Distributed and Centralized Task Allocation: When and Where to Use Them

Self-organisation is frequently advocated as the solution for managing large, dynamic systems. Distributed algorithms are implicitly designed for infinitely large problems, while small systems are regarded as being controllable using traditional, centralised approaches. Many real-world systems, however, do not fit conveniently into these "small" or "large" categories, resulting in a range of cases where the optimal solution is ambiguous. This difficulty is exacerbated by enthusiasts of either approach constructing problems that suit their preferred control architecture. We address this ambiguity by building an abstract model of task allocation in a community of specialised agents. We are inspired by the problem of work distribution in distributed satellite systems, but the model is also relevant to the resource allocation problems in distributed robotics, autonomic computing and wireless sensor networks. We compare the behaviour of a self-organising, market-based task allocation strategy to a classical approach that uses a central controller with global knowledge. The objective is not to prove one mechanism inherently superior to the other; instead we are interested in the regions of problem space where each of them dominates. Simulation is used to explore the trade-off between energy consumption and robustness in a system of intermediate size, with fixed communication costs and varying rates of component failure. We identify boundaries between regions in the parameter space where one or the other architecture will be favoured. This allows us to derive guidelines for system designers, thus contributing to the development of a disciplined approach to controlling distributed systems using self-organising mechanisms

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Strategic Structural Reorganization in Multi-agent Systems Inspired by Social Organization Theory

Autonomic systems, capable of adaptive behavior, are envisioned as a solution for maintaining large, complex, real-time computing systems that are situated in dynamic and open environments. These systems are subject to uncertainties in their perceptual, computational, and communication loads. As a result, the individual system components find the need to cooperate with each other to acquire more information and accomplish complex tasks. Critical to the effective performance of these systems, is the effectiveness of communication and coordination methods. In many practical applications of distributed and multi-agent systems, the problem of communication and coordination becomes even more complicated because of the geographic disparity of tasks and/or agents that are performing the tasks. Experience with even small systems has shown that lack of an effective communication and coordination strategy leads the system to no-answer, or sub-optimal answer situations. To address this problem, many large-scale systems employ an additional layer of structuring, known as organizational structure, which governs assignment of roles to individual agents, existence of relations between the agents , and any authority structures in between. Applying different organizational structures to the same problem will lead to different performance characteristics. As the system and environment conditions change, it becomes important to reorganize to a more effective organization. Due to the costs associated with reorganization, finding a balance in how often or when a reorganization is performed becomes necessary. In multi-agent systems community, not a lot of attention has been paid to reorganizing a system to a different organizational structure. Most systems reorganize within the same structure, for example reorganizing in a hierarchy by changing the width or depth of the hierarchy. To approach this problem, we looked into adaptation of concepts and theories from social organization theory. In particular, we got insights from Schwaninger's model of Intelligent Human Organizations. We introduced a strategic reorganization model which enables the system to reorganize to a different type of organizational structure at run time. The proposed model employs different levels of organizational control for making organizational change decisions. We study the performance trade-offs and the efficacy of the proposed approach by running experiments using two instances of cooperative distributed problem solving applications. The results indicate that the proposed reorganization model results in performance improvements when task complexity increases

Multiple UAV systems: a survey

Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of tasks. There are few research works that cover the applications of these systems and they are all highly specialized. The goal of this survey is to fill this gap by providing a generic review on different applications of multiple UAV systems that have been developed in recent years. We also present a nomenclature and architecture taxonomy for these systems. In the end, a discussion on current trends and challenges is provided.This work was funded by the Ministry of Economy, Industryand Competitiveness of Spain under Grant Nos. TRA2016-77012-R and BES-2017-079798Peer ReviewedPostprint (published version

Action Learning with Second Life - A Pilot Study

Virtual worlds, computer-based simulated environments in which users interact via avatars, provide an opportunity for the highly realistic enactment of real life activities online. Unlike computer games, which have a pre-defined purpose, pay-off structure, and action patterns, virtual worlds can leave many of these elements for users to determine. One such world, Second Life (SL), is frequently used as platform for revenue generation, information and knowledge sharing, and learning. As a learning environment, Second Life appears to be particularly amenable to action learning, where learners are not simply observers, but plan, implement, observe, and draw conclusions from their actions. We tested the usefulness of SL as an action learning environment in a senior course for management information systems students. The findings demonstrate learning in the SL environment contributes to the students’ perceived value of learning through the Action Learning steps