Abstraction Learning in Prodigy
Abstraction Learning in Prodigy
ALPINE produces abstraction hierarchies to help control the problem
solver's search. To do this, the module employs an algorithm that
takes a set of operators and a problem domain as input then partitions
the literals in the operators' preconditions, addlists, and
deletelists to form a hierarchy of abstraction spaces. The literals in
a given level of the hierarchy are constained to be achievable without
the addition or deletion of a literal at a higher level in the
hierarchy. The higher levels represent more abstract descriptions of
the domain. The lower levels represent the domain in more detail. The
problem solver can plan at a high level of abstraction. The most
important preconditions and effects are considered in planning at this
level. The plan is then refined as needed by planning at lower levels.
Return to the top of this architecture.
Go to a discussion of this capability
for multiple architectures.
Current Location: Prodigy-Capabilities-Multi-Method Learning-Abstraction Learning