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  • Bio-inspired Optimization: metaheuristic algorithms for optimization
    arXiv.cs.MA Pub Date : 2020-02-24
    Pravin S Game; Dr. Vinod Vaze; Dr. Emmanuel M

    In today's day and time solving real-world complex problems has become fundamentally vital and critical task. Many of these are combinatorial problems, where optimal solutions are sought rather than exact solutions. Traditional optimization methods are found to be effective for small scale problems. However, for real-world large scale problems, traditional methods either do not scale up or fail to

    更新日期:2020-03-28
  • Order Effects of Measurements in Multi-Agent Hypothesis Testing
    arXiv.cs.MA Pub Date : 2020-03-26
    Aneesh Raghavan; John S. Baras

    All propositions from the set of events for an agent in a multi-agent system might not be simultaneously verifiable. In this paper, we revisit the concepts of \textit{event-state-operation structure} and \textit{relationship of incompatibility} from literature and use them as a tool to study the algebraic structure of the set of events. We present an example from multi-agent hypothesis testing where

    更新日期:2020-03-28
  • Too many cooks: Coordinating multi-agent collaboration through inverse planning
    arXiv.cs.MA Pub Date : 2020-03-26
    Rose E. Wang; Sarah A. Wu; James A. Evans; Joshua B. Tenenbaum; David C. Parkes; Max Kleiman-Weiner

    Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub-tasks to work on in parallel. Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi-agent learning

    更新日期:2020-03-28
  • Is the Juice Worth the Squeeze? Machine Learning (ML) In and For Agent-Based Modelling (ABM)
    arXiv.cs.MA Pub Date : 2020-03-26
    Johannes Dahlke; Kristina Bogner; Matthias Mueller; Thomas Berger; Andreas Pyka; Bernd Ebersberger

    In recent years, many scholars praised the seemingly endless possibilities of using machine learning (ML) techniques in and for agent-based simulation models (ABM). To get a more comprehensive understanding of these possibilities, we conduct a systematic literature review (SLR) and classify the literature on the application of ML in and for ABM according to a theoretically derived classification scheme

    更新日期:2020-03-28
  • Resilient Distributed Diffusion for Multi-task Estimation
    arXiv.cs.MA Pub Date : 2020-03-23
    Jiani Li; Xenofon Koutsoukos

    Distributed diffusion is a powerful algorithm for multi-task state estimation which enables networked agents to interact with neighbors to process input data and diffuse information across the network. Compared to a centralized approach, diffusion offers multiple advantages that include robustness to node and link failures. In this paper, we consider distributed diffusion for multi-task estimation

    更新日期:2020-03-28
  • Emergence of Scenario-Appropriate Collaborative Behaviors for Teams of Robotic Bodyguards
    arXiv.cs.MA Pub Date : 2018-09-12
    Hassam Ullah Sheikh; Ladislau Boloni

    We are considering the problem of controlling a team of robotic bodyguards protecting a VIP from physical assault in the presence of neutral and/or adversarial bystanders. This task is part of a much larger class of problems involving coordinated robot behavior in the presence of humans. This problem is challenging due to the large number of active entities with different agendas, the need of cooperation

    更新日期:2020-03-28
  • Norms and Sanctions as a Basis for Promoting Cybersecurity Practices
    arXiv.cs.MA Pub Date : 2020-03-25
    Nirav AjmeriNorth Carolina State University; Shubham GoyalAmazon; Munindar P. SinghNorth Carolina State University

    Many cybersecurity breaches occur due to users not following good cybersecurity practices, chief among them being regulations for applying software patches to operating systems, updating applications, and maintaining strong passwords. We capture cybersecurity expectations on users as norms. We empirically investigate sanctioning mechanisms in promoting compliance with those norms as well as the detrimental

    更新日期:2020-03-26
  • Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory
    arXiv.cs.MA Pub Date : 2020-03-24
    Berat Mert Albaba; Yildiray Yildiz

