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Agentes y Mecanismos de Votación
Abstract and Figures
Resumen Los mecanismos de votación se encuentran entre los métodos más utilizados para resolver problemas de la vida diaria en los que un conjunto de agentes deben tomar una decisión social consensuada. La familiaridad con este tipo de mecanismos ha llevado a que distintos investigado-res propongan esta forma de elección social como un medio para lograr que agentes artificiales interactúen de manera coordinada en un sistema multiagente. Sin embargo, el uso de mecanismos de votación no está restringido a escenarios con múltiples agentes. También son aplicables en si-tuaciones donde existen múltiples componentes internas del agente que pueden entrar en conflicto durante la toma de sus decisiones. En este trabajo analizamos el uso de mecanismos de votación en este último tipo de escenarios donde los "electores" surgen por el diseño modular del agente, o por la existencia de múltiples objetivos o preferencias que pueden entrar en conflicto. Para ello, introducimos conceptos básicos de teoría de votación que son utilizados en el diseño de un agente que debe balancear múltiples preferencias de un usuario durante la selección de aulas para el dic-tado de clases. Las principales fortalezas y debilidades de este enfoque son analizados como así también su vinculación con trabajos relacionados y posibles extensiones futuras.
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... Los sistemas computacionales distribuidos han evolucionado desde arquitecturas tradicionales basadas en el modelo cliente/servidor, a enfoques donde las componentes del sistema exhiben patrones de interacción más flexibles y elaborados, que incluyen comportamiento pro-activo (basado en objetivos), mecanismos de negociación, de argumentación, etc. A partir de esta tendencia, gran parte de las técnicas pensadas como mecanismos de interacción social de humanos, han pasado a convertirse en el fundamento de muchos sistemas computacionales donde las componentes interactúan utilizando protocolos de argumentación [12,2], votación [8,15,23], negociación [18,14] y mercado [20]. Si bien estos mecanismos de interacción han sido frecuentemente estudiados por separado, buena parte de la comunidad científica vinculada aĺ area, ha comenzado a reconocer la importancia de darle un tratamiento integral a estos enfoques y estudiarlos bajo el "paraguas conceptual" denominado tecnologías de acuerdo (en inglés, agreement technologies (AT)). ...
... Asimismo, mediante la instanciación e implementación de esta posible arquitectura, sería conveniente determinar en qué medida esta propuesta se adecua a la resolución de problemas concretos del mundo real que involucren agentes de software y hardware. Como potenciales dominios de aplicación se plantean trabajos con robots o dispositivos de comunicación móviles (agentes de hardware) y problemas vinculados alárea de Web Intelligence[26].En este contexto, ya se han logrado algunos avances en estos temas, relacionados al uso de votación en los procesos internos del agente[8,23], mientras que en[21,22] se desarrolló la primera arquitectura concreta de agente que integra al modelo BDI con servicios Web y razonamiento argumentativo en un mismo framework.Formación de Recursos HumanosTrabajos de tesis vinculados con las temáticas descritas previamente:1 tesis Doctoral en ejecución en co-dirección con investigador de la UNS.1 tesis Doctoral y 1 de Maestría finalizadas.1 tesis de Maestría en trámite de inscripción.3 tesis de Licenciatura aprobadas. ...
Resumen En este artículo se describen, en forma resumida, los trabajos de investigación y de-sarrollo que se están llevando a cabo en la línea de investigación "Sistemas Inteligentes" en la problemática vinculada a la integración de tecnologías de acuerdo dentro de arquitec-turas de razonamiento práctico, y se abordan temas vinculados a arquitecturas de agentes inteligentes, arquitecturas BDI y Procesos de Decisión Markov, mecanismos de votación, argumentación y negociación. Palabras clave: sistemas multi-agen-te, tecnologías de acuerdo, toma de de-cisión. Contexto La línea de investigación "Sistemas In-teligentes" se centra en la formalización, di-seño y desarrollo de agentes computacionales con capacidades cognitivas de alto nivel. Los enfoques utilizados en nuestra línea de tra-bajo, buscan dotar a los agentes inteligentes con las capacidades necesarias para enfrentar problemas complejos de la vida real, que in-volucran ambientes dinámicos, inciertos, li-mitados en recursos y que son parcialmente observables. Esta línea de investigación for-ma parte del proyecto "Nuevas tecnologías para el tratamiento integral de datos mul-timedia", Proyecto de Investigación consoli-dado de la Universidad Nacional de San Luis (UNSL), que se centra en la incorporación de información no estructurada (texto, audio, imágenes y video) en la resolución de pro-blemas y la toma de decisiones. Este proyec-to recibe financiamiento de la UNSL y de la Comisión Europea de Investigación e In-novación (Marie Curie Actions: FP7-People-2010-IRSES).
This textbook aims to provide a comprehensive overview of the essentials of microeconomics. It offers unprecedented depth of coverage, whilst allowing lecturers to 'tailor-make' their courses to suit personal priorities. Covering topics such as noncooperative game theory, information economics, mechanism design and general equilibrium under uncertainty, it is written in a clear, accessible and engaging style and provides practice exercises and a full appendix of terminology.
