Acquired Brain Injury (ABI) constitutes a major and increasing social and healthcare concern of high diagnostic and therapeutic complexity. Its incidence and survival rate after the initial critical phases makes it a prevalent problem that needs to be addressed. ABI patients frequently suffer from a series of cognitive disorders related to memory, attention, language, or executive functions. Cognitive rehabilitation aims to reduce the impact of the disabling conditions in order to reduce functional limitations and increase patient's autonomy. For the rehabilitation process to be more effective, treatments must be intensive, personalized to the patient's condition and evidence-based; and require constant monitoring. The main goal of the IIIA team is the research on new machine learning/CBR techniques able to propose and revise personalized therapies in real-time.

Digital games that are worth playing by older people are a new type of digital games that can reinforce and exploit the strengths of older people as individuals and game players rather than merely compensating for their limitations, reduce isolation and foster socialisation, both offline and online, and improve the physical and psychosocial wellbeing of older people. The research conducted in the WorthPlay project aims to enable an understanding and development of these types of digital games, and is grounded in ethnography, participatory and iterative design, and experiences of digital game play. This research will be conducted within the context of designing, developing and evaluating a prototype of an online game that is worth playing by older people, by involving and engaging in the research a user group consisting of older people of mixed gender and expertise playing games, and relevant members of their social circles. WorthPlay will take the state of the art of human-computer interaction research with older people and digital games forward by producing a human taxonomy of digital games for older people; providing a deeper understanding of everyday digital game by older people, of the challenges and opportunities of emerging human-game interaction styles and gaming platforms to improve the accessibility and playability of digital games for older people, and their physical and psychosocial wellbeing, of game adaptation to individual users, and of cultural similarities and differences in digital game play. WorthPlay will also improve relevant methodological aspects of human-computer interaction research with older people by looking into ethnography, user engagement, and iterative and participatory design, in out-of-laboratory conditions over extended periods of time. WorthPlay will also develop a pipeline to integrate the technology and research created during the project into marketable outcomes, and develop original methods of marketing digital games to the older population.

Ver video de presentación

BUSCAMEDIA aims at significant advances in the areas of semantic search, audiovisual production and riche media distribution thgough the creation of a unique semantic multimedia search engine. Specifically, the involvement of IIIA-CSIC is on the automatic generation of narrative content, such as summaries of soccer matches, or news shows. More information can be found at the overall BUSCAMEDIA page: http://www.cenitbuscamedia.es/

The PON project studies container loading when objects are nonorthogonal, but their shape is represented in terms of polyhedra.

Case-based reasoning (CBR) combines in an effective manner both learning from experience and usage of domain knowledge. Techniques for case retrieval and reuse should not be studied in an isolated manner; instead they should be designed and evaluated in a framework that integrates different types of CBR systems as proposed in this project.

The goal of this project is to enlarge the capabilities of CBR systems to address three challenges we have identified as significant:

A.    The widespread use of ontologies today raises the issue that domain knowledge expressed in ontological frameworks has to be integrated not only in knowledge-intensive CBR but also indata-intensive CBR,

B.    The realization that all models are partial and each approximates reality differently raises the issue of CBR systems capable of using and integrating experience with multiple sources,

C.      The traditional emphasis on CBR research applied to the problem space raises the issue to focus more research on the solution space for improving the case reuse techniques, retrieval guidance techniques, and analyzing the solution space similarity structure.  

Case-based reasoning (CBR) combines in an effective manner both learning from examples and usage of domain knowledge. Techniques for case retrieval and reuse should not be studied in an isolated manner; instead they should be designed and evaluated in model in a framework that integrates different types of CBR systems as proposed in this project.
The main objectives of the project can be summarized as follows:

• new ways to use techniques of soft computing for CBR,
• techniques for case reuse of a declarative and generic nature,
• techniques for case retrieval in knowledge-intensive CBR systems,
• integrating ontologies both in CBR systems and retrieval and reuse techniques
• maintenance techniques both for case bases and for CBR systems capable of dealing with issues arising from design, implementation, and deployment of industrial strength CBR systems,
• the empirical evaluation of the developed techniques by means of CBR prototypes implemented for several experimental domains, and
• developing component-based software platforms to support CBR systems development.

This project will study and develop a support tool for musical recording manipulation with the goal of enhancing sound and musical qualities in professional sound post production. Specifically, the project will address the description of musical attributes, music database storage and retrieval of those descriptions, and sound transformation based on descriptions using physical attributes (e.g. pitch), perceptual attributes (e.g. brightness), and musical knowledge (e.g. tension).

The components used for sound content analysis and description, as well as the components for physical manipulation of sound, are based on spectral modelling analysis and synthesis techniques. Moreover, the descriptions to be used will be compatible as far as possible with the future MPEG-7 standard for multimedia content description. The transformation components will use both case-based reasoning techniques and modelling of musical knowledge.

The goal of this project is to extend the multiagent system for robot navigation, developed in the project DPI2000-1352-C02-02, by means of a CBR (Case-Based Reasoning) Agent.
This agent will provide learning by experience capabilities to the navigation system. This new functionality will be based upon the detection (via vision and laser sensors) of environment situations that may affect the behaviour of the robot in such away that when confronted with a situation similar to previously experienced ones, the CBR Agent should be able to forsee the results of navigation actions based on what happened in the past similar situations.
We will focus our attention to those situations that lead to failures in reaching the navigation goal in order to avoid a new failure. However, we will also take into account those pairs of situation-action that lead to successfully reaching the goal.
To develop such CBR agent, we must deal with what can be called continuous CBR, that is to study how to represent in the case-base simbolic abstractions (high-level knowledge) of low-level continuous information coming from the sensor readings.
Another relevant aspect is that concerning the notion of similarity between situations and, in particular, how to measure these similarities. How to reuse a past successful solution is also a relevant aspect to be developed. It is worth noticing that there are extremely few existing efforts in CBR research dealing with continuous CBR. We, therefore, face an extremely important and interesting research problem.