Results 1  10
of
39
BnBAdopt: An Asynchronous BranchandBound . . .
, 2008
"... Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOP problems optimally with memorybounded and asynchronous algorithms. We introduce BranchandBound ADOPT (BnBADOPT), a memorybounded asyn ..."
Abstract

Cited by 59 (15 self)
 Add to MetaCart
(Show Context)
Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOP problems optimally with memorybounded and asynchronous algorithms. We introduce BranchandBound ADOPT (BnBADOPT), a memorybounded asynchronous DCOP algorithm that uses the message passing and communication framework of ADOPT, a well known memorybounded asynchronous DCOP algorithm, but changes the search strategy of ADOPT from bestfirst search to depthfirst branchandbound search. Our experimental results show that BnBADOPT is up to one order of magnitude faster than ADOPT on a variety of large DCOP problems and faster than NCBB, a memorybounded synchronous DCOP algorithm, on most of these DCOP problems.
Bounded approximate decentralised coordination using the maxsum algorithm
 IN DISTRIBUTED CONSTRAINT REASONING WORKSHOP
, 2009
"... In this paper we propose a novel algorithm that provides bounded approximate solutions for decentralised coordination problems. Our approach removes cycles in any general constraint network by eliminating dependencies between functions and variables which have the least impact on the solution qualit ..."
Abstract

Cited by 30 (9 self)
 Add to MetaCart
In this paper we propose a novel algorithm that provides bounded approximate solutions for decentralised coordination problems. Our approach removes cycles in any general constraint network by eliminating dependencies between functions and variables which have the least impact on the solution quality. It uses the maxsum algorithm to optimally solve the resulting tree structured constraint network, providing a bounded approximation specific to the particular problem instance. We formally prove that our algorithm provides a bounded approximation of the original problem and we present an empirical evaluation in a synthetic scenario. This shows that the approximate solutions that our algorithm provides are typically within 95 % of the optimum and the approximation ratio that our algorithm provides is typically 1.23.
A Survey on Sensor Networks from a MultiAgent perspective
"... Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of realworld applications. Mult ..."
Abstract

Cited by 26 (0 self)
 Add to MetaCart
(Show Context)
Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of realworld applications. MultiAgent systems (MAS) have been identified as one of the most suitable technologies to contribute to this domain due to their appropriateness for modeling autonomous selfaware sensors in a flexible way. Firstly, this survey summarizes the actual challenges and research areas concerning sensor networks while identifying the most relevant MAS contributions. Secondly, we propose a taxonomy for sensor networks that classifies them depending on their features (and the research problems they pose). Finally, we identify some open future research directions and opportunities for MAS research. 1.
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
"... The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots. This paper presents a decentralized data fusion and active sensing (D 2 FAS) algorithm for mobile sensor ..."
Abstract

Cited by 19 (13 self)
 Add to MetaCart
(Show Context)
The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots. This paper presents a decentralized data fusion and active sensing (D 2 FAS) algorithm for mobile sensors to actively explore the road network to gather and assimilate the most informative data for predicting the traffic phenomenon. We analyze the time and communication complexity of D 2 FAS and demonstrate that it can scale well with a large number of observations and sensors. We provide a theoretical guarantee on its predictive performance to be equivalent to that of a sophisticated centralized sparse approximation for the Gaussian process (GP) model: The computation of such a sparse approximate GP model can thus be parallelized and distributed among the mobile sensors (in a Googlelike MapReduce paradigm), thereby achieving efficient and scalable prediction. We also theoretically guarantee its active sensing performance that improves under various practical environmental conditions. Empirical evaluation on realworld urban road network data shows that our D2FAS algorithm is significantly more timeefficient and scalable than stateoftheart centralized algorithms while achieving comparable predictive performance. 1
A Distributed Anytime Algorithm for Dynamic Task Allocation in Multiagent Systems
 In Proc. of AAAI
, 2011
"... We introduce a novel distributed algorithm for multiagent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fastmaxsum), and give it significant new capabilities: namely, an online pruning procedure that simplifies ..."
Abstract

