147 research outputs found
Allowing non-submodular score functions in distributed task allocation
Submodularity is a powerful property that can be exploited for provable performance and convergence guarantees in distributed task allocation algorithms. However, some mission scenarios cannot easily be approximated as submodular a priori. This paper introduces an algorithmic extension for distributed multi-agent multi-task assignment algorithms which provides guaranteed convergence using non-submodular score functions. This algorithm utilizes non-submodular ranking of tasks within each agent's internal decision making process, while externally enforcing that shared bids appear as if they were created using submodular score functions. Provided proofs demonstrate that all convergence and performance guarantees hold with respect to this apparent submodular score function. The algorithm allows significant improvements over heuristic approaches that approximate truly non-submodular score functions.United States. Air Force Office of Scientific Research (Grant FA9550-11-1-0134
The CSR-Firm Performance Missing Link: Complementarity Between Environmental, Social and Business Behavior Criteria?
This article analyses the complementarity between various dimensions of corporate social responsibility (CSR) and financial performance. We hypothesise that the absence of consensus in the empirical literature on the CSR-financial performance relationship may be explained by the existence of synergies (complementarity) and trade-offs (substitutability) between the different CSR components. We investigate such relationships using a sample of 595 firms from 15 European countries over the 2002-2007 period. The results suggest some kind of trade-offs between CSR components. Some CSR combinations appear as relative complements, human resources and business behaviour towards customers and suppliers, suggesting mutual benefits and less conflicts between those stakeholders. Conversely, environment and business behaviour towards customers and suppliers appear as relative substitutes, suggesting more conflict or over investment between such types of stakeholders
Consistent Sensor, Relay, and Link Selection in Wireless Sensor Networks
In wireless sensor networks, where energy is scarce, it is inefficient to
have all nodes active because they consume a non-negligible amount of battery.
In this paper we consider the problem of jointly selecting sensors, relays and
links in a wireless sensor network where the active sensors need to communicate
their measurements to one or multiple access points. Information messages are
routed stochastically in order to capture the inherent reliability of the
broadcast links via multiple hops, where the nodes may be acting as sensors or
as relays. We aim at finding optimal sparse solutions where both, the
consistency between the selected subset of sensors, relays and links, and the
graph connectivity in the selected subnetwork are guaranteed. Furthermore,
active nodes should ensure a network performance in a parameter estimation
scenario. Two problems are studied: sensor and link selection; and sensor,
relay and link selection. To solve such problems, we present tractable
optimization formulations and propose two algorithms that satisfy the previous
network requirements. We also explore an extension scenario: only link
selection. Simulation results show the performance of the algorithms and
illustrate how they provide a sparse solution, which not only saves energy but
also guarantees the network requirements.Comment: 27 pages, 17 figure
Communication Efficiency in Information Gathering through Dynamic Information Flow
This thesis addresses the problem of how to improve the performance of multi-robot information gathering tasks by actively controlling the rate of communication between robots. Examples of such tasks include cooperative tracking and cooperative environmental monitoring. Communication is essential in such systems for both decentralised data fusion and decision making, but wireless networks impose capacity constraints that are frequently overlooked. While existing research has focussed on improving available communication throughput, the aim in this thesis is to develop algorithms that make more efficient use of the available communication capacity. Since information may be shared at various levels of abstraction, another challenge is the decision of where information should be processed based on limits of the computational resources available. Therefore, the flow of information needs to be controlled based on the trade-off between communication limits, computation limits and information value. In this thesis, we approach the trade-off by introducing the dynamic information flow (DIF) problem. We suggest variants of DIF that either consider data fusion communication independently or both data fusion and decision making communication simultaneously. For the data fusion case, we propose efficient decentralised solutions that dynamically adjust the flow of information. For the decision making case, we present an algorithm for communication efficiency based on local LQ approximations of information gathering problems. The algorithm is then integrated with our solution for the data fusion case to produce a complete communication efficiency solution for information gathering. We analyse our suggested algorithms and present important performance guarantees. The algorithms are validated in a custom-designed decentralised simulation framework and through field-robotic experimental demonstrations
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