Distributed strategy-updating rules for aggregative games of multi-integrator systems with coupled constraints

In this paper, we explore aggregative games over networks of multi-integrator agents with coupled constraints. To reach the general Nash equilibrium of an aggregative game, a distributed strategy-updating rule is proposed by a combination of the coordination of Lagrange multipliers and the estimation of the aggregator. Each player has only access to partial-decision information and communicates with his neighbors in a weight-balanced digraph which characterizes players' preferences as to the values of information received from neighbors. We first consider networks of double-integrator agents and then focus on multi-integrator agents. The effectiveness of the proposed strategy-updating rules is demonstrated by analyzing the convergence of corresponding dynamical systems via the Lyapunov stability theory, singular perturbation theory and passive theory. Numerical examples are given to illustrate our results.Comment: 9 pages, 4 figure

Game Theory Based Privacy Protection for Context-Aware Services

In the era of context-aware services, users are enjoying remarkable services based on data collected from a multitude of users. To receive services, they are at risk of leaking private information from adversaries possibly eavesdropping on the data and/or the un--trusted service platform selling off its data. Malicious adversaries may use leaked information to violate users\u27 privacy in unpredictable ways. To protect users\u27 privacy, many algorithms are proposed to protect users\u27 sensitive information by adding noise, thus causing context-aware service quality loss. Game theory has been utilized as a powerful tool to balance the tradeoff between privacy protection level and service quality. However, most of the existing schemes fail to depict the mutual relationship between any two parties involved: user, platform, and adversary. There is also an oversight to formulate the interaction occurring between multiple users, as well as the interaction between any two attributes. To solve these issues, this dissertation firstly proposes a three-party game framework to formulate the mutual interaction between three parties and study the optimal privacy protection level for context-aware services, thus optimize the service quality. Next, this dissertation extends the framework to a multi-user scenario and proposes a two-layer three-party game framework. This makes the proposed framework more realistic by further exploring the interaction, not only between different parties, but also between users. Finally, we focus on analyzing the impact of long-term time-serial data and the active actions of the platform and adversary. To achieve this objective, we design a three-party Stackelberg game model to help the user to decide whether to update information and the granularity of updated information

Semi-decentralized generalized Nash equilibrium seeking in monotone aggregative games

We address the generalized Nash equilibrium seeking problem for a population of agents playing aggregative games with affine coupling constraints. We focus on semi-decentralized communication architectures, where there is a central coordinator able to gather and broadcast signals of aggregative nature to the agents. By exploiting the framework of monotone operator theory and operator splitting, we first critically review the most relevant available algorithms and then design two novel schemes: (i) a single-layer, fixed-step algorithm with convergence guarantee for general (non cocoercive, non-strictly) monotone aggregative games and (ii) a single-layer proximal-type algorithm for a class of monotone aggregative games with linearly coupled cost functions. We also design novel accelerated variants of the algorithms via (alternating) inertial and over-relaxation steps. Finally, we show via numerical simulations that the proposed algorithms outperform those in the literature in terms of convergence speed

The experiences of peer relationships amongst autistic adolescents:A systematic review of the qualitative evidence

Background Peer relationships can be especially difficult for autistic adolescents, given their marked social communication difficulties. The purpose of this systematic review is to synthesise reported qualitative findings on how autistic adolescents experience peer relationships, including the rewards and challenges, from their perspective. The review includes the perspectives of others (e.g. parents, support workers), from included papers where these were reported in addition to the adolescent viewpoint. Method PRISMA guidelines and the Joanna Briggs Institute meta-aggregative approach to qualitative synthesis informed this review process. Articles included were published in the last ten years, specific to autistic adolescents between the ages of 10 and 19. Of the 75 articles meeting eligibility for full-text review, ten matched the final inclusion criteria. Results Findings relating to four main themes emerged from the qualitative synthesis: understanding friendship, having and wanting friends, challenges of peer relationships and overcoming challenges. Fourteen sub-topics are described in detail, for example, that autistic adolescents do have and want friends, though this is often not easily achieved, leading to feelings of loneliness. Findings also revealed experiences of peer rejection and victimisation, and specific factors that might make building positive peer relationships difficult, such as group settings. Conclusions Autistic adolescents face a number of difficulties with understanding social rules and conventions, which seem to make peer relationships difficult. However, due to many autistic adolescents having a desire for friendship, some have developed ways of overcoming these challenges. This review highlights that additional support is needed to support the development of autistic adolescents’ social skills and awareness; further research is needed to establish how this could be done most effectively

