3 research outputs found
Generalized Planning with Positive and Negative Examples
Generalized planning aims at computing an algorithm-like structure
(generalized plan) that solves a set of multiple planning instances. In this
paper we define negative examples for generalized planning as planning
instances that must not be solved by a generalized plan. With this regard the
paper extends the notion of validation of a generalized plan as the problem of
verifying that a given generalized plan solves the set of input positives
instances while it fails to solve a given input set of negative examples. This
notion of plan validation allows us to define quantitative metrics to asses the
generalization capacity of generalized plans. The paper also shows how to
incorporate this new notion of plan validation into a compilation for plan
synthesis that takes both positive and negative instances as input. Experiments
show that incorporating negative examples can accelerate plan synthesis in
several domains and leverage quantitative metrics to evaluate the
generalization capacity of the synthesized plans.Comment: Accepted at AAAI-20 (oral presentation
Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects
Planning as heuristic search is one of the most successful approaches to
classical planning but unfortunately, it does not extend trivially to
Generalized Planning (GP). GP aims to compute algorithmic solutions that are
valid for a set of classical planning instances from a given domain, even if
these instances differ in the number of objects, the number of state variables,
their domain size, or their initial and goal configuration. The generalization
requirements of GP make it impractical to perform the state-space search that
is usually implemented by heuristic planners. This paper adapts the planning as
heuristic search paradigm to the generalization requirements of GP, and
presents the first native heuristic search approach to GP. First, the paper
introduces a new pointer-based solution space for GP that is independent of the
number of classical planning instances in a GP problem and the size of those
instances (i.e. the number of objects, state variables and their domain sizes).
Second, the paper defines a set of evaluation and heuristic functions for
guiding a combinatorial search in our new GP solution space. The computation of
these evaluation and heuristic functions does not require grounding states or
actions in advance. Therefore our GP as heuristic search approach can handle
large sets of state variables with large numerical domains, e.g.~integers.
Lastly, the paper defines an upgraded version of our novel algorithm for GP
called Best-First Generalized Planning (BFGP), that implements a best-first
search in our pointer-based solution space, and that is guided by our
evaluation/heuristic functions for GP.Comment: Under review in the Artificial Intelligence Journal (AIJ