Understanding complex, real-world systems through asynchronous, distributed decision-making algorithms

Ghosh, Sumit

doi:10.1016/s0164-1212(01)00035-8

Cited by 12 publications

(6 citation statements)

References 48 publications

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“…In asynchronous decision-making, agents/robots take decision in an event-driven scheme, as opposed to a synchronous approach where all robots need to take decisions at fixed intervals. The asynchronous decision-making is critical in most real-world settings, due to the presence of stochastic action effects and imperfect and unreliable communication [26]. In addition, it has been shown that having asynchronous parallel sampling in Bayesian optimization (the motivating algorithm behind our proposed search method) can improve the optimization progress in comparison to synchronous implementations [27].…”

Section: Objective Of Thismentioning

confidence: 99%

An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search

Ghassemi

Chowdhury

2020

Journal of Computing and Information Science in Engineering

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Swarm robotic search aims at searching targets using a large number of collaborating simple mobile robots, with applications to search and rescue and hazard localization. In this regard, decentralized swarm systems are touted for their coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely, nature-inspired swarm heuristics and multi-robotic search methods. However, the ability to simultaneously achieve efficient scalability and provide fundamental insights into the exhibited behavior (as opposed to exhibiting a black-box behavior) remains an open problem. To address this problem, this paper extends the underlying search approach in batch-Bayesian optimization to perform search with embodied swarm agents operating in a (simulated) physical 2D arena. Key contributions lie in (1) designing an acquisition function that not only balances exploration and exploitation across the swarm but also allows modeling knowledge extraction over trajectories and (2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target-search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Notably, superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines as well as a swarm optimization-based state-of-the-art method. Favorable scalability characteristics are also demonstrated.

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Section: Objective Of Thismentioning

confidence: 99%

An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search

Ghassemi

Chowdhury

2020

Journal of Computing and Information Science in Engineering

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“…Thus, during the execution of an activity, each subsystem assumes its own unique state (reflected by the data value of the variable, etc.) and none has knowledge of the exact state of the other subsystems (Ghosh, 2001). When activities that depend on several resources are required, then the modules that control these resources (as described in the previous section) have to communicate, in order to update their data and to synchronies their actions (inter-module communication).…”

Section: The Communication Proceduresmentioning

confidence: 99%

Supervision and control of heterarchical discrete event systems

Silveira

Combacau

2006

Sba Controle & Automação

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This paper presents a method for distributing centralized control models of discrete events systems (DES). The contribution of our approach is to offer a systematic way to decompose centralised models in order to obtain a modular representation of the production process. We observe as an advantage of this method that the main model properties are preserved and that the knowledge acquired during the modelling process is still valuable for the new structure. We emphasise that redundant information can be introduced to the system to increase local autonomy and to help the local fault detection and identification functions. Another important aspect is that the distributed models (sub-models) can be modified in order to increase their flexibility and to reach a more realistic behaviour representation. A set of terminologies and definitions related to the Control and Supervision domain are introduced in this paper to assist the understanding of our work.
Este artigo apresenta uma metodologia de distribuição de modelos de controle e de supervisão de sistemas a eventos discretos (SED). A contribuição da nossa proposta é de propiciar uma forma sistemática de decomposição de modelos centralizados com a finalidade de obter uma representação modular da produção, preservando suas principais propriedades assim como o conhecimento adquirido durante a fase de concepção do modelo. Uma das características da nossa proposta é a possibilidade de introduzir informações redundantes nos sub-modelos afim de aumentar a autonomia local e auxiliar as funções de tratamento de falhas. Ressaltamos também que a distribuição do modelo centralizado permite adaptações locais que aumentam sua flexibilidade e precisam seu comportament, aproximando-o do sistema físico considerado. Neste artigo, nos apresentamos também alguns termos e definições relativos à área de Controle e Supervisão de SED que serviram de base aos nossos trabalhos

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“…We formalize the cooperation policy setting as an Asynchronous Distributed Decision Making (ADDM) process [Ghosh, 2001, 2004] to enable peers to make decisions on their own cooperation policies rationally while in line with overall utility of the system. As depicted in Figure 1, to successfully formulate the cooperation policy setting as an ADDM problem, the key challenges are to define the globally optimal performance as the overall utility, to synthesize local objective of individual peers in line with it, and to design their interactions in form of a mechanism that coordinates asynchronous distributed decisions of individual peers and imposes distributed constraints to prevent them from making selfish decisions against the global objective.…”

Section: Introductionmentioning

confidence: 99%

Emergence of cooperation in peer‐to‐peer systems: A complex adaptive system approach

Vakili

Tabatabaee

Khorsandi

2012

Systems Engineering

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Overall performance of a peer-to-peer system can be highly variable and unpredictable as there is no central authority to set and coordinate the amount of resource contributions made by constituent peers. In this paper, we address the problem of optimal cooperation policy setting for individual peers by taking into account their rationality, and with respect to a set of overall constraints imposed by resource-driven requirements of the system. We formalize distributed cooperation policy setting as an asynchronous distributed decision making (ADDM) process and represent an integrated design for this process in two top-down and bottom-up phases. In top-down design, we specify the overall objective and constraints and in line with them, we synthesize the local objectives of individual peers. We show that the rational peers should continuously adapt their cooperation policies asynchronously and autonomously according to the policies of the other participants in order to maximize their local objectives. To this end, we devise a self-organizing interaction mechanism in the bottom-up phase and demonstrate that it coordinates asynchronous distributed decisions of individual peers with respect to the overall objective and constraints through local interactions.

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Understanding complex, real-world systems through asynchronous, distributed decision-making algorithms

Cited by 12 publications

References 48 publications

An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search

An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search

Supervision and control of heterarchical discrete event systems

Emergence of cooperation in peer‐to‐peer systems: A complex adaptive system approach

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