234 research outputs found
Constraint-Based Dynamic Programming for Decentralized POMDPs with Structured Interactions
Decentralized partially observable MDPs (DEC-POMDPs) provide a rich framework for modeling decision making by a team of agents. Despite rapid progress in this area, the limited scalability of solution techniques has restricted the applicability of the model. To overcome this computational barrier, research has focused on restricted classes of DEC-POMDPs, which are easier to solve yet rich enough to capture many practical problems. We present CBDP, an efficient and scalable point-based dynamic programming algorithm for one such model called ND-POMDP (Network Distributed POMDP). Specifically, CBDP provides magnitudes of speedup in the policy computation and generates better quality solution for all test instances. It has linear complexity in the number of agents and horizon length. Furthermore, the complexity per horizon for the examined class of problems is exponential only in a small parameter that depends upon the interaction among the agents, achieving significant scalability for large, loosely coupled multi-agent systems. The efficiency of CBDP lies in exploiting the structure of interactions using constraint networks. These results extend significantly the effectiveness of decision-theoretic planning in multi-agent settings
Influence-Optimistic Local Values for Multiagent Planning --- Extended Version
Recent years have seen the development of methods for multiagent planning
under uncertainty that scale to tens or even hundreds of agents. However, most
of these methods either make restrictive assumptions on the problem domain, or
provide approximate solutions without any guarantees on quality. Methods in the
former category typically build on heuristic search using upper bounds on the
value function. Unfortunately, no techniques exist to compute such upper bounds
for problems with non-factored value functions. To allow for meaningful
benchmarking through measurable quality guarantees on a very general class of
problems, this paper introduces a family of influence-optimistic upper bounds
for factored decentralized partially observable Markov decision processes
(Dec-POMDPs) that do not have factored value functions. Intuitively, we derive
bounds on very large multiagent planning problems by subdividing them in
sub-problems, and at each of these sub-problems making optimistic assumptions
with respect to the influence that will be exerted by the rest of the system.
We numerically compare the different upper bounds and demonstrate how we can
achieve a non-trivial guarantee that a heuristic solution for problems with
hundreds of agents is close to optimal. Furthermore, we provide evidence that
the upper bounds may improve the effectiveness of heuristic influence search,
and discuss further potential applications to multiagent planning.Comment: Long version of IJCAI 2015 paper (and extended abstract at AAMAS
2015
Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)
Many exact and approximate solution methods for Markov Decision Processes
(MDPs) attempt to exploit structure in the problem and are based on
factorization of the value function. Especially multiagent settings, however,
are known to suffer from an exponential increase in value component sizes as
interactions become denser, meaning that approximation architectures are
restricted in the problem sizes and types they can handle. We present an
approach to mitigate this limitation for certain types of multiagent systems,
exploiting a property that can be thought of as "anonymous influence" in the
factored MDP. Anonymous influence summarizes joint variable effects efficiently
whenever the explicit representation of variable identity in the problem can be
avoided. We show how representational benefits from anonymity translate into
computational efficiencies, both for general variable elimination in a factor
graph but in particular also for the approximate linear programming solution to
factored MDPs. The latter allows to scale linear programming to factored MDPs
that were previously unsolvable. Our results are shown for the control of a
stochastic disease process over a densely connected graph with 50 nodes and 25
agents.Comment: Extended version of AAAI 2016 pape
Formal Modelling for Multi-Robot Systems Under Uncertainty
Purpose of Review: To effectively synthesise and analyse multi-robot
behaviour, we require formal task-level models which accurately capture
multi-robot execution. In this paper, we review modelling formalisms for
multi-robot systems under uncertainty, and discuss how they can be used for
planning, reinforcement learning, model checking, and simulation.
Recent Findings: Recent work has investigated models which more accurately
capture multi-robot execution by considering different forms of uncertainty,
such as temporal uncertainty and partial observability, and modelling the
effects of robot interactions on action execution. Other strands of work have
presented approaches for reducing the size of multi-robot models to admit more
efficient solution methods. This can be achieved by decoupling the robots under
independence assumptions, or reasoning over higher level macro actions.
Summary: Existing multi-robot models demonstrate a trade off between
accurately capturing robot dependencies and uncertainty, and being small enough
to tractably solve real world problems. Therefore, future research should
exploit realistic assumptions over multi-robot behaviour to develop smaller
models which retain accurate representations of uncertainty and robot
interactions; and exploit the structure of multi-robot problems, such as
factored state spaces, to develop scalable solution methods.Comment: 23 pages, 0 figures, 2 tables. Current Robotics Reports (2023). This
version of the article has been accepted for publication, after peer review
(when applicable) but is not the Version of Record and does not reflect
post-acceptance improvements, or any corrections. The Version of Record is
available online at: https://dx.doi.org/10.1007/s43154-023-00104-
Influence-Based Abstraction for Multiagent Systems
This paper presents a theoretical advance by which factored POSGs can be decomposed into local models. We formalize the interface between such local models as the influence agents can exert on one another; and we prove that this interface is sufficient for decoupling them. The resulting influence-based abstraction substantially generalizes previous work on exploiting weakly-coupled agent interaction structures. Therein lie several important contributions. First, our general formulation sheds new light on the theoretical relationships among previous approaches, and promotes future empirical comparisons that could come by extending them beyond the more specific problem contexts for which they were developed. More importantly, the influence-based approaches that we generalize have shown promising improvements in the scalability of planning for more restrictive models. Thus, our theoretical result here serves as the foundation for practical algorithms that we anticipate will bring similar improvements to more general planning contexts, and also into other domains such as approximate planning, decision-making in adversarial domains, and online learning.United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (Project FA9550-09-1-0538
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems
A Survey on Sensor Networks from a Multiagent Perspective
Sensor networks (SNs) have arisen as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical systems ease the development and proliferation of this kind of networks in a wide range of actual-world applications. Multiagent systems (MAS) have been identified as one of the most suitable technologies to contribute to the deployment of SNs that exhibit flexibility, robustness and autonomy. The purpose of this survey is 2-fold. On the one hand, we review the most relevant contributions of agent technologies to this emerging application domain. On the other hand, we identify the challenges that researchers must address to establish MAS as the key enabling technology for SNs.This work has been funded by projects IEA(TIN2006-15662-C02-01), Agreement Technologies (CONSOLIDER CSD2007-0022, INGENIO 2010), EVE (TIN2009-14702-C02-01,TIN2009-14702-C02-02) and Generalitat de Catalunya under the gran
t2009-SGR-1434. Meritxell Vinyals is supported by the Spanish Ministry of Education (FPU grant AP2006-04636)Peer Reviewe
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