76,967 research outputs found
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes
Information-theoretic principles for learning and acting have been proposed
to solve particular classes of Markov Decision Problems. Mathematically, such
approaches are governed by a variational free energy principle and allow
solving MDP planning problems with information-processing constraints expressed
in terms of a Kullback-Leibler divergence with respect to a reference
distribution. Here we consider a generalization of such MDP planners by taking
model uncertainty into account. As model uncertainty can also be formalized as
an information-processing constraint, we can derive a unified solution from a
single generalized variational principle. We provide a generalized value
iteration scheme together with a convergence proof. As limit cases, this
generalized scheme includes standard value iteration with a known model,
Bayesian MDP planning, and robust planning. We demonstrate the benefits of this
approach in a grid world simulation.Comment: 16 pages, 3 figure
Logic Programming for Describing and Solving Planning Problems
A logic programming paradigm which expresses solutions to problems as stable
models has recently been promoted as a declarative approach to solving various
combinatorial and search problems, including planning problems. In this
paradigm, all program rules are considered as constraints and solutions are
stable models of the rule set. This is a rather radical departure from the
standard paradigm of logic programming. In this paper we revisit abductive
logic programming and argue that it allows a programming style which is as
declarative as programming based on stable models. However, within abductive
logic programming, one has two kinds of rules. On the one hand predicate
definitions (which may depend on the abducibles) which are nothing else than
standard logic programs (with their non-monotonic semantics when containing
with negation); on the other hand rules which constrain the models for the
abducibles. In this sense abductive logic programming is a smooth extension of
the standard paradigm of logic programming, not a radical departure.Comment: 8 pages, no figures, Eighth International Workshop on Nonmonotonic
Reasoning, special track on Representing Actions and Plannin
On the Complexity of Case-Based Planning
We analyze the computational complexity of problems related to case-based
planning: planning when a plan for a similar instance is known, and planning
from a library of plans. We prove that planning from a single case has the same
complexity than generative planning (i.e., planning "from scratch"); using an
extended definition of cases, complexity is reduced if the domain stored in the
case is similar to the one to search plans for. Planning from a library of
cases is shown to have the same complexity. In both cases, the complexity of
planning remains, in the worst case, PSPACE-complete
- …