Sevilla-Villanueva, Beatriz; Gibert, Karina; Sànchez-Marrè, M. Frontiers in artificial intelligence and applications Vol. 256, p. 215-224 DOI: 10.3233/978-1-61499-320-9-215 Data de publicació: 2013-11-01 Article en revista
A profiling methodology is introduced for automatic interpretation of clusters in this work. This methodology contributes to the characterization of the resulting classes from a clustering process. This work aims to find a concordance between the proposed methodology and the experts’ description of these classes.
In this work the resulting classes from a clustering of a general population sample
based on their diet and physical activity habits are interpreted and compared with
the experts’ description of these classes by using the Class Panel Graphs. In this
work, we import techniques from the multivariate analysis into the cluster interpretation process.
One of the main problems enterprises face today is the bulk of data derived from various resources. Furthermore, the growth of technology and sciences has greatly influenced the area of management and decision-making procedures, and has dramatically changed the decision-making processes in different levels, both quantitatively and qualitatively. Knowledge management plays a vital role in supporting enterprise learning, since it facilitates the effective collective intellect of the enterprise. Question Answering (QA) system is playing an important role in current search engine optimization. Natural language processing technique is mostly implemented in QA system for asking user's question and several steps are also followed for conversion of questions to query form for getting an exact answer. Query languages have complex syntax, requiring a good understanding of the representation schema, including knowledge of details like namespaces, class and property names. In this research we proposed an initial model to implement Conceptual Question Answering and Automatic Information Inferences for the enterprise's operational knowledge management in ontology-based learning organization.
Clustering techniques are commonly performed to find homogeneous and distinguishable prototypes. However, a careful interpretation of these prototypes is the key to assist the expert to better organize this knowledge for decision making support. We use the annotated Traffic Lights Panel (aTPL), not only as a postprocessing tool to help understand clustering prototypes, but also to manage uncertainty related to variability which is inherent by itself within the prototypes. The aTPL handles this uncertainty by using the variation coefficients (VC) in the classes over two dimensions - tone and saturation. Two different aTLPs were obtained to a WWTP in Slovenia. Results suggested that aTPL could be seen as a useful tool with good levels of reliability when interpreting and managing uncertainty related to decision-making based on clustering prototypes.
Traumatic brain injury (TBI) is the leading cause of death and disability in children and young adults around the world. There is not enough on-field experience yet regarding which specific intervention (tasks or exercises assignation) is more appropriated to help therapists to design their cognitive rehabilitation (CR) plans. Our proposal is to consider the CR treatment as a sequence of tasks and to determine the associations between a CR treatment (or relevant subsequences of it) and the degree of response of the patient to it. In the proposed methodology, a clustering process is performed in such a way that treatment profiles (classes) are identified.. Afterwards, responses to CR (improvements) of the patients placed in the different classes have been studied by means of conditional distributions of variables versus the classes. Analyzing CR tasks as treatment patterns offers a different perspective from the traditional single task focus, and may provide a comprehensive approach to therapists to design CR programs.
For almost twenty years the Catalan Association of Artificial Intelligence (ACIA) has been promoting cooperation between researchers in artificial intelligence within the Catalan speaking community.
This book presents the proceedings of the 16th International Conference (CCIA 2013), held at the University of Vic (UVIC), Catalonia, Spain, in October 2013. This annual conference aims to foster discussion of the latest developments in artificial intelligence within the community of Catalan countries, as well as amongst members of the AI community worldwide.
The book contains the 26 full papers, 5 short papers and 12 poster presentations from the conference, which are grouped under the following topics: relational learning, planning; satisfiability and constraints; perception and image processing; preprocessing; patterns extraction and learning; post-processing, model interpretability and decision support; recommenders, similarity and CBR; and multiagent systems.
Batet, M.; Valls, A.; Gibert, Karina; Sánchez, D. Frontiers in artificial intelligence and applications Vol. 220, p. 207-216 DOI: 10.3233/978-1-60750-643-0-207 Data de publicació: 2010-10 Article en revista
Batet, M.; Martínez, S.; Valls, A.; Gibert, Karina Frontiers in artificial intelligence and applications Vol. 202, p. 242-251 DOI: 10.3233/978-1-60750-061-2-242 Data de publicació: 2009-10-01 Article en revista
In this paper, the automatic customization of an agent-based medical system is approached by means of ontologies. The particular case of Home Care studied and developed in the EU K4Care project, is presented. The customization is achieved by means of generating individual versions of a reference ontology, called Actor Profile Ontology, which defines the behaviour of the actors in the multi-agent system. The paper, analyses the usability and advantages of this customization in order to add flexibility and adaptability to the system. It also shows how the personalized ontology is able to represent the liabilities and permissions of a particular user, providing the base for automatically generating the behaviour of the corresponding personal agent. A tool, called ATAPO, is also presented. It has been designed to assist the user in the personalization process. The way how this tool interacts with the system to permit the online modification of the behaviour of the agents is also discussed.
Chieppa, A.; Gibert, Karina; Ignasi Gómez-Sebastià; Sànchez-Marrè, M. Frontiers in artificial intelligence and applications Vol. 184, p. 161-169 DOI: 10.3233/978-1-58603-925-7-161 Data de publicació: 2008-10 Article en revista
Gibert, Karina; García-Rudolph, A.; García, A.; Roig-Rovira, T.; Bernabeu, M.; Tormos, J. Frontiers in artificial intelligence and applications Vol. 184, p. 170-177 DOI: 10.3233/978-1-58603-925-7-170 Data de publicació: 2008-10 Article en revista
Sanchez, M.; Yu-Chiang, H.; Prats, F.; Rovira, X.; Sayeras, J.; Dawson, J. Frontiers in artificial intelligence and applications Vol. 163, num. 1, p. 310-319 Data de publicació: 2007-10 Article en revista
In this paper we present a novel approach for combining Case-Based Reasoning (CBR)Argumentation. This approach involves 1) the use of CBR for evaluating the arguments submitted by agents in collaborative decision making dialogs, and 2) the use of Argument Schemes and Critical Questions to organize the CBR memory space. The former involves use of past cases to resolve conflicts among newly submitted arguments by assigning them a strength, and possibly submitting additional arguments deemed relevant in similar past deliberations. The latter enables use of agents' submitted arguments instantiating Argument Schemes and Critical Questions, to assess the similarity among cases. This use of CBR and argumentation is formulated with the ProCLAIM model, which features a Mediator Agent that directs proponent agents in their deliberation and subsequently evaluates their submitted arguments so as to conclude whether a proposed decision is valid. To motivate and substantiate the practical value of this approach, we illustrate its application in the human organ transplantation field.