On the Effects of Omitting Information Exchange between Autonomous Resource Management Agents

We study the problem of information exchange between coordinated autonomous resource management agents. We limit information to that which can be directly observed by each agent. While exchanging all relevant observables leads to near optimal management, leaving out information leads to ”hidden variable” problems that affect only part of the overall behavior. The patterns observed for hidden variables in simulation predict what will happen in a realistic situation when not all information is interchanged. Through simulation, we observe that leaving out information results in non-optimal behavior of the resource management model when resource needs are decreasing, although the partial information model performs very well when resource needs are increasing

A Networking Framework for Multi-Robot Coordination

Autonomous robots operating in real environments need to be able to interact with a dynamic world populated with objects, people, and, in general, other agents. The current generation of autonomous robots, such as the ASIMO robot by Honda or the QRIO by Sony, has showed impressive performances in mechanics and control of movements; moreover, recent literature reports encouraging results about the capability of such robots of representing themselves with respect to a dynamic external world, of planning future actions and of evaluating resulting situations in order to make new plans. However, when multiple robots are supposed to operate together, coordination and communication issues arise; while noteworthy results have been achieved with respect to the control of a single robot, novel issues arise when the actions of a robot influence another''s behavior. The increase in computational power available to systems nowadays makes it feasible, and even convenient, to organize them into a single distributed computing environment in order to exploit the synergy among different entities. This is especially true for robot teams, where cooperation is supposed to be the most natural scheme of operation, especially when robots are required to operate in highly constrained scenarios, such as inhospitable sites, remote sites, or indoor environments where strict constraints on intrusiveness must be respected. In this case, computations will be inherently network-centric, and to solve the need for communication inside robot collectives, an efficient network infrastructure must be put into place; once a proper communication channel is established, multiple robots may benefit from the interaction with each other in order to achieve a common goal. The framework presented in this paper adopts a composite networking architecture, in which a hybrid wireless network, composed by commonly available WiFi devices, and the more recently developed wireless sensor networks, operates as a whole in order both to provide a communication backbone for the robots and to extract useful information from the environment. The ad-hoc WiFi backbone allows robots to exchange coordination information among themselves, while also carrying data measurements collected from surrounding environment, and useful for localization or mere data gathering purposes. The proposed framework is called RoboNet, and extends a previously developed robotic tour guide application (Chella et al., 2007) in the context of a multi-robot application; our system allows a team of robots to enhance their perceptive capabilities through coordination obtained via a hybrid communication network; moreover, the same infrastructure allows robots to exchange information so as to coordinate their actions in order to achieve a global common goal. The working scenario considered in this paper consists of a museum setting, where guided tours are to be automatically managed. The museum is arranged both chronologically and topographically, but the sequence of findings to be visited can be rearranged depending on user queries, making a sort of dynamic virtual labyrinth with various itineraries. Therefore, the robots are able to guide visitors both in prearranged tours and in interactive tours, built in itinere depending on the interaction with the visitor: robots are able to rebuild the virtual connection between findings and, consequently, the path to be followed. This paper is organized as follows. Section 2 contains some background on multi-robot coordination, and Section 3 describes the underlying ideas and the motivation behind the proposed architecture, whose details are presented in Sections 4, 5, and 6. A realistic application scenario is described in Section 7, and finally our conclusions are drawn in Section 8

The urban real-time traffic control (URTC) system : a study of designing the controller and its simulation

