• arXiv.cs.MA Pub Date : 2020-11-30
Niko A. Grupen; Daniel D. Lee; Bart Selman

In this work, we study emergent communication through the lens of cooperative multi-agent behavior in nature. Using insights from animal communication, we propose a spectrum from low-bandwidth (e.g. pheromone trails) to high-bandwidth (e.g. compositional language) communication that is based on the cognitive, perceptual, and behavioral capabilities of social agents. Through a series of experiments

更新日期：2020-12-01
• arXiv.cs.MA Pub Date : 2020-11-29
Vida Fathi; Jalal Arabneydi; Amir G. Aghdam

In this paper, we study the global convergence of model-based and model-free policy gradient descent and natural policy gradient descent algorithms for linear quadratic deep structured teams. In such systems, agents are partitioned into a few sub-populations wherein the agents in each sub-population are coupled in the dynamics and cost function through a set of linear regressions of the states and

更新日期：2020-12-01
• arXiv.cs.MA Pub Date : 2020-11-29
Changxi Zhu; Ho-fung Leung; Shuyue Hu; Yi Cai

In teacher-student framework, a more experienced agent (teacher) helps accelerate the learning of another agent (student) by suggesting actions to take in certain states. In cooperative multiagent reinforcement learning (MARL), where agents need to cooperate with one another, a student may fail to cooperate well with others even by following the teachers' suggested actions, as the polices of all agents

更新日期：2020-12-01
• arXiv.cs.MA Pub Date : 2020-11-28
Karena X. Cai; Tung Phan-Minh; Soon-Jo Chung; Richard M. Murray

The ability to guarantee safety and progress for all vehicles is vital to the success of the autonomous vehicle industry. We present a framework for the distributed control of autonomous vehicles that is safe and guarantees progress for all agents. In this paper, we first introduce a new game paradigm which we term the quasi-simultaneous discrete-time game. We then define an Agent Protocol agents must

更新日期：2020-12-01
• arXiv.cs.MA Pub Date : 2020-11-27
Elise Beck; Julie Dugdale; Carole Adam; Christelle Gaïdatzis; Julius Bañgate

How should computer science and social science collaborate to build a common model? How should they proceed to gather data that is really useful to the modelling? How can they design a survey that is tailored to the target model? This paper aims to answer those crucial questions in the framework of a multidisciplinary research project. This research addresses the issue of co-constructing a model when

更新日期：2020-12-01
• arXiv.cs.MA Pub Date : 2020-11-26
Qingbiao Li; Weizhe Lin; Zhe Liu; Amanda Prorok

The domains of transport and logistics are increasingly relying on autonomous mobile robots for the handling and distribution of passengers or resources. At large system scales, finding decentralized path planning and coordination solutions is key to efficient system performance. Recently, Graph Neural Networks (GNNs) have become popular due to their ability to learn communication policies in decentralized

更新日期：2020-12-01
• arXiv.cs.MA Pub Date : 2020-11-25
Rafał Kucharski; Oded Cats

Two-sided mobility platforms, such as Uber and Lyft, widely emerged in the urban mobility landscape, bringing disruptive changes to transportation systems worldwide. This calls for a simulation framework where researchers from various and across disciplines may introduce models aimed at representing the dynamics of platform-driven urban mobility systems. In this work, we present MaaSSim, an agent-based

更新日期：2020-11-27
• arXiv.cs.MA Pub Date : 2020-11-25
Weizhi Du; Harvey Tian

Optimization of resource distribution has been a challenging topic in current society. To explore this topic, we develop a Coalition Control Model(CCM) based on the Model Predictive Control(MPC) and test it using a fishing model with linear parameters. The fishing model focuses on the problem of distributing fishing fleets in certain regions to maximize fish caught using either exhaustive or heuristic

更新日期：2020-11-27
• arXiv.cs.MA Pub Date : 2020-11-25
Rafał Kucharski; Oded Cats; Julian Sienkiewicz

Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the

更新日期：2020-11-27
• arXiv.cs.MA Pub Date : 2020-11-25
Beatriz A. Asfora; Jacopo Banfi; Mark Campbell

