2,154 research outputs found
Learning Features and Abstract Actions for Computing Generalized Plans
Generalized planning is concerned with the computation of plans that solve
not one but multiple instances of a planning domain. Recently, it has been
shown that generalized plans can be expressed as mappings of feature values
into actions, and that they can often be computed with fully observable
non-deterministic (FOND) planners. The actions in such plans, however, are not
the actions in the instances themselves, which are not necessarily common to
other instances, but abstract actions that are defined on a set of common
features. The formulation assumes that the features and the abstract actions
are given. In this work, we address this limitation by showing how to learn
them automatically. The resulting account of generalized planning combines
learning and planning in a novel way: a learner, based on a Max SAT
formulation, yields the features and abstract actions from sampled state
transitions, and a FOND planner uses this information, suitably transformed, to
produce the general plans. Correctness guarantees are given and experimental
results on several domains are reported.Comment: Preprint of paper accepted at AAAI'19 conferenc
Iterative Depth-First Search for Fully Observable Non-Deterministic Planning
Fully Observable Non-Deterministic (FOND) planning models uncertainty through
actions with non-deterministic effects. Existing FOND planning algorithms are
effective and employ a wide range of techniques. However, most of the existing
algorithms are not robust for dealing with both non-determinism and task size.
In this paper, we develop a novel iterative depth-first search algorithm that
solves FOND planning tasks and produces strong cyclic policies. Our algorithm
is explicitly designed for FOND planning, addressing more directly the
non-deterministic aspect of FOND planning, and it also exploits the benefits of
heuristic functions to make the algorithm more effective during the iterative
searching process. We compare our proposed algorithm to well-known FOND
planners, and show that it has robust performance over several distinct types
of FOND domains considering different metrics
Contingent planning under uncertainty via stochastic satisfiability
We describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability (SSAT) and solving these problems instead. We make fundamental contributions in two areas: the solution of SSAT problems and the solution of stochastic planning problems. This is the first work extending the planning-as-satisfiability paradigm to stochastic domains. Our planner, ZANDER, can solve arbitrary, goal-oriented, finite-horizon partially observable Markov decision processes (POMDPs). An empirical study comparing ZANDER to seven other leading planners shows that its performance is competitive on a range of problems. © 2003 Elsevier Science B.V. All rights reserved
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