    In this paper, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The need for a modeling framework that can address multiple human-human and human-automation interactions, where all the agents can be modeled as decision makers simultaneously, is the main motivation

    更新日期:2020-03-26
  • Robust Stochastic Bayesian Games for Behavior Space Coverage
    arXiv.cs.MA Pub Date : 2020-03-25
    Julian Bernhard; Alois Knoll

    A key challenge in multi-agent systems is the design of intelligent agents solving real-world tasks in close interaction with other agents (e.g. humans), thereby being confronted with a variety of behavioral variations and limited knowledge about the true behaviors of observed agents. The practicability of existing works addressing this challenge is being limited due to using finite sets of hypothesis

    更新日期:2020-03-26
  • A generic ontology and recovery protocols for Human-Robot Collaboration (HRC) systems
    arXiv.cs.MA Pub Date : 2020-03-20
    Kamil Skarzynski; Marcin Stepniak; Waldemar Bartyna; Stanislaw Ambroszkiewicz

    Humans are considered as integral components of Human-Robot Collaboration (HRC) systems, not only as object (e.g. in health care), but also as operators and service providers in manufacturing. Sophisticated and complex tasks are to be collaboratively executed by devices (robots) and humans. We introduce a generic ontology for HRC systems. Description of humans is a part of the ontology. Critical and

    更新日期:2020-03-24
  • Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation
    arXiv.cs.MA Pub Date : 2020-03-21
    Guohui Ding; Joewie J. Koh; Kelly Merckaert; Bram Vanderborght; Marco M. Nicotra; Christoffer Heckman; Alessandro Roncone; Lijun Chen

    We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate

    更新日期:2020-03-24
  • Who2com: Collaborative Perception via Learnable Handshake Communication
    arXiv.cs.MA Pub Date : 2020-03-21
    Yen-Cheng Liu; Junjiao Tian; Chih-Yao Ma; Nathan Glaser; Chia-Wen Kuo; Zsolt Kira

    In this paper, we propose the problem of collaborative perception, where robots can combine their local observations with those of neighboring agents in a learnable way to improve accuracy on a perception task. Unlike existing work in robotics and multi-agent reinforcement learning, we formulate the problem as one where learned information must be shared across a set of agents in a bandwidth-sensitive

    更新日期:2020-03-24
  • Resilience in Collaborative Optimization: Redundant and Independent Cost Functions
    arXiv.cs.MA Pub Date : 2020-03-21
    Nirupam Gupta; Nitin H. Vaidya

    This report considers the problem of Byzantine fault-tolerance in multi-agent collaborative optimization. In this problem, each agent has a local cost function. The goal of a collaborative optimization algorithm is to compute a minimum of the aggregate of the agents' cost functions. We consider the case when a certain number of agents may be Byzantine faulty. Such faulty agents may not follow a prescribed

    更新日期:2020-03-24
  • A Game-Theoretic Model of Human Driving and Application to Discretionary Lane-Changes
    arXiv.cs.MA Pub Date : 2020-03-22
    Jehong Yoo; Reza Langari

    In this paper we consider the application of Stackelberg game theory to model discretionary lane-changing in lightly congested highway setting. The fundamental intent of this model, which is parameterized to capture driver disposition (aggressiveness or inattentiveness), is to help with the development of decision-making strategies for autonomous vehicles in ways that are mindful of how human drivers

    更新日期:2020-03-24
  • Data-based Receding Horizon Control of Linear Network Systems
    arXiv.cs.MA Pub Date : 2020-03-22
    Ahmed Allibhoy; Jorge Cortés

    We propose a distributed data-based predictive control scheme to stabilize a network system described by linear dynamics. Agents cooperate to predict the future system evolution without knowledge of the dynamics, relying instead on learning a data-based representation from a single sample trajectory. We employ this representation to reformulate the finite-horizon Linear Quadratic Regulator problem

