2,965 research outputs found
Differentially Private Multi-Agent Planning for Logistic-like Problems
Planning is one of the main approaches used to improve agents' working
efficiency by making plans beforehand. However, during planning, agents face
the risk of having their private information leaked. This paper proposes a
novel strong privacy-preserving planning approach for logistic-like problems.
This approach outperforms existing approaches by addressing two challenges: 1)
simultaneously achieving strong privacy, completeness and efficiency, and 2)
addressing communication constraints. These two challenges are prevalent in
many real-world applications including logistics in military environments and
packet routing in networks. To tackle these two challenges, our approach adopts
the differential privacy technique, which can both guarantee strong privacy and
control communication overhead. To the best of our knowledge, this paper is the
first to apply differential privacy to the field of multi-agent planning as a
means of preserving the privacy of agents for logistic-like problems. We
theoretically prove the strong privacy and completeness of our approach and
empirically demonstrate its efficiency. We also theoretically analyze the
communication overhead of our approach and illustrate how differential privacy
can be used to control it
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Finance is a particularly difficult playground for deep reinforcement
learning. However, establishing high-quality market environments and benchmarks
for financial reinforcement learning is challenging due to three major factors,
namely, low signal-to-noise ratio of financial data, survivorship bias of
historical data, and model overfitting in the backtesting stage. In this paper,
we present an openly accessible FinRL-Meta library that has been actively
maintained by the AI4Finance community. First, following a DataOps paradigm, we
will provide hundreds of market environments through an automatic pipeline that
collects dynamic datasets from real-world markets and processes them into
gym-style market environments. Second, we reproduce popular papers as stepping
stones for users to design new trading strategies. We also deploy the library
on cloud platforms so that users can visualize their own results and assess the
relative performance via community-wise competitions. Third, FinRL-Meta
provides tens of Jupyter/Python demos organized into a curriculum and a
documentation website to serve the rapidly growing community. FinRL-Meta is
available at: https://github.com/AI4Finance-Foundation/FinRL-MetaComment: NeurIPS 2022 Datasets and Benchmarks. 36th Conference on Neural
Information Processing Systems Datasets and Benchmarks Trac
How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems
Multi-agent cyberphysical systems enable new capabilities in efficiency,
resilience, and security. The unique characteristics of these systems prompt a
reevaluation of their security concepts, including their vulnerabilities, and
mechanisms to mitigate these vulnerabilities. This survey paper examines how
advancement in wireless networking, coupled with the sensing and computing in
cyberphysical systems, can foster novel security capabilities. This study
delves into three main themes related to securing multi-agent cyberphysical
systems. First, we discuss the threats that are particularly relevant to
multi-agent cyberphysical systems given the potential lack of trust between
agents. Second, we present prospects for sensing, contextual awareness, and
authentication, enabling the inference and measurement of ``inter-agent trust"
for these systems. Third, we elaborate on the application of quantifiable trust
notions to enable ``resilient coordination," where ``resilient" signifies
sustained functionality amid attacks on multiagent cyberphysical systems. We
refer to the capability of cyberphysical systems to self-organize, and
coordinate to achieve a task as autonomy. This survey unveils the cyberphysical
character of future interconnected systems as a pivotal catalyst for realizing
robust, trust-centered autonomy in tomorrow's world
Differentially Private Reward Functions for Multi-Agent Markov Decision Processes
Reward functions encode desired behavior in multi-agent Markov decision
processes, but onlookers may learn reward functions by observing agents, which
can reveal sensitive information. Therefore, in this paper we introduce and
compare two methods for privatizing reward functions in policy synthesis for
multi-agent Markov decision processes. Reward functions are privatized using
differential privacy, a statistical framework for protecting sensitive data.
Both methods we develop rely on the Gaussian mechanism, which is a method of
randomization we use to perturb (i) each agent's individual reward function or
(ii) the joint reward function shared by all agents. We prove that both of
these methods are differentially private and compare the abilities of each to
provide accurate reward values for policy synthesis. We then develop an
algorithm for the numerical computation of the performance loss due to privacy
on a case-by-case basis. We also exactly compute the computational complexity
of this algorithm in terms of system parameters and show that it is inherently
tractable. Numerical simulations are performed on a gridworld example and in
waypoint guidance of an autonomous vehicle, and both examples show that privacy
induces only negligible performance losses in practice.Comment: 11 Pages, 7 figure
Ethical Issues in Engineering Models: Personal Reflections
I start this contribution with an overview of my personal involvement—as an Operations Research consultant—in several engineering case-studies that may raise ethical questions; these case studies employ simulation models. Next, I present an overview of the recent literature on ethical issues in modeling, focusing on the validation of the model’s assumptions; the decisive role of these assumptions leads to the quest for robust models. Actually, models are meant to solve practical problems; these problems may have ethical implications for the various stakeholders; namely, modelers, clients, and the public at large. Finally, I briefly discuss whistle blowing.ethics;code of conduct;stakeholders;validity;risk analysis;simulation;operations research
Efficient approaches for multi-agent planning
Multi-agent planning (MAP) deals with planning systems that reason on long-term goals by multiple collaborative agents which want to maintain privacy on their knowledge. Recently, new MAP techniques have been devised to provide efficient solutions. Most approaches expand distributed searches using modified planners, where agents exchange public information. They present two drawbacks: they are planner-dependent; and incur a high communication cost. Instead, we present two algorithms whose search processes are monolithic (no communication while individual planning) and MAP tasks are compiled such that they are planner-independent (no programming effort needed when replacing the base planner). Our two approaches first assign each public goal to a subset of agents. In the first distributed approach, agents iteratively solve problems by receiving plans, goals and states from previous agents. After generating new plans by reusing previous agents' plans, they share the new plans and some obfuscated private information with the following agents. In the second centralized approach, agents generate an obfuscated version of their problems to protect privacy and then submit it to an agent that performs centralized planning. The resulting approaches are efficient, outperforming other state-of-the-art approaches.This work has been partially supported by MICINN projects TIN2008-06701-C03-03, TIN2011-27652-C03-02 and TIN2014-55637-C2-1-R
Smart Metering, Water Pricing and Social Media to Stimulate Residential Water Efficiency: Opportunities for the SmartH2O Project
Abstract The SmartH2O project aims to provide water utilities, municipalities and citizens with an ICT enabled platform to design, develop and implement better water management policies using innovative metering, social media and pricing mechanisms. This project has as a working hypothesis that high data quality obtained from smart meters and communicable through social media and other forms of interaction could be used to design and implement innovative and effective water pricing policies. Planned case studies in the UK and Switzerland are introduced. We anticipate that SmartH20 research outcomes will be of use to those interested in linking smart metering, social media and smart pricing approaches to achieve more sustainable water management outcomes
- …