The growth of the number of automobiles on the roads in China has put higher demands on the traffic control system that needs to efficiently reduce the level of congestion occurrence, which increases travel delay, fuel consumption, and air pollution. The traffic control system, urban real-time traffic control system based on multi-agent (MA-URTC) is presented in this thesis. According to the present situation and the traffic's future development in China, the researches on intelligent traffic control strategy and simulation based on agent lays a foundation for the realization of the system. The thesis is organized as follows: The first part focuses on the intersection' real-time signal control strategy. It contains the limitations of current traffic control systems, application of artificial intelligence in the research, how to bring the dynamic traffic flow forecast into effect by combining the neural network with the genetic arithmetic, and traffic signal real-time control strategy based on fuzzy control. The author uses sorne simple simulation results to testify its superiority. We adopt the latest agent technology in designing the logical structure of the MA-URTC system. By exchanging traffic flows information among the relative agents, MA-URTC provides a new concept in urban traffic control. With a global coordination and cooperation on autonomy-based view of the traffic in cities, MA-URTC anticipates the congestion and control traffic flows. It is designed to support the real-time dynamic selection of intelligent traffic control strategy and the real-time communication requirements, together with a sufficient level of fault-tolerance. Due to the complexity and levity of urban traffic, none strategy can be universally applicable. The agent can independently choose the best scheme according to the real-time situation. To develop an advanced traffic simulation system it can be helpful for us to find the best scheme and the best switch-point of different schemes. Thus we can better deal with the different real-time traffic situations. The second part discusses the architecture and function of the intelligent traffic control simulation based on agent. Meanwhile the author discusses the design model of the vehicle-agent, road agent in traffic network and the intersection-agent so that we can better simulate the real-time environment. The vehicle-agent carries out the intelligent simulation based on the characteristics of the drivers in the actual traffic condition to avoid the disadvantage of the traditional traffic simulation system, simple-functioned algorithm of the vehicles model and unfeasible forecasting hypothesis. It improves the practicability of the whole simulation system greatly. The road agent's significance lies in its guidance of the traffic participants. It avoids the urban traffic control that depends on only the traffic signal control at intersection. It gives the traffic participants the most comfortable and direct guidance in traveling. It can also make a real-time and dynamic adjustment on the urban traffic flow, thus greatly lighten the pressure of signal control in intersection area. To sorne extent, the road agent is equal to the pre-caution mechanism. In the future, the construction of urban roads tends to be more intelligent. Therefore, the research on road agent is very important. All kinds of agents in MA-URTC are interconnected through a computer network. In the end, the author discusses the direction of future research. As the whole system is a multi-agent system, the intersection, the road and the vehicle belongs to multi-agent system respectively. So the emphasis should be put on the structure design and communication of all kinds of traffic agents in the system. Meanwhile, as an open and flexible real-time traffic control system, it is also concerned with how to collaborate with other related systems effectively, how to conform the resources and how to make the traffic participants anywhere throughout the city be in the best traffic guidance at all times and places. To actualize the genuine ITS will be our final goal. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Artificial Intelligence, Computer simulation, Fuzzy control, Genetic Algorithm, Intelligent traffic control, ITS, Multi-agent, Neural Network, Real-time