    更新日期:2020-03-24
  • Modelling transmission and control of the COVID-19 pandemic in Australia
    arXiv.cs.MA Pub Date : 2020-03-23
    Sheryl L. Chang; Nathan Harding; Cameron Zachreson; Oliver M. Cliff; Mikhail Prokopenko

    In this paper we develop an agent-based model for a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to reproduce several characteristics of COVID-19 transmission, accounting for its reproductive number, the length of incubation and generation periods, age-dependent attack rates, and the growth rate of cumulative incidence during a sustained

    更新日期:2020-03-24
  • Optimising Game Tactics for Football
    arXiv.cs.MA Pub Date : 2020-03-23
    Ryan Beal; Georgios Chalkiadakis; Timothy J. Norman; Sarvapali D. Ramchurn

    In this paper we present a novel approach to optimise tactical and strategic decision making in football (soccer). We model the game of football as a multi-stage game which is made up from a Bayesian game to model the pre-match decisions and a stochastic game to model the in-match state transitions and decisions. Using this formulation, we propose a method to predict the probability of game outcomes

    更新日期:2020-03-24
  • Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium
    arXiv.cs.MA Pub Date : 2020-02-17
    Qiaomin Xie; Yudong Chen; Zhaoran Wang; Zhuoran Yang

    We develop provably efficient reinforcement learning algorithms for two-player zero-sum Markov games in which the two players simultaneously take actions. To incorporate function approximation, we consider a family of Markov games where the reward function and transition kernel possess a linear structure. Both the offline and online settings of the problems are considered. In the offline setting, we

    更新日期:2020-03-24
  • Strategic Contention Resolution in Multiple Channels
    arXiv.cs.MA Pub Date : 2018-10-10
    George Christodoulou; Themistoklis Melissourgos; Paul G. Spirakis

    We consider the problem of resolving contention in communication networks with selfish users. In a \textit{contention game} each of $n \geq 2$ identical players has a single information packet that she wants to transmit using one of $k \geq 1$ multiple-access channels. To do that, a player chooses a slotted-time protocol that prescribes the probabilities with which at a given time-step she will attempt

    更新日期:2020-03-24
  • Modeling limited attention in opinion dynamics by topological interactions
    arXiv.cs.MA Pub Date : 2020-03-19
    Francesca Ceragioli; Paolo Frasca; Wilbert Samuel Rossi

    This work explores models of opinion dynamics with opinion-dependent connectivity. Our starting point is that individuals have limited capabilities to engage in interactions with their peers. Motivated by this observation, we propose a continuous-time opinion dynamics model such that interactions take place with a limited number of peers: we refer to these interactions as topological, as opposed to

    更新日期:2020-03-20
  • Redistribution Systems and PRAM
    arXiv.cs.MA Pub Date : 2020-03-18
    Paul Cohen; Tomasz Loboda

    Redistribution systems iteratively redistribute mass between groups under the control of rules. PRAM is a framework for building redistribution systems. We discuss the relationships between redistribution systems, agent-based systems, compartmental models and Bayesian models. PRAM puts agent-based models on a sound probabilistic footing by reformulating them as redistribution systems. This provides

    更新日期:2020-03-20
  • Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
    arXiv.cs.MA Pub Date : 2020-03-19
    Tabish Rashid; Mikayel Samvelyan; Christian Schroeder de Witt; Gregory Farquhar; Jakob Foerster; Shimon Whiteson

    In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion. At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted. Learning joint action-values conditioned on extra state information is an attractive way to exploit centralised learning

    更新日期:2020-03-20
  • Limited Lookahead in Imperfect-Information Games
    arXiv.cs.MA Pub Date : 2019-02-17
    Christian Kroer; Tuomas Sandholm

    Limited lookahead has been studied for decades in perfect-information games. We initiate a new direction via two simultaneous deviation points: generalization to imperfect-information games and a game-theoretic approach. We study how one should act when facing an opponent whose lookahead is limited. We study this for opponents that differ based on their lookahead depth, based on whether they, too,

