5 research outputs found
An Anytime Algorithm for Reachability on Uncountable MDP
We provide an algorithm for reachability on Markov decision processes with
uncountable state and action spaces, which, under mild assumptions,
approximates the optimal value to any desired precision. It is the first such
anytime algorithm, meaning that at any point in time it can return the current
approximation with its precision. Moreover, it simultaneously is the first
algorithm able to utilize \emph{learning} approaches without sacrificing
guarantees and it further allows for combination with existing heuristics
GENERATING PLANS IN CONCURRENT, PROBABILISTIC, OVER-SUBSCRIBED DOMAINS
Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve the same goal are used to increase the plan’s probability of success. Traditionally, only parallel steps that serve different goals are used to reduce plan execution time. Second, actions that are executing but are no longer useful can be terminated to save resources and time. Conventional planners assume that all actions that were started will be carried out to completion. To reduce the size of the search space, several domain independent heuristic functions and pruning techniques were developed. The key ideas are to exploit dominance relations for candidate action sets and to develop relaxed planning graphs to estimate the expected rewards of states. This thesis contributes (1) an AO* based planner to generate parallel plans, (2) domain independent heuristics to increase planner efficiency, and (3) the ability to execute redundant actions and to terminate useless actions to increase plan efficiency
Probabilistic Inference in Piecewise Graphical Models
In many applications of probabilistic inference the models
contain piecewise densities that are differentiable except at
partition boundaries. For instance, (1) some models may
intrinsically have finite support, being constrained to some
regions; (2) arbitrary density functions may be approximated by
mixtures of piecewise functions such as piecewise polynomials or
piecewise exponentials; (3) distributions derived from other
distributions (via random variable transformations) may be highly
piecewise; (4) in applications of Bayesian inference such as
Bayesian discrete classification and preference learning, the
likelihood functions may be piecewise; (5) context-specific
conditional probability density functions (tree-CPDs) are
intrinsically piecewise; (6) influence diagrams (generalizations
of Bayesian networks in which along with probabilistic inference,
decision making problems are modeled) are in many applications
piecewise; (7) in probabilistic programming, conditional
statements lead to piecewise models. As we will show, exact
inference on piecewise models is not often scalable (if
applicable) and the performance of the existing approximate
inference techniques on such models is usually quite poor.
This thesis fills this gap by presenting scalable and accurate
algorithms for inference in piecewise probabilistic graphical
models. Our first contribution is to present a variation of Gibbs
sampling algorithm that achieves an exponential sampling speedup
on a large class of models (including Bayesian models with
piecewise likelihood functions). As a second contribution, we
show that for a large range of models, the time-consuming Gibbs
sampling computations that are traditionally carried out per
sample, can be computed symbolically, once and prior to the
sampling process. Among many potential applications, the
resulting symbolic Gibbs sampler can be used for fully automated
reasoning in the presence of deterministic constraints among
random variables. As a third contribution, we are motivated by
the behavior of Hamiltonian dynamics in optics —in particular,
the reflection and refraction of light on the refractive
surfaces— to present a new Hamiltonian Monte Carlo method that
demonstrates a significantly improved performance on piecewise
models.
Hopefully, the present work represents a step towards scalable
and accurate inference in an important class of probabilistic
models that has largely been overlooked in the literature