Cooperative planning in multi-agent systems

Tesis por compendio[EN] Automated planning is a centralized process in which a single planning entity, or agent, synthesizes a course of action, or plan, that satisfies a desired set of goals from an initial situation. A Multi-Agent System (MAS) is a distributed system where a group of autonomous agents pursue their own goals in a reactive, proactive and social way. Multi-Agent Planning (MAP) is a novel research field that emerges as the integration of automated planning in MAS. Agents are endowed with planning capabilities and their mission is to find a course of action that attains the goals of the MAP task. MAP generalizes the problem of automated planning in domains where several agents plan and act together by combining their knowledge, information and capabilities. In cooperative MAP, agents are assumed to be collaborative and work together towards the joint construction of a competent plan that solves a set of common goals. There exist different methods to address this objective, which vary according to the typology and coordination needs of the MAP task to solve; that is, to which extent agents are able to make their own local plans without affecting the activities of the other agents. The present PhD thesis focuses on the design, development and experimental evaluation of a general-purpose and domain-independent resolution framework that solves cooperative MAP tasks of different typology and complexity. More precisely, our model performs a multi-agent multi-heuristic search over a plan space. Agents make use of an embedded search engine based on forward-chaining Partial Order Planning to successively build refinement plans starting from an initial empty plan while they jointly explore a multi-agent search tree. All the reasoning processes, algorithms and coordination protocols are fully distributed among the planning agents and guarantee the preservation of the agents' private information. The multi-agent search is guided through the alternation of two state-based heuristic functions. These heuristic estimators use the global information on the MAP task instead of the local projections of the task of each agent. The experimental evaluation shows the effectiveness of our multi-heuristic search scheme, obtaining significant results in a wide variety of cooperative MAP tasks adapted from the benchmarks of the International Planning Competition.[ES] La planificación automática es un proceso centralizado en el que una única entidad de planificación, o agente, sintetiza un curso de acción, o plan, que satisface un conjunto deseado de objetivos a partir de una situación inicial. Un Sistema Multi-Agente (SMA) es un sistema distribuido en el que un grupo de agentes autónomos persiguen sus propias metas de forma reactiva, proactiva y social. La Planificación Multi-Agente (PMA) es un nuevo campo de investigación que surge de la integración de planificación automática en SMA. Los agentes disponen de capacidades de planificación y su propósito consiste en generar un curso de acción que alcance los objetivos de la tarea de PMA. La PMA generaliza el problema de planificación automática en dominios en los que diversos agentes planifican y actúan conjuntamente mediante la combinación de sus conocimientos, información y capacidades. En PMA cooperativa, se asume que los agentes son colaborativos y trabajan conjuntamente para la construcción de un plan competente que resuelva una serie de objetivos comunes. Existen distintos métodos para alcanzar este objetivo que varían de acuerdo a la tipología y las necesidades de coordinación de la tarea de PMA a resolver; esto es, hasta qué punto los agentes pueden generar sus propios planes locales sin afectar a las actividades de otros agentes. La presente tesis doctoral se centra en el diseño, desarrollo y evaluación experimental de una herramienta independiente del dominio y de propósito general para la resolución de tareas de PMA cooperativa de distinta tipología y nivel de complejidad. Particularmente, nuestro modelo realiza una búsqueda multi-agente y multi-heurística sobre el espacio de planes. Los agentes hacen uso de un motor de búsqueda embebido basado en Planificación de Orden Parcial de encadenamiento progresivo para generar planes refinamiento de forma sucesiva mientras exploran conjuntamente el árbol de búsqueda multiagente. Todos los procesos de razonamiento, algoritmos y protocolos de coordinación están totalmente distribuidos entre los agentes y garantizan la preservación de la información privada de los agentes. La búsqueda multi-agente se guía mediante la alternancia de dos funciones heurísticas basadas en estados. Estos estimadores heurísticos utilizan la información global de la tarea de PMA en lugar de las proyecciones locales de la tarea de cada agente. La evaluación experimental muestra la efectividad de nuestro esquema de búsqueda multi-heurístico, que obtiene resultados significativos en una amplia variedad de tareas de PMA cooperativa adaptadas a partir de los bancos de pruebas de las Competición Internacional de Planificación.[CA] La planificació automàtica és un procés centralitzat en el que una única entitat de planificació, o agent, sintetitza un curs d'acció, o pla, que satisfau un conjunt desitjat d'objectius a partir d'una situació inicial. Un Sistema Multi-Agent (SMA) és un sistema distribuït en el que un grup d'agents autònoms persegueixen les seues pròpies metes de forma reactiva, proactiva i social. La Planificació Multi-Agent (PMA) és un nou camp d'investigació que sorgeix de la integració de planificació automàtica en SMA. Els agents estan dotats de capacitats de planificació i el seu propòsit consisteix en generar un curs d'acció que aconseguisca els objectius de la tasca de PMA. La PMA generalitza el problema de planificació automàtica en dominis en què diversos agents planifiquen i actúen conjuntament mitjançant la combinació dels seus coneixements, informació i capacitats. En PMA cooperativa, s'assumeix que els agents són col·laboratius i treballen conjuntament per la construcció d'un pla competent que ressolga una sèrie d'objectius comuns. Existeixen diferents mètodes per assolir aquest objectiu que varien d'acord a la tipologia i les necessitats de coordinació de la tasca de PMA a ressoldre; és a dir, fins a quin punt els agents poden generar els seus propis plans locals sense afectar a les activitats d'altres agents. La present tesi doctoral es centra en el disseny, desenvolupament i avaluació experimental d'una ferramenta independent del domini i de propòsit general per la resolució de tasques de PMA cooperativa de diferent tipologia i nivell de complexitat. Particularment, el nostre model realitza una cerca multi-agent i multi-heuristica sobre l'espai de plans. Els agents fan ús d'un motor de cerca embegut en base a Planificació d'Ordre Parcial d'encadenament progressiu per generar plans de refinament de forma successiva mentre exploren conjuntament l'arbre de cerca multiagent. Tots els processos de raonament, algoritmes i protocols de coordinació estan totalment distribuïts entre els agents i garanteixen la preservació de la informació privada dels agents. La cerca multi-agent es guia mitjançant l'aternança de dues funcions heurístiques basades en estats. Aquests estimadors heurístics utilitzen la informació global de la tasca de PMA en lloc de les projeccions locals de la tasca de cada agent. L'avaluació experimental mostra l'efectivitat del nostre esquema de cerca multi-heurístic, que obté resultats significatius en una ampla varietat de tasques de PMA cooperativa adaptades a partir dels bancs de proves de la Competició Internacional de Planificació.Torreño Lerma, A. (2016). Cooperative planning in multi-agent systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65815TESISPremiadoCompendi