    更新日期:2020-03-20
  • An Iterative Quadratic Method for General-Sum Differential Games with Feedback Linearizable Dynamics
    arXiv.cs.MA Pub Date : 2019-10-01
    David Fridovich-Keil; Vicenc Rubies-Royo; Claire J. Tomlin

    Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear optimal control community. Recent work has applied similar methodology in the setting of multiplayer general-sum differential games. Here, ILQ methods are capable of finding local equilibria in interactive motion planning problems in real-time. As in most iterative procedures, however, this approach can be sensitive to initial

    更新日期:2020-03-20
  • Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
    arXiv.cs.MA Pub Date : 2019-11-13
    Luca Ballotta; Luca Schenato; Luca Carlone

    This paper investigates the use of a networked system (e.g., swarm of robots, smart grid, sensor network) to monitor a time-varying phenomenon of interest in the presence of communication and computation latency. Recent advances on edge computing are enabling processing to be performed at each sensor, hence we investigate the fundamental latency-accuracy trade-off, arising when a sensor in the network

    更新日期:2020-03-20
  • Ford Multi-AV Seasonal Dataset
    arXiv.cs.MA Pub Date : 2020-03-17
    Siddharth AgarwalFord AV LLC; Ankit VoraFord AV LLC; Gaurav PandeyFord Motor Company; Wayne WilliamsFord AV LLC; Helen KourousFord AV LLC; James McBrideFord Motor Company

    This paper presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. The vehicles traversed an average route of 66 km in Michigan that included a mix of driving scenarios such as the Detroit Airport, freeways, city-centers, university campus and suburban neighbourhoods, etc. Each vehicle used in this data collection

    更新日期:2020-03-19
  • Multi-Agent Reinforcement Learning with Emergent Roles
    arXiv.cs.MA Pub Date : 2020-03-18
    Tonghan Wang; Heng Dong; Victor Lesser; Chongjie Zhang

    The role concept provides a useful tool to design and understand complex multi-agent systems, which allows agents with a similar role to share similar behaviors. However, existing role-based methods use prior domain knowledge and predefine role structures and behaviors. In contrast, multi-agent reinforcement learning (MARL) provides flexibility and adaptability, but less efficiency in complex tasks

    更新日期:2020-03-19
  • How social feedback processing in the brain shapes collective opinion processes in the era of social media
    arXiv.cs.MA Pub Date : 2020-03-18
    Sven Banisch; Felix Gaisbauer; Eckehard Olbrich

    What are the mechanisms by which groups with certain opinions gain public voice and force others holding a different view into silence? And how does social media play into this? Drawing on recent neuro-scientific insights into the processing of social feedback, we develop a theoretical model that allows to address these questions. The model captures phenomena described by spiral of silence theory of

    更新日期:2020-03-19
  • Social navigation with human empowerment driven reinforcement learning
    arXiv.cs.MA Pub Date : 2020-03-18
    Tessa van der Heiden; Christian Weiss; Naveen Nagaraja Shankar; Efstratios Gavves; Herke van Hoof

    The next generation of mobile robots needs to be socially-compliant to be accepted by humans. As simple as this task may seem, defining compliance formally is not trivial. Yet, classical reinforcement learning (RL) relies upon hard-coded reward signals. In this work, we go beyond this approach and provide the agent with intrinsic motivation using empowerment. Empowerment maximizes the influence of

    更新日期:2020-03-19
  • Influence of CAV Clustering Strategies on Mixed Traffic Flow Characteristics: An Analysis of Vehicle Trajectory Data
    arXiv.cs.MA Pub Date : 2020-03-16
    Zijia Zhong; Earl E. Lee; Mark Nejad; Joyoung Lee

    Being one of the most promising applications enabled by connected and automated vehicles (CAV) technology, Cooperative Adaptive Cruise Control (CACC) is expected to be deployed in the near term on public roads.} Thus far, the majority of the CACC studies have been focusing on the overall network performance with limited insights on the potential impacts of CAVs on human-driven vehicles (HVs).This paper