In this letter, we consider the Multi-Robot Efficient Search Path Planning (MESPP) problem, where a team of robots is deployed in a graph-represented environment to capture a moving target within a given deadline. We prove this problem to be NP-hard, and present the first set of Mixed-Integer Linear Programming (MILP) models to tackle the MESPP problem. Our models are the first to encompass multiple

更新日期：2020-11-27
• arXiv.cs.MA Pub Date : 2020-11-24
Dong Chen; Zhaojian Li; Tianshu Chu; Rui Yao; Feng Qiu; Kaixiang Lin

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet

更新日期：2020-11-27
• arXiv.cs.MA Pub Date : 2020-11-24
Alexis Poulhes; Paul Mirial

In leisure spaces, particularly theme parks and museums, researchers and managers have long been using simulation tools to tackle the big issue associated with attractiveness, flow management. In this research, we present the management and planning perspective of a multi-agent simulation tool which models the behavior of skiers in a ski-area. This is the first tool able to simulate and compare management

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-23
Robert Bredereck; Andrzej Kaczmarczyk; Rolf Niedermeier

Finding an envy-free allocation of indivisible resources to agents is a central task in many multiagent systems. Often, non-trivial envy-free allocations do not exist, and, when they do, finding them can be computationally hard. Classical envy-freeness requires that every agent likes the resources allocated to it at least as much as the resources allocated to any other agent. In many situations this

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-23
Shridhar Velhal; Suresh Sundaram

This paper addresses the problem of restricted airspace protection from invaders using the cooperative multi-UAV system. The objective is to detect and capture the invaders cooperatively by a team of homogeneous UAVs (called evaders)before invaders enter the restricted airspace. The problem of restricted airspace protection problem is formulated as a Multi-UAV Spatio-Temporal Multi-Task Allocation

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-20
Kelvin K. F. Li; Stephen A. Jarvis; Fayyaz Minhas

COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11th, 2020. With half of the world's countries in lockdown as of April due to this pandemic, monitoring and understanding the spread of the virus and infection rates and how these factors relate to behavioural and societal parameters is crucial for effective policy making. This paper aims to investigate the effectiveness

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-22
Zhenyu Shou; Xuan Di

This paper aims to develop a unified paradigm that models one's learning behavior and the system's equilibrating processes in a routing game among atomic selfish agents. Such a paradigm can assist policymakers in devising optimal operational and planning countermeasures under both normal and abnormal circumstances. To this end, a multi-agent reinforcement learning (MARL) paradigm is proposed in which

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-21
Miklos BorsiUniversity of Bristol

Modern financial market dynamics warrant detailed analysis due to their significant impact on the world. This, however, often proves intractable; massive numbers of agents, strategies and their change over time in reaction to each other leads to difficulties in both theoretical and simulational approaches. Notable work has been done on strategy dominance in stock markets with respect to the ratios

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-21
Avik Pal; Jonah Philion; Yuan-Hong Liao; Sanja Fidler

For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow. "Road rules" include rules that drivers are required to follow by law -- such as the requirement that vehicles stop at red lights -- as well as more subtle social rules -- such as the implicit designation of fast lanes on the highway

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-21

We study the problem of learning-based attacks in linear systems, where the communication channel between the controller and the plant can be hijacked by a malicious attacker. We assume the attacker learns the dynamics of the system from observations, then overrides the controller's actuation signal, while mimicking legitimate operation by providing fictitious sensor readings to the controller. On

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-21
Bolin Gao; Lacra Pavel

In this paper, we provide exponential rates of convergence to the Nash equilibrium of continuous-time game dynamics such as mirror descent (MD) and actor-critic (AC) in $N$-player continuous games that are either potential games or monotone games but possibly potential-free. In the first part of this paper, under the assumption the game admits a relatively strongly concave potential, we show that MD

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-20
James Z. Hare; Cesar A. Uribe; Lance Kaplan; Ali Jadbabaie

This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations

更新日期：2020-11-25
• arXiv.cs.MA Pub Date : 2020-11-19
Yuxiao Chen; Ugo Rosolia; Aaron D. Ames

We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) framework consisting of a task allocation module and a localized path planning module. Each task is modeled as a Markov Decision Process (MDP) or a Mixed Observed Markov Decision Process (MOMDP) depending on whether full states

