Application of Techniques for MAP Estimation to Distributed Constraint Optimization Problem

The problem of efficiently finding near-optimal decisions in multi-agent systems has become increasingly important because of the growing number of multi-agent applications with large numbers of agents operating in real-world environments. In these systems, agents are often subject to tight resource constraints and agents have only local views. When agents have non-global constraints, each of which is independent, the problem can be formalized as a distributed constraint optimization problem (DCOP). The DCOP is closely associated with the problem of inference on graphical models. Many approaches from inference literature have been adopted to solve DCOPs. We focus on the Max-Sum algorithm and the Action-GDL algorithm that are DCOP variants of the popular inference algorithm called the Max-Product algorithm and the Belief Propagation algorithm respectively. The Max-Sum algorithm and the Action-GDL algorithm are well-suited for multi-agent systems because it is distributed by nature and requires less communication than most DCOP algorithms. However, the resource requirements of these algorithms are still high for some multi-agent domains and various aspects of the algorithms have not been well studied for use in general multi-agent settings. This thesis is concerned with a variety of issues of applying the Max-Sum algorithms and the Action-GDL algorithm to general multi-agent settings. We develop a hybrid algorithm of ADOPT and Action-GDL in order to overcome the communication complexity of DCOPs. Secondly, we extend the Max-Sum algorithm to operate more efficiently in more general multi-agent settings in which computational complexity is high. We provide an algorithm that has a lower expected computational complexity for DCOPs even with n-ary constraints. Finally, In most DCOP literature, a one-to-one mapping between a variable and an agent is assumed. However, in real applications, many-to-one mappings are prevalent and can also be beneficial in terms of communication and hardware cost in situations where agents are acting as independent computing units. We consider how to exploit such mapping in order to increase efficiency

Dynamic Continuous Distributed Constraint Optimization Problems

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model multi-agent coordination problems that are distributed by nature. The formulation is suitable for problems where the environment does not change over time and where agents seek their value assignment from a discrete domain. However, in many real-world applications, agents often interact in a more dynamic environment and their variables usually require a more complex domain. Thus, the DCOP formulation lacks the capabilities to model the problems in such dynamic and complex environments. To address these limitations, researchers have proposed Dynamic DCOPs (D-DCOPs) to model how DCOPs dynamically change over time and Continuous DCOPs (C-DCOPs) to model DCOPs with continuous variables. The two models address the limitations of DCOPs but in isolation, and thus, it remains a challenge to model problems that have continuous variables and are in a dynamic environment. Therefore, this dissertation investigates a novel formulation that addresses the two limitations of DCOPs together by modeling both dynamic nature of the environment and continuous nature of the variables. Firstly, we propose Proactive Dynamic DCOPs (PD-DCOPs) which model and solve DCOPs in dynamic environment in a proactive manner. Secondly, we propose several C-DCOP algorithms that are efficient and we provide quality guarantee on their solution. Finally, we propose Dynamic Continuous DCOP (DC-DCOP), a novel formulation that models the DCOPs with continuous variables in a dynamic environment

1994 Science Information Management and Data Compression Workshop

This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on September 26-27, 1994, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival and retrieval of large quantities of data in future Earth and space science missions. It consisted of eleven presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center

On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective

Our situated environment is full of uncertainty and highly dynamic, thus hindering the widespread adoption of machine-led Intelligent Decision-Making (IDM) in real world scenarios. This means IDM should have the capability of continuously learning new skills and efficiently generalizing across wider applications. IDM benefits from any new approaches and theoretical breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the barriers between tasks and applications. Recent research has well-examined neural architecture, Transformer, as a backbone foundation model and its generalization to various tasks, including computer vision, natural language processing, and reinforcement learning. We therefore argue that a foundation decision model (FDM) can be established by formulating various decision-making tasks as a sequence decoding task using the Transformer architecture; this would be a promising solution to advance the applications of IDM in more complex real world tasks. In this paper, we elaborate on how a foundation decision model improves the efficiency and generalization of IDM. We also discuss potential applications of a FDM in multi-agent game AI, production scheduling, and robotics tasks. Finally, through a case study, we demonstrate our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters, which achieves human-level performance over 453 tasks, including text generation, images caption, video games playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 would be a baby step towards more autonomous and efficient real world IDM applications.Comment: 26 pages, 4 figure