    更新日期:2020-03-19
  • The Shapeshifter: a Morphing, Multi-Agent,Multi-Modal Robotic Platform for the Exploration of Titan (preprint version)
    arXiv.cs.MA Pub Date : 2020-03-16
    Ali-akbar Agha-mohammadiJet Propulsion Lab., California Institute of Technology and; Andrea TagliabueMassachusetts Institute of Technology and; Stephanie SchneiderSanford University and; Benjamin MorrellJet Propulsion Lab., California Institute of Technology and; Marco PavoneSanford University and; Jason HofgartnerJet Propulsion Lab., California Institute of Technology and; Issa A. D. NesnasJet Propulsion

    In this report for the Nasa NIAC Phase I study, we present a mission architecture and a robotic platform, the Shapeshifter, that allow multi-domain and redundant mobility on Saturn's moon Titan, and potentially other bodies with atmospheres. The Shapeshifter is a collection of simple and affordable robotic units, called Cobots, comparable to personal palm-size quadcopters. By attaching and detaching

    更新日期:2020-03-19
  • From Sensor to Processing Networks: Optimal Estimation with Computation and Communication Latency
    arXiv.cs.MA Pub Date : 2020-03-16
    Luca Ballotta; Luca Schenato; Luca Carlone

    This paper investigates the use of a networked system ($e.g.$, swarm of robots, smart grid, sensor network) to monitor a time-varying phenomenon of interest in the presence of communication and computation latency. Recent advances in edge computing have enabled processing to be spread across the network, hence we investigate the fundamental computation-communication trade-off, arising when a sensor

    更新日期:2020-03-19
  • A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance
    arXiv.cs.MA Pub Date : 2020-03-17
    Marc Brittain; Xuxi Yang; Peng Wei

    A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynamic sector in en route airspace. Currently the sector capacity is limited by human air traffic controller's cognitive limitation. In order to scale up to a high-density airspace, in this work we investigate the feasibility

    更新日期:2020-03-19
  • Sequential Forecasting of 100,000 Points
    arXiv.cs.MA Pub Date : 2020-03-18
    Xinshuo Weng; Jianren Wang; Sergey Levine; Kris Kitani; Nicholas Rhinehart

    Predicting the future is a crucial first step to effective control, since systems that can predict the future can select plans that lead to desired outcomes. In this work, we study the problem of future prediction at the level of 3D scenes, represented by point clouds captured by a LiDAR sensor, i.e., directly learning to forecast the evolution of >100,000 points that comprise a complete scene. We

    更新日期:2020-03-19
  • Combining No-regret and Q-learning
    arXiv.cs.MA Pub Date : 2019-10-07
    Ian A. Kash; Michael Sullins; Katja Hofmann

    Counterfactual Regret Minimization (CFR) has found success in settings like poker which have both terminal states and perfect recall. We seek to understand how to relax these requirements. As a first step, we introduce a simple algorithm, local no-regret learning (LONR), which uses a Q-learning-like update rule to allow learning without terminal states or perfect recall. We prove its convergence for

    更新日期:2020-03-18
  • Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation
    arXiv.cs.MA Pub Date : 2019-09-20
    Renhao Wang; Adam Scibior; Frank Wood

    Imitation learning is a promising approach to end-to-end training of autonomous vehicle controllers. Typically the driving process with such approaches is entirely automatic and black-box, although in practice it is desirable to control the vehicle through high-level commands, such as telling it which way to go at an intersection. In existing work this has been accomplished by the application of a

    更新日期:2020-03-18
  • Accelerating and Improving AlphaZero Using Population Based Training
    arXiv.cs.MA Pub Date : 2020-03-13
    Ti-Rong Wu; Ting-Han Wei; I-Chen Wu

