Open electronic communities may bring together people geographically and culturally unrelated to each other. In this context, taking costly decisions depends on the expectations created according to past behaviour of others. This kind of information is usually called reputation and it is one of the most significant factors to trust merchants and recommenders in electronic commerce interactions. When agents are acting on behalf of humans in such commercial scenarios, they should represent and reason about trust and reputation as humans do. In this paper a trust management mechanism tackles the vague, subjective and uncertain information about others using fuzzy sets. The operations defined over such fuzzy sets updates the reputation of merchants according to the general situation faced. This trust management mechanism is applied to a multiagent system of merchants, recommenders and buyers, where collaborative recommendations coexist with competitive intentions. The developed multi-agent system is used to compare the level of success of predictions obtained from the fuzzy computations with some of the most well known (crisp) reputation mechanisms: ebay, bizrate, sporas and regret when the behaviour of merchants change in different degrees. Finally, the potential benefits of using fuzzy sets to manage reputation in multi-agent systems are analyzed according to the excellent experimental results shown.
During recent years, conversational agents have become a solution to provide straightforward and more natural ways of retrieving information in the digital domain. In this article, we present an agent based dialog simulation technique for learning new dialog strategies and evaluating con versational agents. Using this technique, the effort necessary to acquire data required to train the dialog model and then explore new dialog strategies is considerably reduced. A set of measures has also been defined to evaluate the dialog strategy that is automatically learned and to compare different dialog corpora. We have applied this technique to explore the space of possible dialog stra tegies and evaluate the dialogs acquired for a conversational agent that collects monitored data from patients suffering from diabetes. The results of the comparison of these measures for an initial corpus and a corpus acquired using the dialog simulation technique show that the conversational agent reduces the time needed to complete the dialogs and improve their quality, thereby allowing the conversational agent to tackle new situations and generate new coherent answers for the situations already present in an initial model.
The deployment of Intelligent Vehicles in urban environments requires reliable estimation of positioning for urban navigation. The inherent complexity of this kind of environments fosters the development of novel systems which should provide reliable and precise solutions to the vehicle. This article details an advanced GNSS/IMU fusion system based on a context-aided Unscented Kalman filter for navigation in urban conditions. The constrained non-linear filter is here conditioned by a contextual knowledge module which reasons about sensor quality and driving context in order to adapt it to the situation, while at the same time it carries out a continuous estimation and correction of INS drift errors. An exhaustive analysis has been carried out with available data in order to characterize the behavior of available sensors and take it into account in the developed solution. The performance is then analyzed with an extensive dataset containing representative situations. The proposed solution suits the use of fusion algorithms for deploying Intelligent Transport Systems in urban environments.
In this work we present a novel and efficient algorithmindependent stopping criterion, called the MGBM criterion, suitable for Multiobjective Optimization Evolutionary Algorithms (MOEAs).The criterion, after each iteration of the optimization algorithm, gathers evidence of the improvement of the solutions obtained so far. A global (execution-wise) evidence accumulation process inspired by recursive Bayesian estimation decides when the optimization should be stopped. Evidence is collected using a novel relative improvement measure constructed on top of the Pareto dominance relations. The evidence gathered after each iteration is accumulated and updated following a rule based on a simplified version of a discrete Kalman filter.Our criterion is particularly useful in complex and/or highdimensional problems where the traditional procedure of stopping after a predefined amount of iterations cannot be used and the waste of computational resources can induce to a detriment of the quality of the results.Although the criterion discussed here is meant for MOEAs, it can be easily adapted to other softcomputing or numerical methods by substituting the local improvement metric with a suitable one.
a b s t r a c tKnowledge-based systems (KBS) are advanced systems for representing complex problems. Their architecture and representation formalisms are the groundwork of today's systems. The knowledge is usually derived from expertise in specific areas and has to be validated according to a different methodology than is used in conventional systems because the knowledge is symbolic. This paper describes the design, definition and evaluation of a knowledge-based system using the CommonKADS (CKADS) methodology to formally represent contextual information for the Appear platform. We also evaluate the context-aware information system from the user's point of view using a U2E system and also validate it through a simulated example in a realistic environment: an airport domain, which is a significant step towards formally building KBS applications.
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