the case where it did not, which branches the search. Forward search simply evaluates the condition. Reversibility, action counts and unconditional requirements are different issues. A feature that is cheap in one search direction and expensive in the other is worth noting when choosing between them, since the choice is otherwise made on other grounds entirely. Checking the units of each quantity catches a wrong formula before the arithmetic begins.

Discuss in Forum

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