    AlphaZero has been very successful in many games. Unfortunately, it still consumes a huge amount of computing resources, the majority of which is spent in self-play. Hyperparameter tuning exacerbates the training cost since each hyperparameter configuration requires its own time to train one run, during which it will generate its own self-play records. As a result, multiple runs are usually needed

    更新日期:2020-03-16
  • SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic
    arXiv.cs.MA Pub Date : 2019-11-11
    Panpan Cai; Yiyuan Lee; Yuanfu Luo; David Hsu

    Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants. This paper presents SUMMIT, a high-fidelity simulator that facilitates the development and testing of crowd-driving algorithms. By leveraging the open-source OpenStreetMap map database and a heterogeneous multi-agent motion prediction model developed

    更新日期:2020-03-16
  • Negotiation-based Human-Robot Collaboration via Augmented Reality
    arXiv.cs.MA Pub Date : 2019-09-24
    Kishan Chandan; Vidisha Kudalkar Xiang Li; Shiqi Zhang

    Effective human-robot collaboration (HRC) requires extensive communication among the human and robot teammates, because their actions can potentially produce conflicts, synergies, or both. We develop a novel augmented reality (AR) interface to bridge the communication gap between human and robot teammates. Building on our AR interface, we develop an AR-mediated, negotiation-based (ARN) framework for

    更新日期:2020-03-12
  • Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing
    arXiv.cs.MA Pub Date : 2020-03-05
    Hangyu Mao; Zhibo Gong; Zhen Xiao

    In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global reward signal assigns the same global reward to all agents without distinguishing their contributions, while the local reward signal provides different local rewards to each agent based solely on individual behavior. Both

    更新日期:2020-03-10
  • Strategic Abilities of Asynchronous Agents: Semantic Paradoxes and How to Tame Them
    arXiv.cs.MA Pub Date : 2020-03-08
    Wojciech Jamroga; Wojciech Penczek; Teofil Sidoruk

    Recently, we proposed a framework for verification of agents' abilities in asynchronous multi-agent systems, together with an algorithm for automated reduction of models. The semantics was built on the modeling tradition of distributed systems. As we show here, this can sometimes lead to paradoxical interpretation of formulas when reasoning about the outcome of strategies. First, the semantics disregards

    更新日期:2020-03-10
  • Keeping Your Friends Close: Land Allocation with Friends
    arXiv.cs.MA Pub Date : 2020-03-07
    Edith Elkind; Neel Patel; Alan Tsang; Yair Zick

    We examine the problem of assigning plots of land to prospective buyers who prefer living next to their friends. They care not only about the plot they receive, but also about their neighbors. This externality results in a highly non-trivial problem structure, as both friendship and land value play a role in determining agent behavior. We examine mechanisms that guarantee truthful reporting of both

    更新日期:2020-03-10
  • FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
    arXiv.cs.MA Pub Date : 2020-03-09
    Aman Sinha; Matthew O'Kelly; Hongrui Zheng; Rahul Mangharam; John Duchi; Russ Tedrake

    Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. This work makes algorithmic

    更新日期:2020-03-10
  • BitTensor: An Intermodel Intelligence Measure
    arXiv.cs.MA Pub Date : 2020-03-09
    Jacob Steeves; Ala Shaabana; Matthew McAteer

    A purely inter-model version of a machine intelligence benchmark would allow us to measure intelligence directly as information without projecting that information onto labeled datasets. We propose a framework in which other learners measure the informational significance of their peers across a network and use a digital ledger to negotiate the scores. However, the main benefits of measuring intelligence

    更新日期:2020-03-10
  • AED: An Anytime Evolutionary DCOP Algorithm
    arXiv.cs.MA Pub Date : 2019-09-13
    Saaduddin Mahmud; Moumita Choudhury; Md. Mosaddek Khan; Long Tran-Thanh; Nicholas R. Jennings

    Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combinatorial optimization problems. However, the potential of this metaheuristic has not been utilized in Distributed Constraint Optimization Problems (DCOPs), a well-known class of combinatorial optimization problems prevalent