Cited by 11 (3 self)
 Add to MetaCart
(Show Context)
We introduce a novel distributed algorithm for multiagent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fastmaxsum), and give it significant new capabilities: namely, an online pruning procedure that simplifies the problem, and a branchandbound technique that reduces the search space. This allows us to scale to problems with hundreds of tasks and agents. We empirically evaluate our algorithm against established benchmarks and find that, even in such large environments, a solution is found up to 31% faster, and with up to 23 % more utility, than stateoftheart approximation algorithms. In addition, our algorithm sends up to 30 % fewer messages than current approaches when the set of agents or tasks changes.
Agentmediated Multistep Optimization for Resource Allocation in Distributed Sensor Networks
"... Distributed collaborative adaptive sensing (DCAS) of the atmosphere is a new paradigm for detecting and predicting hazardous weather using a large dense network of shortrange, lowpowered radars to sense the lowest few kilometers of the earths atmosphere. In DCAS, radars are controlled by a collect ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Distributed collaborative adaptive sensing (DCAS) of the atmosphere is a new paradigm for detecting and predicting hazardous weather using a large dense network of shortrange, lowpowered radars to sense the lowest few kilometers of the earths atmosphere. In DCAS, radars are controlled by a collection of Meteorological Command and Control (MC&C) agents that instruct where to scan based on emerging weather conditions. Within this context, this work concentrates on designing efficient approaches for allocating sensing resources to cope with restricted realtime requirements and limited computational resources. We have developed a new approach based on explicit goals that can span multiple system heartbeats. This allows us to reason ahead about sensor allocations based on expected requirements of goals as they project forward in time. Each goal explicitly specifies endusers ’ preferences as well as a prediction of how a phenomena will move. We use a genetic algorithm to generate scanning strategies of each single MC&C and a distributed negotiation model to coordinate multiple MC&Cs ’ scanning strategies over multiple heartbeats. Simulation results show that as compared to simpler variants of our approach, the proposed distributed model achieved the highest social welfare. Our approach also has exhibited similarly very good performance in an operational radar testbed that is deployed in Oklahoma to observe severe weather events.
Gaussian process decentralized data fusion and active sensing for spatiotemporal traffic modeling and prediction in mobilityondemand systems
 IEEE Transactions on Automation Science and Engineering
, 2015
"... Abstract—Mobilityondemand (MoD) systems have recently emerged as a promising paradigm of oneway vehicle sharing for sustainable personal urban mobility in densely populated cities. We assume the capability of a MoD system to be enhanced by deploying robotic shared vehicles that can autonomously c ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Abstract—Mobilityondemand (MoD) systems have recently emerged as a promising paradigm of oneway vehicle sharing for sustainable personal urban mobility in densely populated cities. We assume the capability of a MoD system to be enhanced by deploying robotic shared vehicles that can autonomously cruise the streets to be hailed by users. A key challenge of the MoD system is that of realtime, finegrained mobility demand and traffic flow sensing and prediction. This paper presents novel Gaussian process (GP) decentralized data fusion and active sensing algorithms for realtime, finegrained traffic modeling and prediction with a fleet of MoD vehicles. The predictive performance of our decentralized data fusion algorithms are theoretically guaranteed to be equivalent to that of sophisticated centralized sparse GP approximations. We derive consensus filtering variants requiring only local
V.: Effective variants of the maxsum algorithm for radar coordination and scheduling
 In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
, 2011
"... AbstractSolving a coordination problem in a decentralized environment requires a large amount of resources and thus exploiting the innate system structure and external information as much as possible is necessary for such a problem to be solved in a computationally effective manner. This work prop ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
AbstractSolving a coordination problem in a decentralized environment requires a large amount of resources and thus exploiting the innate system structure and external information as much as possible is necessary for such a problem to be solved in a computationally effective manner. This work proposes new techniques for saving communication and computational resources when solving distributed constraint optimization problems using the MaxSum algorithm in an environment where system hardware resources are clustered. These techniques facilitate effective problem solving through the use of a precomputed policy and two phase propagation on MaxSum algorithm, one inside the clustered resources and one among clustered resources. This approach shows equivalent quality to the standard MaxSum algorithm while reducing communication requirements on average by 50% and computation resources by 5 to 30% depending on the specific problem instance. These experiments were performed in a realistic setting involving the scheduling of a network of as many as 192 radars in 48 clusters.
A COMPARATIVE STUDY OF UNDERWATER ROBOT PATH PLANNING ALGORITHMS FOR ADAPTIVE SAMPLING IN A NETWORK OF SENSORS
, 2014
"... Monitoring lakes, rivers, and oceans is critical to improving our understanding of complex largescale ecosystems. We introduce a method of underwater monitoring using semimobile underwater sensor networks and mobile underwater robots in this thesis. The underwater robots can move freely in all dim ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Monitoring lakes, rivers, and oceans is critical to improving our understanding of complex largescale ecosystems. We introduce a method of underwater monitoring using semimobile underwater sensor networks and mobile underwater robots in this thesis. The underwater robots can move freely in all dimension while the sensor nodes are anchored to the bottom of the water column and can move only up and down along the depth of the water column. We develop three different algorithms to optimize the path of the