Robust and reliable decision-making systems and algorithms

We investigate robustness and reliability in decision-making systems and algorithms based on the tradeoff between cost and performance. We propose two abstract frameworks to investigate robustness and reliability concerns, which critically impact the design and analysis of systems and algorithms based on unreliable components. We consider robustness in online systems and algorithms under the framework of online optimization subject to adversarial perturbations. The framework of online optimization models a rich class of problems from information theory, machine learning, game theory, optimization, and signal processing. This is a repeated game framework where, on each round, a player selects an action from a decision set using a randomized strategy, and then Nature reveals a loss function for this action, for which the player incurs a loss. Through a worst case adversary framework to model the perturbations, we introduce a randomized algorithm that is provably robust even against such adversarial attacks. In particular, we show that this algorithm is Hannan-consistent with respect to a rich class of randomized strategies under mild regularity conditions. We next focus on reliability of decision-making systems and algorithms based on the problem of fusing several unreliable computational units that perform the same task under cost and fidelity constraints. In particular, we model the relationship between the fidelity of the outcome and the cost of computing it as an additive perturbation. We analyze performance of repetition-based strategies that distribute cost across several unreliable units and fuse their outcomes. When the cost is a convex function of fidelity, the optimal repetition-based strategy in terms of minimizing total incurred cost while achieving a target mean-square error performance may fuse several computational units. For concave and linear costs, a single more reliable unit incurs lower cost compared to fusion of several lower cost and less reliable units while achieving the same mean-square error (MSE) performance. We show how our results give insight into problems from theoretical neuroscience, circuits, and crowdsourcing. We finally study an application of a partial information extension of the cost-fidelity framework of this dissertation to a stochastic gradient descent problem, where the underlying cost-fidelity function is assumed to be unknown. We present a generic framework for trading off fidelity and cost in computing stochastic gradients when the costs of acquiring stochastic gradients of different quality are not known a priori. We consider a mini-batch oracle that distributes a limited query budget over a number of stochastic gradients and aggregates them to estimate the true gradient. Since the optimal mini-batch size depends on the unknown cost fidelity function, we propose an algorithm, EE-Grad, that sequentially explores the performance of mini-batch oracles and exploits the accumulated knowledge to estimate the one achieving the best performance in terms of cost efficiency. We provide performance guarantees for EE-Grad with respect to the optimal mini-batch oracle, and illustrate these results in the case of strongly convex objectives

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

The discipline of embedded systems design

The wall between computer science and electrical engineering has kept the potential of embedded systems at bay. It is time to build a new scientific foundation with embedded systems design as the cornerstone, which will ensure a systematic and even-handed integration of the two fields. The embedded systems design problem certainly raises technology questions, but more important, it requires building a new scientific foundation that will systematically and even-handedly integrate computation and physicality from the bottom up. Support for this foundation will require enriching computer science paradigms to encompass models and methods traditionally found in electrical engineering

General Design Principles for Resilience and Adaptive Capacity in Legal Systems--With Applications to Climate Change Adaptation

No force has put more pressure on the legal system than is likely to be exerted as climate change begins to disrupt the settled expectations of humans. Demands on the legal system will be intense and long-term, but is the law up to the task? If it is, it will at least in part be because the legal system proves to be resilient and adaptive. The question this Article explores, therefore, is how to think about designing legal instruments and institutions now with confidence they will be resilient and adaptive to looming problems as massive, variable, and long-term in scale as climate change. Drawing from the body of resilience theory forged in natural and social sciences, this Article is the first to synthesize resilience theory in a framework relevant to lawyers and explore the general design principles it suggests for legal systems. Part I examines resilience - what it is and how to design for it in legal systems. It examines the normative dimensions of resilience and makes important distinctions between resilience of legal systems, resilience of laws they produce, and resilience of the other social and natural systems law addresses. Part II provides the theoretical context and design principles for adaptive capacity, focusing on adaptive management theory as an example for legal design. Part III suggests applications of these general principles to the challenge of designing law for responding to climate change, arguing that climate change adaptation law should draw from theories of adaptive management, dynamic federalism, new governance, and trans-governmental networks

Interactive Execution Monitoring of Agent Teams

There is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents, both human and machine. We characterize the domain-independent challenges posed by this problem, and describe how properties of domains influence the challenges and their solutions. We will concentrate on dynamic, data-rich domains where humans are ultimately responsible for team behavior. Thus, the automated aid should interactively support effective and timely decision making by the human. We present a domain-independent categorization of the types of alerts a plan-based monitoring system might issue to a user, where each type generally requires different monitoring techniques. We describe a monitoring framework for integrating many domain-specific and task-specific monitoring techniques and then using the concept of value of an alert to avoid operator overload. We use this framework to describe an execution monitoring approach we have used to implement Execution Assistants (EAs) in two different dynamic, data-rich, real-world domains to assist a human in monitoring team behavior. One domain (Army small unit operations) has hundreds of mobile, geographically distributed agents, a combination of humans, robots, and vehicles. The other domain (teams of unmanned ground and air vehicles) has a handful of cooperating robots. Both domains involve unpredictable adversaries in the vicinity. Our approach customizes monitoring behavior for each specific task, plan, and situation, as well as for user preferences. Our EAs alert the human controller when reported events threaten plan execution or physically threaten team members. Alerts were generated in a timely manner without inundating the user with too many alerts (less than 10 percent of alerts are unwanted, as judged by domain experts)

Efficient Distributed Online Prediction and Stochastic Optimization with Approximate Distributed Averaging

We study distributed methods for online prediction and stochastic optimization. Our approach is iterative: in each round nodes first perform local computations and then communicate in order to aggregate information and synchronize their decision variables. Synchronization is accomplished through the use of a distributed averaging protocol. When an exact distributed averaging protocol is used, it is known that the optimal regret bound of $\mathcal{O}(\sqrt{m})$ can be achieved using the distributed mini-batch algorithm of Dekel et al. (2012), where $m$ is the total number of samples processed across the network. We focus on methods using approximate distributed averaging protocols and show that the optimal regret bound can also be achieved in this setting. In particular, we propose a gossip-based optimization method which achieves the optimal regret bound. The amount of communication required depends on the network topology through the second largest eigenvalue of the transition matrix of a random walk on the network. In the setting of stochastic optimization, the proposed gossip-based approach achieves nearly-linear scaling: the optimization error is guaranteed to be no more than $\epsilon$ after $\mathcal{O}(\frac{1}{n \epsilon^2})$ rounds, each of which involves $\mathcal{O}(\log n)$ gossip iterations, when nodes communicate over a well-connected graph. This scaling law is also observed in numerical experiments on a cluster.Comment: 30 pages, 2 figure