    更新日期:2020-03-10
  • Private Rank Aggregation under Local Differential Privacy
    arXiv.cs.MA Pub Date : 2019-08-13
    Ziqi Yan; Gang Li; Jiqiang Liu

    In typical collective decision-making scenarios, rank aggregation aims to combine different agents' preferences over the given alternatives into an aggregate ranking that agrees the most with all the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. All existing works that

    更新日期:2020-03-10
  • Asynchronous and Parallel Distributed Pose Graph Optimization
    arXiv.cs.MA Pub Date : 2020-03-06
    Yulun Tian; Alec Koppel; Amrit Singh Bedi; Jonathan P. How

    We present Asynchronous Stochastic Parallel Pose Graph Optimization (ASAPP), the first asynchronous algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous localization and mapping. By enabling robots to optimize their local trajectory estimates without synchronization, ASAPP offers resiliency against communication delays and alleviates the need to wait for stragglers in

    更新日期:2020-03-09
  • Hysteresis and disorder-induced order in continuous kinetic-like opinion dynamics in complex networks
    arXiv.cs.MA Pub Date : 2020-02-21
    A. L. Oestereich; M. A. Pires; S. M. Duarte Queirós; N. Crokidakis

    In this work we tackle a kinetic-like model of opinions dynamics in a networked population endued with a quenched plurality and polarization. Additionally, we consider pairwise interactions that are restrictive, which is modeled with a smooth bounded confidence. Our results show the interesting emergence of nonequilibrium hysteresis and heterogeneity-assisted ordering. Such counterintuitive phenomena

    更新日期:2020-03-09
  • BARK: Open Behavior Benchmarking in Multi-Agent Environments
    arXiv.cs.MA Pub Date : 2020-03-05
    Julian Bernhard; Klemens Esterle; Patrick Hart; Tobias Kessler

    Predicting and planning interactive behaviors in complex traffic situations presents a challenging task. Especially in scenarios involving multiple traffic participants that interact densely, autonomous vehicles still struggle to interpret situations and to eventually achieve their own driving goal. As driving tests are costly and challenging scenarios are hard to find and reproduce, simulation is

    更新日期:2020-03-06
  • Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
    arXiv.cs.MA Pub Date : 2019-01-14
    Markus Fröhle; Karl Granström; Henk Wymeersch

    A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization

    更新日期:2020-03-06
  • CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association
    arXiv.cs.MA Pub Date : 2019-02-06
    Kaveh Fathian; Kasra Khosoussi; Yulun Tian; Parker Lusk; Jonathan P. How

    Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment. Multi-way data association methods provide a mechanism to improve alignment accuracy of pairwise associations and ensure their consistency. However, existing methods that solve this computationally challenging problem are often too slow for real-time applications

    更新日期:2020-03-06
  • Fair Division of Mixed Divisible and Indivisible Goods
    arXiv.cs.MA Pub Date : 2019-11-16
    Xiaohui Bei; Zihao Li; Jinyan Liu; Shengxin Liu; Xinhang Lu

    We study the problem of fair division when the resources contain both divisible and indivisible goods. Classic fairness notions such as envy-freeness (EF) and envy-freeness up to one good (EF1) cannot be directly applied to the mixed goods setting. In this work, we propose a new fairness notion envy-freeness for mixed goods (EFM), which is a direct generalization of both EF and EF1 to the mixed goods

    更新日期:2020-03-06
  • On Emergent Communication in Competitive Multi-Agent Teams
    arXiv.cs.MA Pub Date : 2020-03-04
    Paul Pu Liang; Jeffrey Chen; Ruslan Salakhutdinov; Louis-Philippe Morency; Satwik Kottur

    Several recent works have found the emergence of grounded compositional language in the communication protocols developed by mostly cooperative multi-agent systems when learned end-to-end to maximize performance on a downstream task. However, human populations learn to solve complex tasks involving communicative behaviors not only in fully cooperative settings but also in scenarios where competition

