210 lines
7.9 KiB
Markdown
210 lines
7.9 KiB
Markdown
# Output Format
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`plasp` 3 translates SAS and PDDL files into a uniform ASP fact format.
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## Overview
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Essentially, `plasp`’s output format consists of [state variables](#variables) that are modified by [actions](#actions) if their preconditions are fulfilled.
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Variables reference [entities](#constants-objects) that are affected by the actions.
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As with PDDL, the objective is to achieve a specific [goal](#goal) starting from an [initial state](#initial-state) by executing a sequence of actions.
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`plasp`’s variables correspond to the multivalued variables in SAS.
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PDDL predicates are turned into Boolean variables to make the output format consistent.
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Actions are modeled exactly as PDDL actions and SAS operators.
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## In a Nutshell
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The following illustrates `plasp`’s output format for the problem of turning switches on and off.
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```prolog
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% declares the type "type(switch)"
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type(type(switch)).
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% introduces a switch "constant(a)"
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constant(constant(a)).
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has(constant(a), type(switch)).
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% declares a variable "variable(on(X))" for switches X
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variable(variable(on(X))) :- has(X, type(switch)).
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% the variable may be true or false
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contains(variable(on(X)), value(on(X)), true)) :- has(X, type(switch)).
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contains(variable(on(X)), value(on(X)), false)) :- has(X, type(switch)).
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% declares the action "action(turnOn(X))", which requires switch X to be off and then turns it on
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action(action(turnOn(X))) :- has(X, type(switch)).
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precondition(action(turnOn(X)), variable(on(X)), value(on(X), false)) :- has(X, type(switch)).
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postcondition(action(turnOn(X)), effect(0), variable(on(X)), value(on(X), true)) :- has(X, type(switch)).
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% initially, the switch is off
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initialState(variable(on(constant(a))), value(on(constant(a)), false)).
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% in the end, the switch should be on
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goal(variable(on(constant(a))), value(on(constant(a)), true)).
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```
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## Syntax and Semantics
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`plasp` structures the translated ASP facts into multiple sections, which are explained in the following.
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### Feature Requirements
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```prolog
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% declares a required feature
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requires(feature(<name>)).
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```
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`plasp` recognizes and declares advanced features used by the input problem, such as conditional effects, [mutex groups](#mutex-groups) and [axiom rules](#axiom-rules) (currently only SAS).
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See the [full list of supported features](feature-requirements.md) for more information.
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The feature requirement predicates may be used in meta encodings to warn about unsupported features.
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### Types
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```prolog
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% declares a <type>
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type(type(<name>)).
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% specifies that <type 1> inherits <type 2>
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inherits(type(<type 1>), type(<type 2>)).
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% specifies <constant> to be of type type(<name>)
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has(<constant>, type(<name>)).
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```
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[Variables](#variables), [constants](#constants-objects), and [objects](#constants-objects) may be typed. Types are only available with PDDL and if typing is enabled.
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`plasp` automatically generates all matching `has` predicates for objects with types that inherit other types.
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### Variables
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```prolog
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% declares a <variable>
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variable(variable(<name>)).
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% adds a <value> to the domain of a <variable>
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contains(<variable>, <value>).
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```
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`plasp`’s variables represent the current state of the planning problem.
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Variables are linked to the problem's [objects](#constants-objects) and [constants](#constants-objects).
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`plasp`’s variables are multivalued, and each variable has exactly one value at each point in time.
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With SAS, variable names are numbers starting at 0, `variable(<number>)`.
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SAS variables are inherently multivalued, which results in two or more values of the form `value(<SAS predicate>, <bool>)` for each variable.
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With PDDL, Boolean variables are created from the PDDL predicates.
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Variables ared named after the PDDL predicates, `variable(<PDDL predicate>).`
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Each variable contains exactly two values (one `true`, one `false`) of the form `value(<PDDL predicate>, <bool>)`.
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Note that with PDDL, variables and values are named identically.
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### Actions
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```prolog
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% declares an <action>
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action(action(<name>)).
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% defines that as a precondition to <action>, <variable> must have value <value>
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precondition(<action>, <variable>, <value>).
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% defines that after applying <action>, <variable> is assigned <value>
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postcondition(<action>, effect(<number>), <variable>, <value>).
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% defines the condition of a conditional effect
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precondition(effect(<number>), <variable>, <value>).
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% specifies the costs of applying <action>
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costs(<action>, <number>).
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```
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Actions may require certain variables to have specific values in order to be executed.
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After applying an action, variables get new values according to the action's postconditions.
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Actions may have *conditional effects*, that is, certain postconditions are only applied if additional conditions are satisfied.
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For this reason, each conditional effect is uniquely identified with a predicate `effect(<number>)` as the second argument of the `postcondition` facts.
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The conditions of conditional effects are given by additional `precondition` facts that take the respective `effect(<number>)` predicates as the first argument.
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Unconditional effects are identified with `effect(unconditional)`.
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Conditional effects are currently only supported with SAS input problems.
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Actions may also have *action costs* required to apply them. Action costs are currently supported for SAS only.
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### Constants/Objects
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```prolog
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% declares a <constant> or object
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constant(constant(<name>)).
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% specifies <constant> to be of type type(<name>)
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has(<constant>, <type>).
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```
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Constants and objects are the entities that are affected by [actions](#actions), for instance, the blocks in a Blocks World problem.
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Constants are global for a domain, while objects are problem-specific.
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`plasp` does not distinguish between the two (modeling both as constants), as both are identically used static identifiers.
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### Initial State
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```prolog
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% initializes <variable> with a specific <value>
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initialState(<variable>, <value>).
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```
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The initial state contains all [variable](#variables) assignments that hold before executing any [actions](#actions).
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Note that with PDDL, the initial state might not assign values to all variables. Instead, unassigned values have to be assigned `false` manually.
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### Goal
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```prolog
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% specifies that <variable> shall obtain <value> in the end
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goal(<variable>, <value>).
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```
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The goal specifies all variable assignments that have to be fulfilled after executing the plan.
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### Mutex Groups
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```prolog
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% declares a <mutex group>
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mutexGroup(mutexGroup(<number>)).
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% adds the assignment of <variable> to <value> to a <mutex group>
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contains(<mutex group>, <variable>, <value>).
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```
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SAS contains information about mutually exclusive [variable](#variables) assignments.
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That is, *at most one* variable assignment of each mutex group must be satisfied at all times.
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Mutex group facts are only present with SAS input programs and not PDDL.
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Mutex groups contain essential information in order to find plans correctly.
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That is, if mutex groups are present in `plasp`’s output, they have to be accounted for appropriately.
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### Axiom Rules
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```prolog
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% declares an <axiom rule>
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axiomRule(axiomRule(<number>)).
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% defines that as a precondition to <axiom rule>, <variable> must have value <value>
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precondition(<axiom rule>, <variable>, <value>).
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% defines that after applying <axiom rule>, <variable> is assigned <value>
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postcondition(<axiom rule>, effect(unconditional), <variable>, <value>).
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```
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Axiom rules are similar to [actions](#actions) in that they modify [variables](#variables) if certain preconditions are satisfied.
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However, axiom rules must be applied *immediately* as soon as their preconditions are satisfied.
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The second argument of `postcondition`, `effect(unconditional)`, is not used and exists only for consistency with [actions](#actions).
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Axiom rule facts are only present with SAS input programs and not PDDL.
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Axiom rules contain essential information in order to find plans correctly.
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That is, if axiom rules are present in `plasp`’s output, they have to be accounted for appropriately.
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