Norm approximation for imperfect monitors

In this paper, we consider the runtime monitoring of norms with imperfect monitors. A monitor is imperfect for a norm if it has insufficient observational capabilities to determine if a given execution trace of a multi-agent system complies with or violates the norm. One approach to the problem of imperfect monitors is to enhance the observational capabilities of the normative organisation. However this may be costly or in some cases impossible. Instead we show how to synthesise an approximation of an 'ideal' norm that can be perfectly monitored given a monitor, and which is optimal in the sense that any other approximation would fail to detect at least as many violations of the ideal norm. We give a logical analysis of (im)perfect monitors. We state the computational complexity of the norm approximation problem, and give an optimal algorithm for generating optimal approximations of norms given a monitor

Bounded-Monitor Placement in Normative Environments

ISSN: 16130073 Funding: This work is partially supported by grants from CNPq/Brazil numbers 132339/2016-1 and 305969/2016-1.Publisher PD

Norm Monitoring under Partial Action Observability

In the context of using norms for controlling multi-agent systems, a vitally important question that has not yet been addressed in the literature is the development of mechanisms for monitoring norm compliance under partial action observability. This paper proposes the reconstruction of unobserved actions to tackle this problem. In particular, we formalise the problem of reconstructing unobserved actions, and propose an information model and algorithms for monitoring norms under partial action observability using two different processes for reconstructing unobserved actions. Our evaluation shows that reconstructing unobserved actions increases significantly the number of norm violations and fulfilments detected.Comment: Accepted at the IEEE Transaction on Cybernetic

Data-driven Inverse Optimization with Imperfect Information

In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent's objective function that best explains a historical sequence of signals and corresponding optimal actions. We focus here on situations where the observer has imperfect information, that is, where the agent's true objective function is not contained in the search space of candidate objectives, where the agent suffers from bounded rationality or implementation errors, or where the observed signal-response pairs are corrupted by measurement noise. We formalize this inverse optimization problem as a distributionally robust program minimizing the worst-case risk that the {\em predicted} decision ({\em i.e.}, the decision implied by a particular candidate objective) differs from the agent's {\em actual} response to a random signal. We show that our framework offers rigorous out-of-sample guarantees for different loss functions used to measure prediction errors and that the emerging inverse optimization problems can be exactly reformulated as (or safely approximated by) tractable convex programs when a new suboptimality loss function is used. We show through extensive numerical tests that the proposed distributionally robust approach to inverse optimization attains often better out-of-sample performance than the state-of-the-art approaches

Sensor Placement for Plan Monitoring using Genetic Programming

Postprin

A Compressive Sensing Based Method for Harmonic State Estimation

Power quality monitoring has become a vital need in modern power systems owing to the need for agile operation and troubleshooting scheme. On the other hand, the nature of load in modern power system is changing in many ways. Digital loads, which are mostly relied on power electronic equipment, may distort the quality of power flowing through the network. Moreover, one of the most critical objectives of smart grids is to improve quality of services delivered to customers, alongside with security, reliability and efficiency. To this end, a novel method based on compressive sensing is proposed in this paper to detect the source and the magnitude of the harmonics. The method takes advantages of compressive sensing theory in such a way that a real-time monitoring of harmonic distortion is obtained with a limited number of measurements. The efficacy of the method is checked by means of various simulations on IEEE 118 bus test system. The results show the capabilities of the method in both noisy and noise-free conditions

An Sveir Model for Assessing Potential Impact of an Imperfect Anti-SARS Vaccine

The control of severe acute respiratory syndrome (SARS), a fatal contagious viral disease that spread to over 32 countries in 2003, was based on quarantine of latently infected individuals and isolation of individuals with clinical symptoms of SARS. Owing to the recent ongoing clinical trials of some candidate anti-SARS vaccines, this study aims to assess, via mathematical modelling, the potential impact of a SARS vaccine, assumed to be imperfect, in curtailing future outbreaks. A relatively simple deterministic model is designed for this purpose. It is shown, using Lyapunov function theory and the theory of compound matrices, that the dynamics of the model are determined by a certain threshold quantity known as the control reproduction number (Rv). If Rv ≤ 1, the disease will be eliminated from the community; whereas an epidemic occurs if Rv \u3e 1. This study further shows that an imperfect SARS vaccine with infection-blocking efficacy is always beneficial in reducing disease spread within the community, although its overall impact increases with increasing efficacy and coverage. In particular, it is shown that the fraction of individuals vaccinated at steady-state and vaccine efficacy play equal roles in reducing disease burden, and the vaccine must have efficacy of at least 75% to lead to effective control of SARS (assuming R0 = 4). Numerical simulations are used to explore the severity of outbreaks when Rv \u3e 1