Francisco José Sanguino has a BA in Spanish language and literature. He is a teacher of Spanish as a second language, and a secondary school teacher. He has received grants for the University of fashioned models which ignore the evolution that society is going through.Design/methodology/approach -The educational environment cannot continue to be a fixed, closed and isolated environment where students -assuming a basically passive role -receive standardised teaching. It must consequently experience a fast and decisive transformation which allows it, amongst other things, to respond to the new challenge posed by society: the need for all of us to share the knowledge we generate, so that further progress can be made. Originality/value -RUA is the storage place of all the teaching materials published by our teaching staff, and which are retrieved from OCW-UA, while OCW-UA serves as an organisational model of teaching content self-archived by the teaching staff in RUA. The connection between the projects has allowed us to present the promotion of open knowledge as a global strategic gamble of the University, which has contributed to a greater acceptance by the teaching staff. This work is original in that it shows a successful experience of involvement by one university and its members in the promotion of open knowledge. Findings -

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Purpose -One of the main goals of vision systems is to recognize objects in real world to perform appropriate actions. This implies the ability of handling objects and, moreover, to know the relations between these objects and their environment in what we call scenes. Most of the time, navigation in unknown environments is difficult due to a lack of easily identifiable landmarks. Hence, in this work, some geometric features to identify objects are considered. Firstly, a Markov random field segmentation approach is implemented. Then, the key factor for the recognition is the calculation of the so-called distance histograms, which relate the distances between the border points to the mass center for each object in a scene. Design/methodology/approach -This work, first discusses the features to be analyzed in order to create a reliable database for a proper recognition of the objects in a scene. Then, a robust classification system is designed and finally some experiments are completed to show that the recognition system can be utilized in a real-world operation. Findings -The results of the experiments show that including this distance information improves significantly the final classification process. Originality/value -This paper describes an object recognition scheme, where a set of histograms is included to the features vector. As is shown, the incorporation of this feature improves the robustness of the system and the recognition rate.

Summary. The prey-predator pursuit problem is referenced many times in literature. It is a generic multi-agent problem whose solutions could by applied to many particular instances. Solutions proposed usually apply non-supervised learning algorithms to train prey and predators. Most of these solutions criticize the greedy algorithm originally proposed by Korf. However, we believe that the improvement obtained by these new proposals does not pay off with relation to their complexity.The method used by Korf is a natural way to surround a prey without explicit communication between predators. The knowledge one predator has about others is limited just to what it can see. In Korf's model, agents are able to see the complete world at once. In this paper we propose to start from Korf's ideas and extend them to improve his model. First, we propose a simple extension of Korf's fitness function and we consider the problems related to a partial view of the world. Second, we propose a communication protocol to partially overcome them. The final results suggest that more work needs to be done, and we propose a way to follow-on.

Abstract. Developing coodination among groups of agents is a big challenge in multi-agent systems. An appropriate enviroment to test new solutions is the prey-predator pursuit problem. As it is stated many times in literature, algorithms and conclusions obtained in this environment can be extended and applied to many particular problems. The first solutions for this problem proposed greedy algorithms that seemed to do the job. However, when concurrency is added to the environment it is clear that inter-agent communication and coordination is essential to achieve good results. This paper proposes two new ways to achieve agent coodination. It starts extending a well-known greedy strategy to get the best of a greedy approach. Next, a simple coodination protocol for prey-sight notice is developed. Finally, under the need of better coordination, a Neuroevolution approach is used to improve the solution. With these solutions developed, experiments are carried out and performance measures are compared. Results show that each new step represents an improvement with respect to the previous one. In conclusion, we consider this approach to be a very promising one, with still room for discussion and more improvements.