Ideological production in the era of global media capitalism

Acknowledging the recognition of the media as international actors, this article discusses their role in the process of ideological production in contemporary democracies. The interconnection between the global media industry and market configuration emerges as the structural link of this process, which determines the conditions of reproduction and dissemination of ideas and the construction of the reference frames that allow defining the positioning of votersconsumers. Following Downs’ theory, this paper examines the conceptual relationship between social positioning and political positioning, proposing the introduction of a third variable in Downs’ analysis, media positioning, in order to define and update its postulates. Finally, the stages, processes and outputs involved in the process of ideological construction in three different market configurations are identified. The conclusion is that there is a qualitative change in the action of global media, which have evolved from being informal actors in the democratic process to formal actors

Melting Pot 2.0

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a "substrate") with a reference set of co-players (a "background population"), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity. Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol. This report also contains: (1) details of all substrates and scenarios; (2) a complete description of all baseline algorithms and results. Our intention is for it to serve as a reference for researchers using Melting Pot 2.0.Comment: 59 pages, 54 figures. arXiv admin note: text overlap with arXiv:2107.0685

Role and task allocation framework for Multi-Robot Collaboration with latent knowledge estimation

In this work a novel framework for modeling role and task allocation in Cooperative Heterogeneous Multi-Robot Systems (CHMRSs) is presented. This framework encodes a CHMRS as a set of multidimensional relational structures (MDRSs). This set of structure defines collaborative tasks through both temporal and spatial relations between processes of heterogeneous robots. These relations are enriched with tensors which allow for geometrical reasoning about collaborative tasks. A learning schema is also proposed in order to derive the components of each MDRS. According to this schema, the components are learnt from data reporting the situated history of the processes executed by the team of robots. Data are organized as a multirobot collaboration treebank (MRCT) in order to support learning. Moreover, a generative approach, based on a probabilistic model, is combined together with nonnegative tensor decomposition (NTD) for both building the tensors and estimating latent knowledge. Preliminary evaluation of the performance of this framework is performed in simulation with three heterogeneous robots, namely, two Unmanned Ground Vehicles (UGVs) and one Unmanned Aerial Vehicle (UAV)