    更新日期:2020-03-05
  • A Distributed Pipeline for Scalable, Deconflicted Formation Flying
    arXiv.cs.MA Pub Date : 2020-03-04
    Parker C. Lusk; Xiaoyi Cai; Samir Wadhwania; Aleix Paris; Kaveh Fathian; Jonathan P. How

    Reliance on external localization infrastructure and centralized coordination are main limiting factors for formation flying of vehicles in large numbers and in unprepared environments. While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and scalability. To address these shortcomings

    更新日期:2020-03-05
  • Adaptive Online Distributed Optimal Control of Very-Large-Scale Robotic Systems
    arXiv.cs.MA Pub Date : 2020-03-04
    Pingping Zhu; Chang Liu; Silvia Ferrari

    This paper presents an adaptive online distributed optimal control approach that is applicable to optimal planning for very-large-scale robotics systems in highly uncertain environments. This approach is developed based on the optimal mass transport theory and is also viewed as an online reinforcement learning and approximate dynamic programming approach in the Wasserstein-GMM space, where a novel

    更新日期:2020-03-05
  • Adaptation in Online Social Learning
    arXiv.cs.MA Pub Date : 2020-03-04
    Virginia Bordignon; Vincenzo Matta; Ali H. Sayed

    This work studies social learning under non-stationary conditions. Although designed for online inference, classic social learning algorithms perform poorly under drifting conditions. To mitigate this drawback, we propose the Adaptive Social Learning (ASL) strategy. This strategy leverages an adaptive Bayesian update, where the adaptation degree can be modulated by tuning a suitable step-size parameter

    更新日期:2020-03-05
  • Semantic Web Environments for Multi-Agent Systems: Enabling agents to use Web of Things via semantic web
    arXiv.cs.MA Pub Date : 2020-02-20
    Alaa Daoud

    The Web is ubiquitous, increasingly populated with interconnected data, services, people, and objects. Semantic web technologies (SWT) promote uniformity of data formats, as well as modularization and reuse of specifications (e.g., ontologies), by allowing them to include and refer to information provided by other ontologies. In such a context, multi-agent system (MAS) technologies are the right abstraction

    更新日期:2020-03-05
  • From Motions to Emotions: Can the Fundamental Emotions be Expressed in a Robot Swarm?
    arXiv.cs.MA Pub Date : 2019-03-28
    María Santos; Magnus Egerstedt

    This paper explores the expressive capabilities of a swarm of miniature mobile robots within the context of inter-robot interactions and their mapping to the so-called fundamental emotions. In particular, we investigate how motion and shape descriptors that are psychologically associated with different emotions can be incorporated into different swarm behaviors for the purpose of artistic expositions

    更新日期:2020-03-05
  • A Privacy-preserving Method to Optimize Distributed Resource Allocation
    arXiv.cs.MA Pub Date : 2019-08-07
    Olivier Beaude; Pascal Benchimol; Stéphane Gaubert; Paulin Jacquot; Nadia Oudjane

    We consider a resource allocation problem involving a large number of agents with individual constraints subject to privacy, and a central operator whose objective is to optimize a global, possibly nonconvex, cost while satisfying the agents' constraints, for instance an energy operator in charge of the management of energy consumption flexibilities of many individual consumers. We provide a privacy-preserving

    更新日期:2020-03-05
  • Hierarchical Deep Double Q-Routing
    arXiv.cs.MA Pub Date : 2019-10-09
    Ramy E. Ali; Bilgehan Erman; Ejder Baştuğ; Bruce Cilli

    This paper explores a deep reinforcement learning approach applied to the packet routing problem with high-dimensional constraints instigated by dynamic and autonomous communication networks. Our approach is motivated by the fact that centralized path calculation approaches are often not scalable, whereas the distributed approaches with locally acting nodes are not fully aware of the end-to-end performance

    更新日期:2020-03-05
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