CORE: Automatic Molecule Optimization Using Copy & Refine Strategy

Molecule optimization is about generating molecule $Y$ with more desirable properties based on an input molecule $X$. The state-of-the-art approaches partition the molecules into a large set of substructures $S$ and grow the new molecule structure by iteratively predicting which substructure from $S$ to add. However, since the set of available substructures $S$ is large, such an iterative prediction task is often inaccurate especially for substructures that are infrequent in the training data. To address this challenge, we propose a new generating strategy called "Copy & Refine" (CORE), where at each step the generator first decides whether to copy an existing substructure from input $X$ or to generate a new substructure, then the most promising substructure will be added to the new molecule. Combining together with scaffolding tree generation and adversarial training, CORE can significantly improve several latest molecule optimization methods in various measures including drug likeness (QED), dopamine receptor (DRD2) and penalized LogP. We tested CORE and baselines using the ZINC database and CORE obtained up to 11% and 21% relatively improvement over the baselines on success rate on the complete test set and the subset with infrequent substructures, respectively.Comment: Accepted by AAAI 202

Location Privacy in Usage-Based Automotive Insurance: Attacks and Countermeasures

Usage-based insurance (UBI) is regarded as a promising way to provide accurate automotive insurance rates by analyzing the driving behaviors (e.g., speed, mileage, and harsh braking/accelerating) of drivers. The best practice that has been adopted by many insurance programs to protect users\u27 location privacy is the use of driving speed rather than GPS data. However, in this paper, we challenge this approach by presenting a novel speed-based location trajectory inference framework. The basic strategy of the proposed inference framework is motivated by the following observations. In practice, many environmental factors, such as real-time traffic and traffic regulations, can influence the driving speed. These factors provide side-channel information about the driving route, which can be exploited to infer the vehicle\u27s trace. We implement our discovered attack on a public data set in New Jersey. The experimental results show that the attacker has a nearly 60% probability of obtaining the real route if he chooses the top 10 candidate routes. To thwart the proposed attack, we design a privacy preserving scoring and data audition framework that enhances drivers\u27 control on location privacy without affecting the utility of UBI. Our defense framework can also detect users\u27 dishonest behavior (e.g., modification of speed data) via a probabilistic audition scheme. Extensive experimental results validate the effectiveness of the defense framework

Debating deliberative democracy: how deliberation changes the way people reason

The concepts of deliberation and deliberative democracy have attracted much attention in political theory over the past twenty years. At first seen as both highly idealised and unreflective of reality, they have now shed this accusation of impracticality, as practitioners and policy makers alike have attempted to institute deliberative principles on a national and international scale. Running alongside this has been the desire to both understand political deliberation and its effects more fully, and to then apply this new information back to deliberative democratic theory. This thesis sits in the latter tradition, presenting an empirical investigation of political deliberation and then discussing how it relates back to deliberative models of democracy. Where it departs from all of the contemporary experimental work, however, is the methodology and conceptual model it is founded upon. Embracing the decision and game theoretic approaches, I develop a three-fold framework to study the effects of deliberation on individual decision-making. After outlining two levels of ‘preference’ and ‘issue’, I focus on the third, which I term agency. I then compare a particular case of agency revision, which moves people from individualistic to team reasoning, before developing and putting into action an experimental test of the phenomenon. Finally, I then combine these results with the most recent drive in deliberative democracy towards a systemic approach, and derive an alternative, more positive argument for this recasting