Approximately Truthful Multi-Agent Optimization Using Cloud-Enforced Joint Differential Privacy

Multi-agent coordination problems often require agents to exchange state information in order to reach some collective goal, such as agreement on a final state value. In some cases, it is feasible that opportunistic agents may deceptively report false state values for their own benefit, e.g., to claim a larger portion of shared resources. Motivated by such cases, this paper presents a multi-agent coordination framework which disincentivizes opportunistic misreporting of state information. This paper focuses on multi-agent coordination problems that can be stated as nonlinear programs, with non-separable constraints coupling the agents. In this setting, an opportunistic agent may be tempted to skew the problem's constraints in its favor to reduce its local cost, and this is exactly the behavior we seek to disincentivize. The framework presented uses a primal-dual approach wherein the agents compute primal updates and a centralized cloud computer computes dual updates. All computations performed by the cloud are carried out in a way that enforces joint differential privacy, which adds noise in order to dilute any agent's influence upon the value of its cost function in the problem. We show that this dilution deters agents from intentionally misreporting their states to the cloud, and present bounds on the possible cost reduction an agent can attain through misreporting its state. This work extends our earlier work on incorporating ordinary differential privacy into multi-agent optimization, and we show that this work can be modified to provide a disincentivize for misreporting states to the cloud. Numerical results are presented to demonstrate convergence of the optimization algorithm under joint differential privacy.Comment: 17 pages, 3 figure

Performance Regulation and Tracking via Lookahead Simulation: Preliminary Results and Validation

This paper presents an approach to target tracking that is based on a variable-gain integrator and the Newton-Raphson method for finding zeros of a function. Its underscoring idea is the determination of the feedback law by measurements of the system's output and estimation of its future state via lookahead simulation. The resulting feedback law is generally nonlinear. We first apply the proposed approach to tracking a constant reference by the output of nonlinear memoryless plants. Then we extend it in a number of directions, including the tracking of time-varying reference signals by dynamic, possibly unstable systems. The approach is new hence its analysis is preliminary, and theoretical results are derived for nonlinear memoryless plants and linear dynamic plants. However, the setting for the controller does not require the plant-system to be either linear or stable, and this is verified by simulation of an inverted pendulum tracking a time-varying signal. We also demonstrate results of laboratory experiments of controlling a platoon of mobile robots.Comment: A modified version will appear in Proc. 56th IEEE Conf. on Decision and Control, 201

Realization of the Sensor Web Concept for Earth Science Using Mobile Robotic Platforms

©2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2007 IEEE Aerospace Conference,  3-10 March 2007, Big Sky, MT.DOI: 10.1109/AERO.2007.353086In this paper, we discuss the realization of a robotic mobile sensor network that allows for controlled reconfiguration of sensor assets in a decentralized manner. The motivation is to allow the construction of a new system of in-situ science observations that requires higher spatial and temporal resolution models that are needed for expanding our understanding of Earth system change. These observations could enable recording of spatial and temporal variations in environmental parameters required for such activities as monitoring of seismic activity, monitoring of civil and engineering infrastructures, and detection of toxic agents throughout a region of interest. The difficulty in establishing these science observations are that global formation properties must be achieved based on the local interactions between individual sensors. As such, we present a novel approach that allows for the sensor network to function in a decentralized manner and is thus able to achieve global formations despite individual sensor failure, limitations in communication range, and changing scientific objectives. Details on the sensing and control algorithms for controlled reconfiguration will be discussed and results of field deployment will be presented

Complex Processes from Dynamical Architectures with Time-Scale Hierarchy

The idea that complex motor, perceptual, and cognitive behaviors are composed of smaller units, which are somehow brought into a meaningful relation, permeates the biological and life sciences. However, no principled framework defining the constituent elementary processes has been developed to this date. Consequently, functional configurations (or architectures) relating elementary processes and external influences are mostly piecemeal formulations suitable to particular instances only. Here, we develop a general dynamical framework for distinct functional architectures characterized by the time-scale separation of their constituents and evaluate their efficiency. Thereto, we build on the (phase) flow of a system, which prescribes the temporal evolution of its state variables. The phase flow topology allows for the unambiguous classification of qualitatively distinct processes, which we consider to represent the functional units or modes within the dynamical architecture. Using the example of a composite movement we illustrate how different architectures can be characterized by their degree of time scale separation between the internal elements of the architecture (i.e. the functional modes) and external interventions. We reveal a tradeoff of the interactions between internal and external influences, which offers a theoretical justification for the efficient composition of complex processes out of non-trivial elementary processes or functional modes