José M. Molina

David Sánchez Pedroche, Daniel Amigo, Jesús García et al.

This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information.

D. Griol, A. Sanchis, J. M. Molina et al.

In this paper, we present a methodology for the development of embodied conversational agents for social virtual worlds. The agents provide multimodal communication with their users in which speech interaction is included. Our proposal combines different techniques related to Artificial Intelligence, Natural Language Processing, Affective Computing, and User Modeling. Firstly, the developed conversational agents. A statistical methodology has been developed to model the system conversational behavior, which is learned from an initial corpus and improved with the knowledge acquired from the successive interactions. In addition, the selection of the next system response is adapted considering information stored into users profiles and also the emotional contents detected in the users utterances. Our proposal has been evaluated with the successful development of an embodied conversational agent which has been placed in the Second Life social virtual world. The avatar includes the different models and interacts with the users who inhabit the virtual world in order to provide academic information. The experimental results show that the agents conversational behavior adapts successfully to the specific characteristics of users interacting in such environments.

Ebrahim Navid Sadjadi, Jesus Garcia, Jose M. Molina et al.

This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum values of the parameters on a smooth surface would facilitate further improvements in the application of other derivative based optimization control algorithms such as MPC or robust control algorithms to achieve a combined modeling-control scheme. Compared to the earlier works on the smooth fuzzy modeling structures, we could reach a desired trade-off between the model optimality and the computational load. The proposed method has been evaluated on a test problem as well as the non-linear dynamic of a chemical process.

Javier Carbo, Jose M Molina, Miguel A Patricio

Managing transition plans is one of the major problems of people with cognitive disabilities. Therefore, finding an automated way to generate such plans would be a helpful tool for this community. In this paper we have specifically proposed and compared different alternative ways to merge plans formed by sequences of actions of unknown similarities between goals and actions executed by several operator agents which cooperate between them applying such actions over some passive elements (node agents) that require additional executions of another plan after some time of use. Such ignorance of the similarities between plan actions and goals would justify the use of a distributed recommendation system that would provide an useful plan to be applied for a certain goal to a given operator agent, generated from the known results of previous executions of different plans by other operator agents. Here we provide the general framework of execution (agent system), and the different merging algorithms applied to this problem. The proposed agent system would act as an useful cognitive assistant for people with intelectual disabilities such as autism.

J. Carbo, J. M. Molina

In this paper we propose a trust model to be used into a hypothetical mixed environment where humans and unmanned vehicles cooperate. We address the inclusion of emotions inside a trust model in a coherent way to the practical approaches to the current psychology theories. The most innovative contribution is how privacy issues play a role in the cooperation decisions of the emotional trust model. Both, emotions and trust have been cognitively modeled and managed with the Beliefs, Desires and Intentions (BDI) paradigm into autonomous agents implemented in GAML (the programming language of GAMA agent platform) that communicates using the IEEE FIPA standard. The trusting behaviour of these emotional agents is tested in a cooperative logistics problem where: agents have to move objects to destinations and some of the objects and places have privacy issues. The execution of simulations of this logistic problem shows how emotions and trust contribute to improve the performance of agents in terms of both, time savings and privacy protection

J. Carbo, N. Sanchez, J. M. Molina

In this paper an agent-based simulation is developed in order to evaluate an AmI scenario based on agents. Many AmI applications are implemented through agents but they are not compared to any other existing alternative in order to evaluate the relative benefits of using them. The proposal simulation environment developed in Netlogo analyse such benefits using two evaluation criteria: First, measuring agent satisfaction of different types of desires along the execution. Second, measuring time savings obtained through a correct use of context information. So, here, a previously suggested agent architecture, an ontology and a 12-steps protocol to provide AmI services in airports, is evaluated using a NetLogo simulation environment. The present work uses a NetLogo model considering scalability problems of this application domain but using FIPA and BDI extensions to be coherent with our previous works and our previous JADE implementation of them. The NetLogo model presented simulates an airport with agent users passing through several zones located in a specific order in a map: passport controls, check-in counters of airline companies, boarding gates, different types of shopping. Although initial data in simulations are generated randomly, and the model is just an approximation of real-world airports, the definition of this case of use of Ambient Intelligence through NetLogo agents opens an interesting way to evaluate the benefits of using Ambient Intelligence, which is a significant contribution to the final development of them.

Lukáš Matějů, David Griol, Zoraida Callejas et al.

Deep learning is providing very positive results in areas related to conversational interfaces, such as speech recognition, but its potential benefit for dialog management has still not been fully studied. In this paper, we perform an assessment of different configurations for deep-learned dialog management with three dialog corpora from different application domains and varying in size, dimensionality and possible system responses. Our results have allowed us to identify several aspects that can have an impact on accuracy, including the approaches used for feature extraction, input representation, context consideration and the hyper-parameters of the deep neural networks employed.