Operating system fault tolerance support for real-time embedded applications

Tese de doutoramento em Electrónica Industrial (ramo de conhecimento em Informática Industrial)Fault tolerance is a means of achieving high dependability for critical and highavailability systems. Despite the efforts to prevent and remove faults during the development of these systems, the application of fault tolerance is usually required because the hardware may fail during system operation and software faults are very hard to eliminate completely. One of the difficulties in implementing fault tolerance techniques is the lack of support from operating systems and middleware. In most fault tolerant projects, the programmer has to develop a fault tolerance implementation for each application. This strong customization makes the fault-tolerant software costly and difficult to implement and maintain. In particular, for small-scale embedded systems, the introduction of fault tolerance techniques may also have impact on their restricted resources, such as processing power and memory size. The purpose of this research is to provide fault tolerance support for real-time applications in small-scale embedded systems. The main approach of this thesis is to develop and integrate a customizable and extendable fault tolerance framework into a real-time operating system, in order to fulfill the needs of a large range of dependable applications. Special attention is taken to allow the coexistence of fault tolerance with real-time constraints. The utilization of the proposed framework features several advantages over ad-hoc implementations, such as simplifying application-level programming and improving the system configurability and maintainability. In addition, this thesis also investigates the application of aspect-oriented techniques to the development of real-time embedded fault-tolerant software. Aspect- Oriented Programming (AOP) is employed to modularize all fault tolerant source code, following the principle of separation of concerns, and to integrate the proposed framework into the operating system. Two case studies are used to evaluate the proposed implementation in terms of performance and resource costs. The results show that the overheads related to the framework application are acceptable and the ones related to the AOP implementation are negligible.Tolerância a falhas é um meio de obter-se alta confiabilidade para sistemas críticos e de elevada disponibilidade. Apesar dos esforços para prevenir e remover falhas durante o desenvolvimento destes sistemas, a aplicação de tolerância a falhas é normalmente necessária, já que o hardware pode falhar durante a operação do sistema e falhas de software são muito difíceis de eliminar completamente. Uma das dificuldades na implementação de técnicas de tolerância a falhas é a falta de suporte por parte dos sistemas operativos e middleware. Na maioria dos projectos tolerantes a falhas, o programador deve desenvolver uma implementação de tolerância a falhas para cada aplicação. Esta elevada adaptação torna o software tolerante a falhas dispendioso e difícil de implementar e manter. Em particular, para sistemas embebidos de pequena escala, a introdução de técnicas de tolerância a falhas pode também ter impacto nos seus restritos recursos, tais como capacidade de processamento e tamanho da memória. O propósito desta tese é prover suporte à tolerância a falhas para aplicações de tempo real em sistemas embebidos de pequena escala. A principal abordagem utilizada nesta tese foi desenvolver e integrar uma framework tolerante a falhas, customizável e extensível, a um sistema operativo de tempo real, a fim de satisfazer às necessidades de uma larga gama de aplicações confiáveis. Especial atenção foi dada para permitir a coexistência de tolerância a falhas com restrições de tempo real. A utilização da framework proposta apresenta diversas vantagens sobre implementações ad-hoc, tais como simplificar a programação a nível da aplicação e melhorar a configurabilidade e a facilidade de manutenção do sistema. Além disto, esta tese também investiga a aplicação de técnicas orientadas a aspectos no desenvolvimento de software tolerante a falhas, embebido e de tempo real. A Programação Orientada a Aspectos (POA) é empregada para segregar em módulos isolados todo o código fonte tolerante a falhas, seguindo o princípio da separação de interesses, e para integrar a framework proposta com o sistema operativo. Dois casos de estudo são utilizados para avaliar a implementação proposta em termos de desempenho e utilização de recursos. Os resultados mostram que os acréscimos de recursos relativos à aplicação da framework são aceitáveis e os relativos à implementação POA são insignificantes

Resource-aware IoT Control: Saving Communication through Predictive Triggering

The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, feedback is introduced between them. Multiple feedback loops are thus closed over a shared, general-purpose network. Traditional feedback control is unsuitable for design of IoT control because it relies on high-rate periodic communication and is ignorant of the shared network resource. Therefore, recent event-based estimation methods are applied herein for resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not. While this can reduce network traffic significantly, a severe limitation of typical event-based approaches is the need for instantaneous triggering decisions that leave no time to reallocate freed resources (e.g., communication slots), which hence remain unused. To address this problem, novel predictive and self triggering protocols are proposed herein. From a unified Bayesian decision framework, two schemes are developed: self triggers that predict, at the current triggering instant, the next one; and predictive triggers that check at every time step, whether communication will be needed at a given prediction horizon. The suitability of these triggers for feedback control is demonstrated in hardware experiments on a cart-pole, and scalability is discussed with a multi-vehicle simulation.Comment: 16 pages, 15 figures, accepted article to appear in IEEE Internet of Things Journal. arXiv admin note: text overlap with arXiv:1609.0753