更新日期：2020-11-21
• arXiv.cs.MA Pub Date : 2020-11-19
Yujie Tang; Zhaolin Ren; Na Li

We consider a class of multi-agent optimization problems, where each agent is associated with an action vector and a local cost, and the goal is to cooperatively find the joint action profile that minimizes the average of the local costs. Such problems arise in many applications, such as distributed routing control, wind farm operation, etc. In many of these problems, gradient information may not be

更新日期：2020-11-21
• arXiv.cs.MA Pub Date : 2020-11-18

Braess' paradox has been shown to appear rather generically in many systems of transport on networks. It is especially relevant for vehicular traffic where it shows that in certain situations building a new road in an urban or highway network can lead to increased average travel times for all users. Here we address the question whether this changes if the drivers (agents) have access to traffic information

更新日期：2020-11-21
• arXiv.cs.MA Pub Date : 2020-11-17
Yanumula V. Karteek; Indrani Kar; Somanath Majhi

This paper proposes two algorithms, namely "back-tracking" and "history following", to reach consensus in case of communication loss for a network of distributed agents with switching topologies. To reach consensus in distributed control, considered communication topology forms a strongly connected graph. The graph is no more strongly connected whenever an agent loses communication.Whenever an agent

更新日期：2020-11-21
• arXiv.cs.MA Pub Date : 2020-11-18
Karl Tuyls; Shayegan Omidshafiei; Paul Muller; Zhe Wang; Jerome Connor; Daniel Hennes; Ian Graham; William Spearman; Tim Waskett; Dafydd Steele; Pauline Luc; Adria Recasens; Alexandre Galashov; Gregory Thornton; Romuald Elie; Pablo Sprechmann; Pol Moreno; Kris Cao; Marta Garnelo; Praneet Dutta; Michal Valko; Nicolas Heess; Alex Bridgland; Julien Perolat; Bart De Vylder; Ali Eslami; Mark Rowland; Andrew

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the

更新日期：2020-11-19
• arXiv.cs.MA Pub Date : 2020-11-17
Dror Dayan; Kiril Solovey; Marco Pavone; Dan Halperin

An underlying structure in several sampling-based methods for continuous multi-robot motion planning (MRMP) is the tensor roadmap (TR), which emerges from combining multiple PRM graphs constructed for the individual robots via a tensor product. We study the conditions under which the TR encodes a near-optimal solution for MRMP---satisfying these conditions implies near optimality for a variety of popular

更新日期：2020-11-19
• arXiv.cs.MA Pub Date : 2020-11-17
Mohammed Sharafath Abdul Hameed; Md Muzahid Khan; Andreas Schwung

This paper introduces a novel combination of scheduling control on a flexible robot manufacturing cell with curiosity based reinforcement learning. Reinforcement learning has proved to be highly successful in solving tasks like robotics and scheduling. But this requires hand tuning of rewards in problem domains like robotics and scheduling even where the solution is not obvious. To this end, we apply

更新日期：2020-11-18
• arXiv.cs.MA Pub Date : 2020-10-22
Jaber Valinejad; Lamine Mili; Natalie van der Wal

In the literature, smart grids are modeled as cyber-physical power systems without considering the computational social aspects. However, end-users are playing a key role in their operation and response to disturbances via demand response and distributed energy resources. Therefore, due to the critical role of active and passive end-users and the intermittency of renewable energy, smart grids must

更新日期：2020-11-17
• arXiv.cs.MA Pub Date : 2020-11-16
Christopher D. Hsu; Heejin Jeong; George J. Pappas; Pratik Chaudhari

This paper develops a stochastic Multi-Agent Reinforcement Learning (MARL) method to learn control policies that can handle an arbitrary number of external agents; our policies can be executed for tasks consisting of 1000 pursuers and 1000 evaders. We model pursuers as agents with limited on-board sensing and formulate the problem as a decentralized, partially-observable Markov Decision Process. An

更新日期：2020-11-17
• arXiv.cs.MA Pub Date : 2020-11-16
Panayiotis Danassis; Aleksei Triastcyn; Boi Faltings

When it comes to large-scale multi-agent systems with a diverse set of agents, traditional differential privacy (DP) mechanisms are ill-matched because they consider a very broad class of adversaries, and they protect all users, independent of their characteristics, by the same guarantee. Achieving a meaningful privacy leads to pronounced reduction in solution quality. Such assumptions are unnecessary

更新日期：2020-11-17
• arXiv.cs.MA Pub Date : 2020-11-16
Wojciech Jamroga; David Mestel; Peter B. Roenne; Peter Y. A. Ryan; Marjan Skrobot

The COVID-19 pandemic has influenced virtually all aspects of our lives. Across the world, countries have applied various mitigation strategies for the epidemic, based on social, political, and technological instruments. We postulate that one should {identify the relevant requirements} before committing to a particular mitigation strategy. One way to achieve it is through an overview of what is considered

更新日期：2020-11-17
• arXiv.cs.MA Pub Date : 2020-11-14
Roxana Rădulescu; Timothy Verstraeten; Yijie Zhang; Patrick Mannion; Diederik M. Roijers; Ann Nowé

Many real-world multi-agent interactions consider multiple distinct criteria, i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may lead to different utilities for each participant. Therefore, it is essential for an agent to learn about the behaviour of other agents in the system. In this work, we present the first study of the effects of such opponent

更新日期：2020-11-17
• arXiv.cs.MA Pub Date : 2020-11-16
Simon Oh; Daniel Kondor; Ravi Seshadri; Meng Zhou; Diem-Trinh Le; Moshe Ben-Akiva

The emergence of ride-sourcing platforms has brought an innovative alternative in transportation, radically changed travel behaviors, and suggested new directions for transportation planners and operators. This paper provides an exploratory analysis on the operations of a ride-sourcing service using large-scale data on service performance. Observations over multiple days in Singapore suggest reproducible

更新日期：2020-11-17
• arXiv.cs.MA Pub Date : 2020-09-23

This paper introduces a blazingly fast, no-loss expert system for Tic Tac Toe using Decision Trees called T3DT, that tries to emulate human gameplay as closely as possible. It does not make use of any brute force, minimax or evolutionary techniques, but is still always unbeatable. In order to make the gameplay more human-like, randomization is prioritized and T3DT randomly chooses one of the multiple

更新日期：2020-11-16
• arXiv.cs.MA Pub Date : 2020-11-11
Matthew ZalesakCornell University; Samitha SamaranayakeCornell University

We introduce a Python package for modeling and studying the spread of infectious diseases using an agent-based SEIR style epidemiological model with a focus on university campuses. This document explains the epidemiological model used in the package and gives examples highlighting the ways that the package can be used.

更新日期：2020-11-13
• arXiv.cs.MA Pub Date : 2020-11-11
Forrest Laine; David Fridovich-Keil; Chih-Yuan Chiu; Claire Tomlin

We present a novel method for handling uncertainty about the intentions of non-ego players in dynamic games, with application to motion planning for autonomous vehicles. Equilibria in these games explicitly account for interaction among other agents in the environment, such as drivers and pedestrians. Our method models the uncertainty about the intention of other agents by constructing multiple hypotheses

更新日期：2020-11-13
• arXiv.cs.MA Pub Date : 2020-11-11
Sivanathan Kandhasamy; Vinayagam Babu Kuppusamy; Tanmay Vilas Samak; Chinmay Vilas Samak

This work presents a decentralized motion planning framework for addressing the task of multi-robot navigation using deep reinforcement learning. A custom simulator was developed in order to experimentally investigate the navigation problem of 4 cooperative non-holonomic robots sharing limited state information with each other in 3 different settings. The notion of decentralized motion planning with

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-10
Arthur Bucker; Rogerio Bonatti; Sebastian Scherer

Aerial cinematography is significantly expanding the capabilities of film-makers. Recent progress in autonomous unmanned aerial vehicles (UAVs) has further increased the potential impact of aerial cameras, with systems that can safely track actors in unstructured cluttered environments. Professional productions, however, require the use of multiple cameras simultaneously to record different viewpoints

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-10
Bowen Baker

Multi-agent reinforcement learning (MARL) has shown recent success in increasingly complex fixed-team zero-sum environments. However, the real world is not zero-sum nor does it have fixed teams; humans face numerous social dilemmas and must learn when to cooperate and when to compete. To successfully deploy agents into the human world, it may be important that they be able to understand and help in

