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# Abstract Interpretation | ||
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Coming soon! | ||
PDDL.jl supports [abstract interpretation](https://en.wikipedia.org/wiki/Abstract_interpretation) of PDDL domains, the semantics of which can also be compiled using the [PDDL.jl compiler](compiler.md). This functionality is exposed by the [`abstracted`](@ref) and [`abstractstate`](@ref) functions: | ||
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```@docs | ||
abstracted | ||
abstractstate | ||
``` | ||
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The behavior of the abstract interpreter can be customized by specifying the Julia type used to represent abstract values for a particular fluent or PDDL type: | ||
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```@docs | ||
PDDL.AbstractInterpeter | ||
``` | ||
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## Abstract Values and Types | ||
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Abstract interpretation requires each concrete value to be mapped to an abstract value of a particular type, which represents an (over-approximation) of the set of the possible values reachable after a series of actions have been executed. By default, Boolean values (i.e. predicates) are mapped to the [`BooleanAbs`](@ref) abstraction, while scalar numbers (corresponding to PDDL types like `integer`, `number` and `numeric`) are mapped to the [`IntervalAbs`](@ref) abstraction. Other types of values may use the [`SetAbs`](@ref) abstraction. | ||
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```@docs | ||
PDDL.BooleanAbs | ||
PDDL.IntervalAbs | ||
PDDL.SetAbs | ||
``` |
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