Human-Agent Decision-making: Combining Theory and Practice

Extensive work has been conducted both in game theory and logic to model strategic interaction. An important question is whether we can use these theories to design agents for interacting with people? On the one hand, they provide a formal design specification for agent strategies. On the other hand, people do not necessarily adhere to playing in accordance with these strategies, and their behavior is affected by a multitude of social and psychological factors. In this paper we will consider the question of whether strategies implied by theories of strategic behavior can be used by automated agents that interact proficiently with people. We will focus on automated agents that we built that need to interact with people in two negotiation settings: bargaining and deliberation. For bargaining we will study game-theory based equilibrium agents and for argumentation we will discuss logic-based argumentation theory. We will also consider security games and persuasion games and will discuss the benefits of using equilibrium based agents.Comment: In Proceedings TARK 2015, arXiv:1606.0729

LSTM Path-Maker : une nouvelle stratégie pour la patrouille multiagent basée sur l'architecture LSTM

National audienceAbstract For over a decade, the multi-agent patrol task has received a growing attention from the multi-agent community due to its wide range of potential applications. However, the existing patrolling-specific algorithms based on deep learning algorithms are still in preliminary stages. In this paper, we propose to integrate a recurrent neural network as part of * Paper presented at the 52nd Hawaii International Conference on System Sciences (HICSS52 2019), titre, résumé et mots-clés en français ajou-tés. a multi-agent patrolling strategy. Hence we proposed a formal model of an LSTM-based agent strategy named LSTM Path Maker. The LSTM network is trained over simulation traces of a coordinated strategy, then embedded on each agent of the new strategy to patrol efficiently without communicating. Finally this new LSTM-based strategy is evaluated in simulation and compared with two representative strategies : a coordinated one and a reactive one. Preliminary results indicate that the proposed strategy is better than the reactive.Depuis plus d'une décennie, la tâche de la patrouille mul-tiagent a attiré l'attention de la communauté multiagent de manière croissante, en raison de son grand nombre d'applications potentielles. Cependant, les algorithmes ba-sés sur des méthodes d'apprentissage profond pour traiter cette tâche sont à ce jour peu développés. Dans cet article, nous proposons d'intégrer un réseau de neurone récurrent à une stratégie de patrouille multiagent. Ce faisant, nous avons proposé un modèle formel de stratégie d'agent basée sur l'architecture LSTM, que nous avons nommé LSTM-Path-Maker. Le réseau LSTM est entraîné sur des traces de simulation d'une stratégie coordonnée et centralisée, puis embarqué dans chaque agent en vue de patrouiller effica-cement sans communication. Enfin, cette nouvelle stratégie basée sur l'architecture LSTM est évaluée en simulation et comparée d'une part à une stratégie coordonnée et d'autre part à une stratégie réactive. Les résultats préliminaires in-diquent que la stratégie proposée est meilleure que la stra-tégie réactive

訪問頻度を考慮したマルチエージェント巡回問題における公平なタスク分割法の提案

早大学位記番号:新7959早稲田大

Effective Cooperation and Scalability in Multi-Robot Teams for Automatic Patrolling of Infrastructures

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraIn the digital era that we live in, advances in technology have proliferated throughout our society, quickening the completion of tasks that were painful in the old days, improving solutions to the everyday problems that we face, and generally assisting human beings both in their professional and personal life. Robotics is a clear example of a broad technological field that evolves every day. In fact, scientists predict that in the upcoming few decades, robots will naturally interact and coexist alongside human beings. While it is true that robots already have a strong presence in industrial environments, e.g., robotic arms for manufacturing, the average person still looks upon robots with suspicion, since they are not acquainted by such type of technology. In this thesis, the author deploys teams of mobile robots in indoor scenarios to cooperatively perform patrolling missions, which represents an effort to bring robots closer to humans and assist them in monotonous or repetitive tasks, such as supervising and monitoring indoor infrastructures or simply cooperatively cleaning floors. In this context, the team of robots should be able to sense the environment, localize and navigate autonomously between way points while avoiding obstacles, incorporate any number of robots, communicate actions in a distributed way and being robust not only to agent failures but also communication failures, so as to effectively coordinate to achieve optimal collective performance. The referred capabilities are an evidence that such systems can only prove their reliability in real-world environments if robots are endowed with intelligence and autonomy. Thus, the author follows a line of research where patrolling units have the necessary tools for intelligent decision-making, according to the information of the mission, the environment and teammates' actions, using distributed coordination architectures. An incremental approach is followed. Firstly, the problem is presented and the literature is deeply studied in order to identify potential weaknesses and research opportunities, backing up the objectives and contributions proposed in this thesis. Then, problem fundamentals are described and benchmarking of multi-robot patrolling algorithms in realistic conditions is conducted. In these earlier stages, the role of different parameters of the problem, like environment connectivity, team size and strategy philosophy, will become evident through extensive empirical results and statistical analysis. In addition, scalability is deeply analyzed and tied with inter-robot interference and coordination, imposed by each patrolling strategy. After gaining sensibility to the problem, preliminary models for multi-robot patrol with special focus on real-world application are presented, using a Bayesian inspired formalism. Based on these, distributed strategies that lead to superior team performance are described. Interference between autonomous agents is explicitly dealt with, and the approaches are shown to scale to large teams of robots. Additionally, the robustness to agent and communication failures is demonstrated, as well as the flexibility of the model proposed. In fact, by later generalizing the model with learning agents and maintaining memory of past events, it is then shown that these capabilities can be inherited, while at the same time increasing team performance even further and fostering adaptability. This is verified in simulation experiments and real-world results in a large indoor scenario. Furthermore, since the issue of team scalability is highly in focus in this thesis, a method for estimating the optimal team size in a patrolling mission, according to the environment topology is proposed. Upper bounds for team performance prior to the mission start are provided, supporting the choice of the number of robots to be used so that temporal constraints can be satisfied. All methods developed in this thesis are tested and corroborated by experimental results, showing the usefulness of employing cooperative teams of robots in real-world environments and the potential for similar systems to emerge in our society.FCT - SFRH/BD/64426/200

The Socialization of Female Hostage Negotiators: Their Voices, Perspectives, & Experiences

In its fifth annual study, the National Center for Women and Policing reported that women continue to face widespread bias in police hiring and are under-represented because of biased selection practices and recruitment policies that keep their number artificially low. Once hired, women face discrimination, harassment, intimidation, and are maliciously thwarted as they move up the ranks. With respect to gender and organizational culture, the NCWP study failed to capture and describe the perceptions and socialization experiences of those who moved up into the specialized units, particularly female hostage negotiators. For this reason, the current study was designed to examine the lived experiences of 24 female hostage negotiators located in south Florida’s tri-county area. Through Moustakas’ transcendental phenomenological methodology, this investigation reveals and explains how women are socialized in hostage negotiations. The principal investigator used comprehensive descriptions and interpretation of the women’s experiences to highlight their socialization process. This investigation provides valuable insight about who these women really are, while providing a channel for their voices, their perceptions, and the feelings they experience as hostage negotiators, thereby proving valuable insight for selecting, training, and retaining future female hostage negotiators. Directions for future research as well as implications of the findings are offered

The Cowl - v.52 - n.17 - Sep 14, 1988

The Cowl - student newspaper of Providence College. Vol 52 - No. 17 - September 14, 1988. 16 pages