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-10-27
Sidhdharth Sikka

Autonomous spacecraft maneuver planning using an evolutionary algorithmic approach is investigated. Simulated spacecraft were placed into four different initial orbits. Each was allowed a string of thirty delta-v impulse maneuvers in six cartesian directions, the positive and negative x, y and z directions. The goal of the spacecraft maneuver string was to, starting from some non-polar starting orbit

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-10
Simon Oh; Antonis F. Lentzakis; Ravi Seshadri; Moshe Ben-Akiva

Technological advancements have brought increasing attention to Automated Mobility on Demand (AMOD) as a promising solution that may improve future urban mobility. During the last decade, extensive research has been conducted on the design and evaluation of AMOD systems using simulation models. This paper adds to this growing body of literature by investigating the network impacts of AMOD through high-fidelity

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-07
Dayong Ye; Tianqing Zhu; Zishuo Cheng; Wanlei Zhou; Philip S. Yu

Agent advising is one of the main approaches to improve agent learning performance by enabling agents to share advice. Existing advising methods have a common limitation that an adviser agent can offer advice to an advisee agent only if the advice is created in the same state as the advisee's concerned state. However, in complex environments, it is a very strong requirement that two states are the

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-09
Sushmita Bhattacharya; Siva Kailas; Sahil Badyal; Stephanie Gil; Dimitri Bertsekas

In this paper we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, partial state observations, and a multiagent structure. We discuss and compare algorithms that simultaneously or sequentially optimize the agents' controls by using multistep lookahead, truncated rollout with a known base policy, and a terminal cost function approximation. Our methods

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-08
Priyank Srivastava; Jorge Cortes

This paper proposes a distributed algorithm for a network of agents to solve an optimization problem with separable objective function and locally coupled constraints. Our strategy is based on reformulating the original constrained problem as the unconstrained optimization of a smooth (continuously differentiable) exact penalty function. Computing the gradient of this penalty function in a distributed

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-08
Yun Chang; Yulun Tian; Jonathan P. How; Luca Carlone

We present the first fully distributed multi-robot system for dense metric-semantic Simultaneous Localization and Mapping (SLAM). Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors, and builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label (e.g., building, road, objects). In Kimera-Multi

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-08
Qing Jiao; Yushan Li; Jianping He; Ling Shi

Topology inference is a crucial problem for cooperative control in multi-agent systems. Different from most prior works, this paper is dedicated to inferring the directed network topology from the observations that consist of a single, noisy and finite time-series system trajectory, where the cooperation dynamics is stimulated with the initial network state and the unmeasurable latent input. The unmeasurable

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-08
Sébastien Bubeck; Thomas Budzinski; Mark Sellke

We consider the cooperative multi-player version of the stochastic multi-armed bandit problem. We study the regime where the players cannot communicate but have access to shared randomness. In prior work by the first two authors, a strategy for this regime was constructed for two players and three arms, with regret $\tilde{O}(\sqrt{T})$, and with no collisions at all between the players (with very

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-08
Junha Roh; Christoforos Mavrogiannis; Rishabh Madan; Dieter Fox; Siddhartha S. Srinivasa

We focus on decentralized navigation among multiple non-communicating rational agents at \emph{uncontrolled} intersections, i.e., street intersections without traffic signs or signals. Avoiding collisions in such domains relies on the ability of agents to predict each others' intentions reliably, and react quickly. Multiagent trajectory prediction is NP-hard whereas the sample complexity of existing

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-07
Mashnoon Islam; Touhid Ahmed; Abu Tammam Bin Nuruddin; Mashuda Islam; Shahnewaz Siddique

The application of autonomous mobile robots in robotic security platforms is becoming a promising field of innovation due to their adaptive capability of responding to potential disturbances perceived through a wide range of sensors. Researchers have proposed systems that either focus on utilizing a single mobile robot or a system of cooperative multiple robots. However, very few of the proposed works

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-07
Dimitar P. Guelev

We propose enhancing the use of propositions for denoting decisions and strategies as established in temporal languages such as CTL*, if interpreted on concurrent game models. The enhancement enables specifying varying coalition structure. In quantified CTL* this technique also enables quantifying over coalition structure, and we use it to quantify over an extended form of strategy profiles which capture

