Computación Evolutiva y Teoría de Juegos: Un híbrido para la automatización en sistemas de soporte a la negociación

This paper is about the decisión analysis methods for group decisions over the web, in reason of involve the people in the different decisions that interest them, we have in internet one chance for to reduce the gap between governor and governed We can summarize the main object that pursues this like to propose one automated negotiation approach in the web, which be based on the evolutionary computation and the game theory.Este articulo trata sobre los métodos de análisis de decisión y de apoyo a la decisión de grupos, desplegados sobre la web, con miras a involucrar al público en las decisiones que les afectan, es así como vemos en internet una oportunidad de acortar la brecha entre regidores y regidos. Podemos resumir el objeto principal que persigue este trabajo como proponer e implementar un esquema de negociación automatizada sobre la web, el cual esté basado en la computación evolutiva y la teoría de juegos

Modified bargaining protocols for automated negotiation in open multi-agent systems

Current research in multi-agent systems (MAS) has advanced to the development of open MAS, which are characterized by the heterogeneity of agents, free exit/entry and decentralized control. Conflicts of interest among agents are inevitable, and hence automated negotiation to resolve them is one of the promising solutions. This thesis studies three modifications on alternating-offer bargaining protocols for automated negotiation in open MAS. The long-term goal of this research is to design negotiation protocols which can be easily used by intelligent agents in accommodating their need in resolving their conflicts. In particular, we propose three modifications: allowing non-monotonic offers during the bargaining (non-monotonic-offers bargaining protocol), allowing strategic delay (delay-based bargaining protocol), and allowing strategic ignorance to augment argumentation when the bargaining comprises argumentation (ignorance-based argumentation-based negotiation protocol). Utility theory and decision-theoretic approaches are used in the theoretical analysis part, with an aim to prove the benefit of these three modifications in negotiation among myopic agents under uncertainty. Empirical studies by means of computer simulation are conducted in analyzing the cost and benefit of these modifications. Social agents, who use common human bargaining strategies, are the subjects of the simulation. In general, we assume that agents are bounded rational with various degrees of belief and trust toward their opponents. In particular in the study of the non-monotonic-offers bargaining protocol, we assume that our agents have diminishing surplus. We further assume that our agents have increasing surplus in the study of delay-based bargaining protocol. And in the study of ignorance-based argumentation-based negotiation protocol, we assume that agents may have different knowledge and use different ontologies and reasoning engines. Through theoretical analysis under various settings, we show the benefit of allowing these modifications in terms of agents’ expected surplus. And through simulation, we show the benefit of allowing these modifications in terms of social welfare (total surplus). Several implementation issues are then discussed, and their potential solutions in terms of some additional policies are proposed. Finally, we also suggest some future work which can potentially improve the reliability of these modifications

Overeager Reciprocal Rationality and Mixed Strategy Equilibria

A rational agent in a multiagent world must decide on its actions based on the decisions it expects others to make, but it might believe that they in turn might be basing decisions on what they believe the initial agent will decide. Such reciprocal rationality leads to a nesting of models that can potentially become intractable. To solve such problems, game theory has developed techniques for discovering rational, equilibrium solutions, and AI has developed computational, recursive methods. These different approaches can involve different solution concepts. For example, the Recursive Modeling Method (RMM) finds different solutions than game-theoretic methods when solving problems that require mixed-strategy equilibrium solutions. In this paper, we show that a crucial difference between the approaches is that RMM employs a solution concept that is overeager. This eagerness can be reduced by introducing into RMM second-order knowledge about what it knows, in the form of a flexible functi..

Interaction and Intelligent Behavior

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage