142,266 research outputs found
Resource-Aware Junction Trees for Efficient Multi-Agent Coordination
In this paper we address efficient decentralised coordination of cooperative multi-agent systems by taking into account the actual computation and communication capabilities of the agents. We consider coordination problems that can be framed as Distributed Constraint Optimisation Problems, and as such, are suitable to be deployed on large scale multi-agent systems such as sensor networks or multiple unmanned aerial vehicles. Specifically, we focus on techniques that exploit structural independence among agents’ actions to provide optimal solutions to the coordination problem, and, in particular, we use the Generalized Distributive Law (GDL) algorithm. In this settings, we propose a novel resource aware heuristic to build junction trees and to schedule GDL computations across the agents. Our goal is to minimise the total running time of the coordination process, rather than the theoretical complexity of the computation, by explicitly considering the computation and communication capabilities of agents. We evaluate our proposed approach against DPOP, RDPI and a centralized solver on a number of benchmark coordination problems, and show that our approach is able to provide optimal solutions for DCOPs faster than previous approaches. Specifically, in the settings considered, when resources are scarce our approach is up to three times faster than DPOP (which proved to be the best among the competitors in our settings)
Maximal reliability of controlled Markov systems
This paper concentrates on the reliability of a discrete-time controlled
Markov system with finite states and actions, and aims to give an efficient
algorithm for obtaining an optimal (control) policy that makes the system have
the maximal reliability for every initial state. After establishing the
existence of an optimal policy, for the computation of optimal policies, we
introduce the concept of an absorbing set of a stationary policy, and find some
characterization and a computational method of the absorbing sets. Using the
largest absorbing set, we build a novel optimality equation (OE), and prove the
uniqueness of a solution of the OE. Furthermore, we provide a policy iteration
algorithm of optimal policies, and prove that an optimal policy and the maximal
reliability can be obtained in a finite number of iterations. Finally, an
example in reliability and maintenance problems is given to illustrate our
results
Learning to Plan Near-Optimal Collision-Free Paths
A new approach to find a near-optimal collision-free
path is presented. The path planner is an implementation
of the adaptive error back-propagation algorithm
which learns to plan “good”, if not optimal,
collision-free paths from human-supervised training
samples.
Path planning is formulated as a classification
problem in which class labels are uniquely mapped
onto the set of maneuverable actions of a robot or
vehicle. A multi-scale representational scheme maps
physical problem domains onto an arbitrarily chosen
fixed size input layer of an error back-propagation
network. The mapping does not only reduce the size
of the computation domain, but also ensures applicability
of a trained network over a wide range of
problem sizes. Parallel implementation of the neural
network path planner on hypercubes or Transputers
based on Parasoft EXPRESS is simple and efficient,
Simulation results of binary terrain navigation indicate
that the planner performs effectively in unknown
environment in the test cases
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling
State-of-the-art approaches to partially observable planning like POMCP are
based on stochastic tree search. While these approaches are computationally
efficient, they may still construct search trees of considerable size, which
could limit the performance due to restricted memory resources. In this paper,
we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory
bounded approach to open-loop planning in large POMDPs, which optimizes a fixed
size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four
large benchmark problems and compare its performance with different tree-based
approaches. We show that POSTS achieves competitive performance compared to
tree-based open-loop planning and offers a performance-memory tradeoff, making
it suitable for partially observable planning with highly restricted
computational and memory resources.Comment: Presented at AAAI 201
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