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-07
Grant Schoenebeck; Chenkai Yu; Fang-Yi Yu

Prediction markets are powerful tools to elicit and aggregate beliefs from strategic agents. However, in current prediction markets, agents may exhaust the social welfare by competing to be the first to update the market. We initiate the study of the trade-off between how quickly information is aggregated by the market, and how much this information costs. We design markets to aggregate timely information

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-06
Kiril Solovey; Saptarshi Bandyopadhyay; Federico Rossi; Michael T. Wolf; Marco Pavone

Multi-robot systems are uniquely well-suited to performing complex tasks such as patrolling and tracking, information gathering, and pick-up and delivery problems, offering significantly higher performance than single-robot systems. A fundamental building block in most multi-robot systems is task allocation: assigning robots to tasks (e.g., patrolling an area, or servicing a transportation request)

更新日期：2020-11-12
• arXiv.cs.MA Pub Date : 2020-11-06
Jun Ma; Zilong Cheng; Xiaoxue Zhang; Abdullah Al Mamun; Clarence W. de Silva; Tong Heng Lee

In the recent literature, significant and substantial efforts have been dedicated to the important area of multi-agent decision-making problems. Particularly here, the model predictive control (MPC) methodology has demonstrated its effectiveness in various applications, such as mobile robots, unmanned vehicles, and drones. Nevertheless, in many specific scenarios involving the MPC methodology, accurate

更新日期：2020-11-09
• arXiv.cs.MA Pub Date : 2020-11-05
Ben Abramowitz; Ehud Shapiro; Nimrod Talmon

A self-governed community or society must have rules by which group decisions are made. These rules are often codified by a written constitution that specifies not only the rules by which decisions are made, but also the means by which these rules can be changed, or amended. One of the defining characteristics of constitutions is entrenchment, or the difficulty of enacting changes. Too little entrenchment

更新日期：2020-11-09
• arXiv.cs.MA Pub Date : 2020-11-05

Connected and automated vehicles (CAVs) can alleviate traffic congestion, air pollution, and improve safety. In this paper, we provide a decentralized coordination framework for CAVs at a signal-free intersection to minimize travel time and improve fuel efficiency. We employ a simple yet powerful reinforcement learning approach, an off-policy temporal difference learning called Q-learning, enhanced

更新日期：2020-11-09
• arXiv.cs.MA Pub Date : 2020-11-05
Huy Ha; Jingxi Xu; Shuran Song

We present a closed-loop multi-arm motion planner that is scalable and flexible with team size. Traditional multi-arm robot systems have relied on centralized motion planners, whose runtimes often scale exponentially with team size, and thus, fail to handle dynamic environments with open-loop control. In this paper, we tackle this problem with multi-agent reinforcement learning, where a decentralized

更新日期：2020-11-06
• arXiv.cs.MA Pub Date : 2020-11-04
Kshitij Kulkarni; Sven Neth

We study group decision making with changing preferences as a Markov Decision Process. We are motivated by the increasing prevalence of automated decision-making systems when making choices for groups of people over time. Our main contribution is to show how classic representation theorems from social choice theory can be adapted to characterize optimal policies in this dynamic setting. We provide

更新日期：2020-11-06
• arXiv.cs.MA Pub Date : 2020-11-04
Suman Ojha; Jonathan Vitale; Mary-Anne Williams

This paper presents the computational details of our emotion model, EEGS, and also provides an overview of a three-stage validation methodology used for the evaluation of our model, which can also be applicable for other computational models of emotion. A major gap in existing emotion modelling literature has been the lack of computational/technical details of the implemented models, which not only

更新日期：2020-11-06
• arXiv.cs.MA Pub Date : 2020-11-04
Mathieu Even; Hadrien Hendrikx; Laurent Massoulié

This paper considers the minimization of a sum of smooth and strongly convex functions dispatched over the nodes of a communication network. Previous works on the subject either focus on synchronous algorithms, which can be heavily slowed down by a few slow nodes (the \emph{straggler problem}), or consider a historical asynchronous setting (Boyd et al., 2006), which relies on a communication model

更新日期：2020-11-05
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