Consider the designers of a multiagent environment, who are charged with establishing the rules by which agents in an encounter will interact. Once the rules of encounter have been determined, each builder of each agent is free to design his own machine any way that he wants. However, the rules that were established will certainly affect the choices he makes in building his own agent.In this article we suggest an economic decision process that can be used to derive multiagent consensus, namely, the Clarke tax mechanism (E.H. Clarke, 1971). Consensus is reached through the process of voting; each agent expresses its preferences, and a group choice mechanism is used to select the result. Clarke tax-like mechanisms provide a set of attractive alternatives for the designers of multiagent environments, particularly if those environments consist of individually motivated heterogeneous agents.The Clarke tax mechanism has many desirable properties such as non-manipulability, individual rationality, and maximization of the agents' global utility. However, though theoretically attractive, the Clarke tax presents a number of difficulties when one attempts to use it in practical implementations. This article examines how the Clarke tax could be used as an effective consensus mechanism in domains consisting of automated agents. In particular, we consider how agents can come to a consensus without needing to reveal full information about their preferences, and without needing to generate alternatives prior to the voting process.
Our research agenda focuses on building software agents that can facilitate and streamline group problem solving in organizations. We are particularly interested in developing intelligent agents that can partially automate routine information processing tasks by representing and reasoning with the preferences and biases of associated users. The distributed meeting scheduler is a collection of agents, responsible for scheduling meetings for their respective users. Users have preferences on when they like to meet, e.g. time of day, day of week, status of other invitees, topic of the meeting, etc. The agent must balance such concerns, proposing and accepting meeting times that satisfy as many of these criteria as possible. For example, a user might prefer not to meet at lunchtime unless the president of the company is hosting the meeting. We apply techniques from voting theory to arrive at consensus choices for meeting times while balancing different preferences.
An architecture is presented in which distributed task- achieving modules, or behaviors, cooperatively determine a mobile robotís path by voting for each of various possible actions. An arbiter then performs command fusion and selects that action which best satisfies the prioritized goals of the system, as expressed by these votes, without the need to average commands. Command fusion allows multiple goals and constraints to be considered simultaneously. Examples of implemented systems are given, and future research directions in command fusion are discussed.
Of several responses made to the same situation, those which are accompanied or closely followed by satisfaction to the animal will, other things being equal, be more firmly connected with the situation, so that, when it recurs, they will be more likely to recur; those which are accompanied or closely followed by discomfort to the animal will, other things being equal, have their connections with that situation weakened, so that, when it recurs, they will be less likely to occur. The greater the satisfaction or discomfort, the greater the strengthening or weakening of the bond. (Thorndike, 1911) The idea of learning to make appropriate responses based on reinforcing events has its roots in early psychological theories such as Thorndike's "law of effect" (quoted above). Although several important contributions were made in the 1950s, 1960s and 1970s by illustrious luminaries such as Bellman, Minsky, Klopf and others (Farley and Clark, 1954; Bellman, 1957; Minsky, 1961; Samuel, 1963; Michie and Chambers, 1968; Grossberg, 1975; Klopf, 1982), the last two decades have wit- nessed perhaps the strongest advances in the mathematical foundations of reinforcement learning, in addition to several impressive demonstrations of the performance of reinforcement learning algo- rithms in real world tasks. The introductory book by Sutton and Barto, two of the most influential and recognized leaders in the field, is therefore both timely and welcome. The book is divided into three parts. In the first part, the authors introduce and elaborate on the es- sential characteristics of the reinforcement learning problem, namely, the problem of learning "poli- cies" or mappings from environmental states to actions so as to maximize the amount of "reward"
Control in behavior-based systems is distributed among a set of specialized behaviors. To achieve efficient implementation, behaviors exploit specific assumptions about a given task and environment. Thus they become vulnerable to deviations that render these assumption invalid. Yet it is important to provide appropriate responses to unforeseen situations. We demonstrate that, using voting techniques, a model-free approach may be provided to constructing reliable behaviors from a multitude of less reliable ones. A team of complementary behaviors vote for the set of possible actions and the action which is most favored is selected for controlling the system. We conjecture that selecting actions according to this scheme can improve the probability of success. Our conjecture is investigated through two sets of experiments. In the first, a team of obstacle avoidance behaviors vote to guide a mobile robot platform in the most appropriate direction. In the second, four object tracking modules...
The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the behavior of multiple users to recommend items of interest to individual users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed several variations of the technology. We take the perspective of CF as a methodology for combining preferences. The preferences predicted for the end user is some function of all of the known preferences for everyone in a database. Social Choice theorists, concerned with the properties of voting methods, have been investigating preference aggregation for decades. At the heart of this body of work is Arrow's result demonstrating the impossibility of combining preferences in a way that satisfies several desirable and innocuous-looking properties. We show that researchers working on CF algorithms of...