1,588 research outputs found

    Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space

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    Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic. We propose an approach that allows detection of collisions even between continuous, stochastic trajectories with the only restriction that means and variances can be computed. To this end, we employ probabilistic bounds to derive criterion functions whose negative sign provably is indicative of probable collisions. For criterion functions that are Lipschitz, an algorithm is provided to rapidly find negative values or prove their absence. We propose an iterative policy-search approach that avoids prior discretisations and yields collision-free trajectories with adjustably high certainty. We test our method with both fixed-priority and auction-based protocols for coordinating the iterative planning process. Results are provided in collision-avoidance simulations of feedback controlled plants.Comment: This preprint is an extended version of a conference paper that is to appear in \textit{Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014)

    Leveraging repeated games for solving complex multiagent decision problems

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    Prendre de bonnes décisions dans des environnements multiagents est une tâche difficile dans la mesure où la présence de plusieurs décideurs implique des conflits d'intérêts, un manque de coordination, et une multiplicité de décisions possibles. Si de plus, les décideurs interagissent successivement à travers le temps, ils doivent non seulement décider ce qu'il faut faire actuellement, mais aussi comment leurs décisions actuelles peuvent affecter le comportement des autres dans le futur. La théorie des jeux est un outil mathématique qui vise à modéliser ce type d'interactions via des jeux stratégiques à plusieurs joueurs. Des lors, les problèmes de décision multiagent sont souvent étudiés en utilisant la théorie des jeux. Dans ce contexte, et si on se restreint aux jeux dynamiques, les problèmes de décision multiagent complexes peuvent être approchés de façon algorithmique. La contribution de cette thèse est triple. Premièrement, elle contribue à un cadre algorithmique pour la planification distribuée dans les jeux dynamiques non-coopératifs. La multiplicité des plans possibles est à l'origine de graves complications pour toute approche de planification. Nous proposons une nouvelle approche basée sur la notion d'apprentissage dans les jeux répétés. Une telle approche permet de surmonter lesdites complications par le biais de la communication entre les joueurs. Nous proposons ensuite un algorithme d'apprentissage pour les jeux répétés en ``self-play''. Notre algorithme permet aux joueurs de converger, dans les jeux répétés initialement inconnus, vers un comportement conjoint optimal dans un certain sens bien défini, et ce, sans aucune communication entre les joueurs. Finalement, nous proposons une famille d'algorithmes de résolution approximative des jeux dynamiques et d'extraction des stratégies des joueurs. Dans ce contexte, nous proposons tout d'abord une méthode pour calculer un sous-ensemble non vide des équilibres approximatifs parfaits en sous-jeu dans les jeux répétés. Nous montrons ensuite comment nous pouvons étendre cette méthode pour approximer tous les équilibres parfaits en sous-jeu dans les jeux répétés, et aussi résoudre des jeux dynamiques plus complexes.Making good decisions in multiagent environments is a hard problem in the sense that the presence of several decision makers implies conflicts of interests, a lack of coordination, and a multiplicity of possible decisions. If, then, the same decision makers interact continuously through time, they have to decide not only what to do in the present, but also how their present decisions may affect the behavior of the others in the future. Game theory is a mathematical tool that aims to model such interactions as strategic games of multiple players. Therefore, multiagent decision problems are often studied using game theory. In this context, and being restricted to dynamic games, complex multiagent decision problems can be algorithmically approached. The contribution of this thesis is three-fold. First, this thesis contributes an algorithmic framework for distributed planning in non-cooperative dynamic games. The multiplicity of possible plans is a matter of serious complications for any planning approach. We propose a novel approach based on the concept of learning in repeated games. Our approach permits overcoming the aforementioned complications by means of communication between players. We then propose a learning algorithm for repeated game self-play. Our algorithm allows players to converge, in an initially unknown repeated game, to a joint behavior optimal in a certain, well-defined sense, without communication between players. Finally, we propose a family of algorithms for approximately solving dynamic games, and for extracting equilibrium strategy profiles. In this context, we first propose a method to compute a nonempty subset of approximate subgame-perfect equilibria in repeated games. We then demonstrate how to extend this method for approximating all subgame-perfect equilibria in repeated games, and also for solving more complex dynamic games

    From supply chains to demand networks. Agents in retailing: the electrical bazaar

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    A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