Modeling Security and Cooperation in Wireless Networks Using Game Theory

This research involves the design, development, and theoretical demonstration of models resulting in integrated misbehavior resolution protocols for ad hoc networked devices. Game theory was used to analyze strategic interaction among independent devices with conflicting interests. Packet forwarding at the routing layer of autonomous ad hoc networks was investigated. Unlike existing reputation based or payment schemes, this model is based on repeated interactions. To enforce cooperation, a community enforcement mechanism was used, whereby selfish nodes that drop packets were punished not only by the victim, but also by all nodes in the network. Then, a stochastic packet forwarding game strategy was introduced. Our solution relaxed the uniform traffic demand that was pervasive in other works. To address the concerns of imperfect private monitoring in resource aware ad hoc networks, a belief-free equilibrium scheme was developed that reduces the impact of noise in cooperation. This scheme also eliminated the need to infer the private history of other nodes. Moreover, it simplified the computation of an optimal strategy. The belief-free approach reduced the node overhead and was easily tractable. Hence it made the system operation feasible. Motivated by the versatile nature of evolutionary game theory, the assumption of a rational node is relaxed, leading to the development of a framework for mitigating routing selfishness and misbehavior in Multi hop networks. This is accomplished by setting nodes to play a fixed strategy rather than independently choosing a rational strategy. A range of simulations was carried out that showed improved cooperation between selfish nodes when compared to older results. Cooperation among ad hoc nodes can also protect a network from malicious attacks. In the absence of a central trusted entity, many security mechanisms and privacy protections require cooperation among ad hoc nodes to protect a network from malicious attacks. Therefore, using game theory and evolutionary game theory, a mathematical framework has been developed that explores trust mechanisms to achieve security in the network. This framework is one of the first steps towards the synthesis of an integrated solution that demonstrates that security solely depends on the initial trust level that nodes have for each other

Formal methods for analysing, coordinating, and controlling decisions in multi-agent systems

Multiagentensysteme sind verteilte (Computer)Systeme, die sich aus autonomen interagierenden Systemkomponenten, bezeichnet als Agenten, zusammensetzen. Sie bieten ein flexibles Framework zur Modellierung und Analyse von interaktiven Systemen, in denen Kooperation, Eigeninteresse und Autonomie eine entscheidende Rolle spielen. Dies ist zum Beispiel der Fall in Smart Grids. Eine Herausforderung in solchen Systemen ist die Kontrolle und die Koordination von Systemausführungen. Agenten handeln autonom und lassen sich daher oftmals nicht direkt kontrollieren, sondern bestenfalls beeinflussen. Aufgrund der Autonomie und des Selbstinteresses, ist es schwierig, angemessene Kontrollmechanismen zu finden. Die vorliegende Arbeit behandelt formale Grundlagen zu den Themen Entscheidungsfindung, Koordination und Kontrolle in Multiagentensystemen. Insbesondere werden in diesem Zusammenhang Logiken zur Analyse und Spezifikation von strategischen Fähigkeiten von Agenten, unter diversen Restriktionen, untersucht. Es werden formale Ansätze zur Beeinflussung und Überwachung von Systemausführungen eingeführt. In einem weiteren Teil der Arbeit wird mittels spieltheoretischer Verfahren analysiert, wie rationale Agenten interagieren und Entscheidungen treffen. Es wird argumentiert, dass formale Methoden und Werkzeuge zur Analyse und Kontrolle von autonomen Systemen entscheidend für deren verlässliche Entwicklung sind.Multi-agent systems (MASs) are distributed (computer) systems composed of autonomously (inter-)acting system components referred to as agents. MASs offer a flexible framework to model and analyse many real world settings in which cooperation, self-interest, and autonomy are crucial elements. A key challenge in such settings is the control and coordination of behavior. However, due to the agents' autonomy behavior can often not be controlled, but at best be influenced in some way or another. For example, agents can be given incentives in order to affect their decision-making in such a way that the emergent behavior of all actors is desirable from the system's perspective. The properties of self-interest and autonomy make it challenging to find appropriate control mechanisms. Existing coordination and control approaches from the distributed system literature are often not applicable due to the lack of direct control on the system components of MASs. New methods and tools are needed. In this thesis formal foundations related to the subjects of decision making, coordination and control in MASs are studied. In particular, we investigate (extensions of) temporal and strategic logics which capture specific capabilities of agents that influence their decision making. We also propose formal approaches to control, coordinate and monitor the emergent behavior in MASs. In the last part of the thesis we analyse how rational agents interact and make decisions using game theoretical methods. We argue that such formal approaches and tools to analyse and control autonomous systems are crucial for the development of reliable and flexible systems and will become even more crucial in the near future