62,966 research outputs found
Stochastic Shortest Path with Energy Constraints in POMDPs
We consider partially observable Markov decision processes (POMDPs) with a
set of target states and positive integer costs associated with every
transition. The traditional optimization objective (stochastic shortest path)
asks to minimize the expected total cost until the target set is reached. We
extend the traditional framework of POMDPs to model energy consumption, which
represents a hard constraint. The energy levels may increase and decrease with
transitions, and the hard constraint requires that the energy level must remain
positive in all steps till the target is reached. First, we present a novel
algorithm for solving POMDPs with energy levels, developing on existing POMDP
solvers and using RTDP as its main method. Our second contribution is related
to policy representation. For larger POMDP instances the policies computed by
existing solvers are too large to be understandable. We present an automated
procedure based on machine learning techniques that automatically extracts
important decisions of the policy allowing us to compute succinct human
readable policies. Finally, we show experimentally that our algorithm performs
well and computes succinct policies on a number of POMDP instances from the
literature that were naturally enhanced with energy levels.Comment: Technical report accompanying a paper published in proceedings of
AAMAS 201
Planning for Decentralized Control of Multiple Robots Under Uncertainty
We describe a probabilistic framework for synthesizing control policies for
general multi-robot systems, given environment and sensor models and a cost
function. Decentralized, partially observable Markov decision processes
(Dec-POMDPs) are a general model of decision processes where a team of agents
must cooperate to optimize some objective (specified by a shared reward or cost
function) in the presence of uncertainty, but where communication limitations
mean that the agents cannot share their state, so execution must proceed in a
decentralized fashion. While Dec-POMDPs are typically intractable to solve for
real-world problems, recent research on the use of macro-actions in Dec-POMDPs
has significantly increased the size of problem that can be practically solved
as a Dec-POMDP. We describe this general model, and show how, in contrast to
most existing methods that are specialized to a particular problem class, it
can synthesize control policies that use whatever opportunities for
coordination are present in the problem, while balancing off uncertainty in
outcomes, sensor information, and information about other agents. We use three
variations on a warehouse task to show that a single planner of this type can
generate cooperative behavior using task allocation, direct communication, and
signaling, as appropriate
Adapting Search Theory to Networks
The CSE is interested in the general problem of locating objects in networks. Because of their exposure to search theory, the problem they brought to the workshop was phrased in terms of adapting search theory to networks. Thus, the first step was the introduction of an already existing healthy literature on searching graphs.
T. D. Parsons, who was then at Pennsylvania State University, was approached in 1977 by some local spelunkers who asked his aid in optimizing a search for someone lost in a cave in Pennsylvania. Parsons quickly formulated the problem as a search problem in a graph. Subsequent papers led to two divergent problems. One problem dealt with searching under assumptions of fairly extensive information, while the other problem dealt with searching under assumptions of essentially zero information. These two topics are developed in the next two sections
Type-Based Detection of XML Query-Update Independence
This paper presents a novel static analysis technique to detect XML
query-update independence, in the presence of a schema. Rather than types, our
system infers chains of types. Each chain represents a path that can be
traversed on a valid document during query/update evaluation. The resulting
independence analysis is precise, although it raises a challenging issue:
recursive schemas may lead to infer infinitely many chains. A sound and
complete approximation technique ensuring a finite analysis in any case is
presented, together with an efficient implementation performing the chain-based
analysis in polynomial space and time.Comment: VLDB201
- …