    Knowledge-Based Task Structure Planning for an Information Gathering Agent

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    An effective solution to model and apply planning domain knowledge for deliberation and action in probabilistic, agent-oriented control is presented. Specifically, the addition of a task structure planning component and supporting components to an agent-oriented architecture and agent implementation is described. For agent control in risky or uncertain environments, an approach and method of goal reduction to task plan sets and schedules of action is presented. Additionally, some issues related to component-wise, situation-dependent control of a task planning agent that schedules its tasks separately from planning them are motivated and discussed

    Self-motivated agents that learn

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    Tese de mestrado em Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013We propose an architecture for the creation of agents with the capacity to learn how to act autonomously, from their interactions with the environment. Predefined solutions such as manually specified behaviors, goals or rewards are avoided in order to maximize autonomous adaptation to unforeseen conditions. We use internal needs to motivate agents to act in an attempt to fulfil them. As a consequence of its interactions with the environment, agents make observations which are used to formulate hypotheses and discover the rules that govern the relationship between the agents actions and their consequences. These rules are then used as criteria in the decision making process. Thus, agents behaviors depend on previous interactions and evolve with experience. We started by proposing a single agent architecture and created simple agents defined by sensors, needs and actuators. These agents adapted autonomously to the environment by discovering behaviors which fulfilled their needs. The single agent approach did not scale well neither allowed the satisfaction of multiple needs simultaneously. In order to face these shortcomings we propose a multiagent architecture which solves the scalability problem found in the single agent approach and offers the capacity to fulfil several needs simultaneously

    Multi-Agent Systems

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    A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

    Multi-platform coordination and resource management in command and control

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    Depuis plusieurs années, nous constatons l'augmentation de l'utilisation des techniques d'agents et multiagent pour assister l'humain dans ses tâches. Ce travail de maîtrise se situe dans la même voie. Précisément, nous proposons d'utiliser les techniques multiagent de planification et de coordination pour la gestion de ressources dans les systèmes de commande et contrôle (C2) temps réel. Le problème particulier que nous avons étudié est la conception d'un système d'aide à la décision pour les opérations anti-aérienne sur les frégates canadiennes. Dans le cas où plusieurs frégates doivent se défendre contre des menaces, la coordination est un problème d'importance capitale. L'utilisation de mécanismes de coordination efficaces permet d'éviter les actions conflictuelles et la redondance dans les engagements. Dans ce mémoire, nous présentons quatre mécanismes de coordination basés sur le partage de tâche. Trois sont basés sur les communications : la coordination centrale, le Contract Net, la coordination similaire à celle proposée par Brown; tandis que la défense de zone est basée sur les lois sociales. Nous exposons enfin les résultats auxquels nous sommes arrivés en simulant ces différents mécanismes.The use of agent and multiagent techniques to assist humans in their daily routines has been increasing for many years, notably in Command and Control (C2) systems. This thesis is is situated in this domain. Precisely, we propose to use multiagent planning and coordination techniques for resource management in real-time \acs{C2} systems. The particular problem we studied is the design of a decision-support for anti-air warfare on Canadian frigates. In the case of several frigates defending against incoming threats, multiagent coordination is a complex problem of capital importance. Better coordination mechanisms are important to avoid redundancy in engagements and inefficient defence caused by conflicting actions. In this thesis, we present four different coordination mechanisms based on task sharing. Three of these mechanisms are based on communications: central coordination, Contract Net coordination and Brown coordination, while the zone defence coordination is based on social laws. Finally, we expose the results obtained while simulating these various mechanisms

    Resilience, reliability, and coordination in autonomous multi-agent systems

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    Acknowledgements The research reported in this paper was funded and supported by various grants over the years: Robotics and AI in Nuclear (RAIN) Hub (EP/R026084/1); Future AI and Robotics for Space (FAIR-SPACE) Hub (EP/R026092/1); Offshore Robotics for Certification of Assets (ORCA) Hub (EP/R026173/1); the Royal Academy of Engineering under the Chair in Emerging Technologies scheme; Trustworthy Autonomous Systems “Verifiability Node” (EP/V026801); Scrutable Autonomous Systems (EP/J012084/1); Supporting Security Policy with Effective Digital Intervention (EP/P011829/1); The International Technology Alliance in Network and Information Sciences.Peer reviewedPostprin
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