Catálogo de publicaciones - libros

In this work, we present a constrained-based representation for specifying the goals of “course design”, that we call curricula model, and introduce a graphical language, grounded into Linear Time Logic, to design curricula models which include knowledge of proficiency levels. Based on this representation, we show how model checking techniques can be used to verify that the user’s learning goal is supplied by a curriculum, that a curriculum is compliant to a curricula model, and that competence gaps are avoided.

This paper presents Symphony, an IMS (Intelligent Manufacturing System) developed in the context of an interregional project supported by the European Commission. Symphony was a three year project that aimed at the development of an integrated set of tools for management of enterprizes, in order facilitate the continuous creation, exploration and exploitation of business opportunities through strategic networking. In particular, Symphony is devoted to support human resource managers of Small–Medium Enterprises in their decision making process about the looking for newcomers and/or assigning people to jobs. About this topic, the paper focuses on SymMemory, a case–based module of the main system that has been developed to identify what features are necessary to evaluate a person, aggregate them into a suitable case–structure representing a person or job profile and comparing profiles according to a specific similarity algorithm.

This paper describes outcome from a project aimed at creating an instance of integrated environment endowed with heterogeneous software and robotic agents to actively assist an elderly person at home. Specifically, a proactive environment for continuous daily activity monitoring has been created in which an autonomous robot acts as the main interactor with the person. This paper describes how the synergy of different technologies guarantees an overall intelligent behavior capable of personalized and contextualized interaction with the assisted person.

Detection of human skin in an arbitrary image is generally hard. Most color-based skin detection algorithms are based on a static color model of the skin. However, a static model cannot cope with the huge variability of scenes, illuminants and skin types. This is not suitable for an interacting robot that has to find people in different rooms with its camera and without any a priori knowledge about the environment nor of the lighting.

In this paper we present a new color-based algorithm called VR filter. The core of the algorithm is based on a statistical model of the colors of the pixels that generates a dynamic boundary for the skin pixels in the color space. The motivation beyond the development of the algorithm was to be able to correctly classify skin pixels in low definition images with moving objects, as the images grabbed by the omnidirectional camera mounted on the robot. However, our algorithm was designed to correctly recognizes skin pixels with any type of camera and without exploiting any information on the camera.

In the paper we present the advantages and the limitations of our algorithm and we compare its performances with the principal existing skin detection algorithms on standard perspective images.

Due to the fast growing of the information available on the Web, the retrieval of relevant content is increasingly hard. The complexity of the task is concerned both with the semantics of contents and with the filtering of quality-based sources. A recent strategy addressing the overwhelming amount of information is to focus the search on a snapshot of internet, namely a Web view. In this paper, we present a system supporting the creation of a quality-based view of the Web. We give a brief overview of the software and of its functional architecture. More emphasis is on the role of AI in supporting the organization of Web resources in a hierarchical structure of categories. We survey our recent works on document classifiers dealing with a twofold challenge. On one side, the task is to recommend classifications of Web resources when the taxonomy does not provide examples of classification, which usually happens when taxonomies are built from scratch. On the other side, even when taxonomies are populated, classifiers are trained with few examples since usually when a category achieves a certain amount of Web resources the organization policy suggests a refinement of the taxonomy. The paper includes a short description of a couple of case studies where the system has been deployed for real world applications.

The application of Reinforcement Learning (RL) algorithms to learn tasks for robots is often limited by the large dimension of the state space, which may make prohibitive its application on a tabular model. In this paper, we describe LEAP (Learning Entities Adaptive Partitioning), a model-free learning algorithm that uses overlapping partitions which are dynamically modified to learn near-optimal policies with a small number of parameters. Starting from a coarse aggregation of the state space, LEAP generates refined partitions whenever it detects an between the current action values and the actual rewards from the environment. Since in highly stochastic problems the adaptive process can lead to over-refinement, we introduce a mechanism that the macrostates without affecting the learned policy. Through refinement and pruning, LEAP builds a multi-resolution state representation specialized only where it is actually needed. In the last section, we present some experimental evaluation on a grid world and a complex simulated robotic soccer task.

In this paper, we analyze work on mobile robotics with the goal of highlighting the uses of contextual knowledge aiming at a flexible and robust performance of the system. In particular, we analyze different robotic tasks, ranging from robot behavior to perception, and then propose to characterize “contextualization” as a design pattern. As a result, we argue that many different tasks indeed can exploit contextual information and, therefore, a single explicit representation of knowledge about context may lead to significant advantages both in the design and in the performance of mobile robots.

The main difficulty to attain fully autonomous robot navigation outdoors is the fast detection of reliable visual references, and their subsequent characterization as landmarks for immediate and unambiguous recognition. Aimed at speed, our strategy has been to track salient regions along image streams by just performing on-line pixel sampling. Persistent regions are considered good candidates for landmarks, which are then characterized by a set of subregions with given color and normalized shape. They are stored in a database for posterior recognition during the navigation process. Some experimental results showing landmark-based navigation of the legged robot Lauron III in an outdoor setting are provided.

This paper addresses the problem of automatic grasp synthesis of unknown planar objects. In other words, we must compute points on the object’s boundary to be reached by the robotic fingers such that the resulting grasp, among infinite possibilities, optimizes some given criteria. Objects to be grasped are represented as superellipses, a family of deformable 2D parametric functions. They can model a large variety of shapes occurring often in practice by changing a small number of parameters. The space of possible grasp configurations is analyzed using genetic algorithms. Several quality criteria from existing literature together with kinematical and mechanical considerations are considered. However, genetic algorithms are not suitable to applications where time is a critical issue. In order to achieve real-time characteristics of the algorithm, neural networks are used: a huge training-set is collected off-line using genetic algorithms, and a feedforward network is trained on these values. We will demonstrate the usefulness of this approach in the process of grasp synthesis, and show the results achieved on an anthropomorphic arm/hand robot.

This paper describes a robotic architecture that uses visual attention mechanisms for autonomous navigation in unknown indoor environments. A foveation mechanism based on classical bottom-up gaze shifts allows the robot to autonomously select landmarks, defined as salient points in the camera images. Landmarks are memorized in a behavioral fashion, coupling sensing and acting to achieve a representation view and scale independent. Selected landmarks are stored in a topological map; during the navigation a top-down mechanism controls the attention system to achieve robot localization. Experiments and results show that our system is robust to noise and odometric errors, being at the same time adaptable to different environments and acting conditions.