197 research outputs found
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
APPSSAT: Approximate probabilistic planning using stochastic satisfiability
AbstractWe describe appssat, an anytime probabilistic contingent planner based on zander, a probabilistic contingent planner that operates by converting the planning problem to a stochastic satisfiability (Ssat) problem and solving that problem instead [S.M. Majercik, M.L. Littman, Contingent planning under uncertainty via stochastic satisfiability, Artificial Intelligence 147 (2003) 119–162]. The values of some of the variables in an Ssat instance are probabilistically determined; appssat considers the most likely instantiations of these variables (the most probable situations facing the agent) and attempts to construct an approximation of the optimal plan that succeeds under those circumstances, improving that plan as time permits. Given more time, less likely instantiations/situations are considered and the plan is revised as necessary. In some cases, a plan constructed to address a relatively low percentage of possible situations will succeed for situations not explicitly considered as well, and may return an optimal or near-optimal plan. We describe experimental results showing that appssat can find suboptimal plans in cases in which zander is unable to find the optimal (or any) plan. Although the test problems are small, the anytime quality of appssat means that it has the potential to efficiently derive suboptimal plans in larger, time-critical domains in which zander might not have sufficient time to calculate any plan. We also suggest further work needed to bring appssat closer to attacking real-world problems
Dependency Stochastic Boolean Satisfiability: A Logical Formalism for NEXPTIME Decision Problems with Uncertainty
Stochastic Boolean Satisfiability (SSAT) is a logical formalism to model
decision problems with uncertainty, such as Partially Observable Markov
Decision Process (POMDP) for verification of probabilistic systems. SSAT,
however, is limited by its descriptive power within the PSPACE complexity
class. More complex problems, such as the NEXPTIME-complete Decentralized POMDP
(Dec-POMDP), cannot be succinctly encoded with SSAT. To provide a logical
formalism of such problems, we extend the Dependency Quantified Boolean Formula
(DQBF), a representative problem in the NEXPTIME-complete class, to its
stochastic variant, named Dependency SSAT (DSSAT), and show that DSSAT is also
NEXPTIME-complete. We demonstrate the potential applications of DSSAT to
circuit synthesis of probabilistic and approximate design. Furthermore, to
study the descriptive power of DSSAT, we establish a polynomial-time reduction
from Dec-POMDP to DSSAT. With the theoretical foundations paved in this work,
we hope to encourage the development of DSSAT solvers for potential broad
applications.Comment: 10 pages, 5 figures. A condensed version of this work is published in
the AAAI Conference on Artificial Intelligence (AAAI) 202
Engineering a Conformant Probabilistic Planner
We present a partial-order, conformant, probabilistic planner, Probapop which
competed in the blind track of the Probabilistic Planning Competition in IPC-4.
We explain how we adapt distance based heuristics for use with probabilistic
domains. Probapop also incorporates heuristics based on probability of success.
We explain the successes and difficulties encountered during the design and
implementation of Probapop
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