Emotions play a key role in any purchasing process even more when it is done through web portals. In this paper, we present a fuzzy-logic based system aimed to generate emotions for a virtual seller or avatar. The 3D avatar is integrated within the e-Zoco e-commerce portal and its behaviour depends on the product search results. Moreover, the mood of the avatar not only depend on the products that the portal can offer, but also the recommendations that can be used to guide the customer when beginning a new search. The system provides advice to the clients in order to assist them in the specification of constraints and thus obtain more interesting products in a shorter time.

Users of B2C (Business-to-Consumer) portals often lack of detailed knowledge about the state of the market related to the product they want to purchase. This leads to consumers purchasing the most popular product of a category, although perhaps that is neither the best suited for their requirements nor at the best cost. In this paper, a methodology to develop a recommender system is proposed. Our proposal is based on a supervised learning approach to infer knowledge that allows consumers to unveil what the existing patterns among the features that describe the searched product are. Such knowledge allows consumers to learn what they can buy and at what cost.

Intelligent surveillance refers to using Artificial Intelligence techniques in order to improve surveillance and deal with semantic information obtained from low-level security devices. In this context, the use of expert knowledge may offer a more realistic solution when dealing with the design of a surveillance system. In this work, a conceptual framework based on normality analysis to detect abnormal behaviors by means of normality concepts is presented. A normality concept specifies how a certain object should ideally behave in a concrete environment depending on such a concept. The definition of the normal path concept is studied in depth in order to analyze behaviors in an outdoor environment.

Machine learning algorithms can be used to discover patterns in data into e-commerce C2C portals and this knowledge can then be useful to help customers to refine their searches and to choose what to buy. In this work, it is proposed a new machine learning algorithm based on fuzzy logic. We describe the proposed algorithm for supporting customer searches in e-Marketplaces and show the results obtained on simulated examples.

In this paper, we propose the use of temporal fuzzy chains for the modeling of dynamical systems in a way that is comprehensible. We are interested in helping the overall understanding of the system execution, over and during a precise and finite time. To this end, we model its input/output behavior and how this has changed in the past. There is a double goal in mind: accuracy and interpretability. An inductive algorithm for analyzing finite continuous multivariate time series will be achieved, in which the use of fuzzy logic has been taken into account. The aim of the algorithm is to help us to find changes in a system, as well as to identify the causes of these changes in a linguistic form. The causes will be specified by means of a set of fuzzy transitions between consecutive states, which consist of fuzzy rules that model the system. The method suggested has been applied on a real life case, human walk modeling.

A lot of current small businesses involve very simple interaction models that are being formalized for carrying out electronic commerce via open networks, like the Internet, or closed, private networks. E-commerce implies carry on negotiation between suppliers and customers, and makes a decision. We develop a technique for multi-issue negotiations in competitive environments in which many sellers negotiate with one agent buyer who has to make a decision. The technique leads the negotiation carrying out comparison-based analysis of multi-options provided for sellers and gains relevant information about each option. Moreover, an algorithm is developed to support buyer's decision based on that acquired information.

Temporal fuzzy models (TFMs) are used to represent the dynamic systems (DSs) in a linguistic way. TFMs are composed of ordered temporal fuzzy rules (TFRs), thus, each rule represents a temporal zone. Normally, we obtain the TFMs by a fuzzy model induced by means of another induction algorithm, that is: first of all, an induction algorithm is used to obtain a fuzzy model, and secondly, an algorithm is used to transform the obtained fuzzy model into a TFM. The aim of this paper is to present a direct induction algorithm of TFMs, that is, to present a method capable of obtaining a TFM which represents the DS study through the use of a time series as an input.

In this paper, we develop a technique for acquiring the finite set of attributes or variables which the expert uses in a classification problem for characterising and discriminating a set of elements. This set will constitute the schema of a training data set to which an inductive learning algorithm will be applied. The technique developed uses ideas taken from psychology, in particular from Kelly's Personal Construct Theory. While we agree that Kelly's repertory grid technique is an efficient way to do this, it has several disadvantages which we shall try to solve by using a fuzzy repertory table. With the suggested technique, we aim to obtain the set of attributes and values which the expert can use to "measure" the object type (class) on the classification problem in some way. We will also acquire some general rules to identify the expert's evident knowledge; these rules will comprise concepts belonging to their conceptual structure.