A project for an intelligent system: Vision and learning

One of the most critical aspects of a truly intelligent system is the ability to learn, that is, to improve its own functionality by interacting with the environment and exploring it. In this paper, we argue that learning from exploring the environment should be the main goal in developing artificial intelligence. We also argue in favor of an integrated system—combining several state-of-the-art aspects of artificial intelligence, such as speech, vision, natural language, expert systems—as the experimental platform with which to approach this problem. We then describe the main features of a project of this type, MAIA, which is under development at I.R.S.T. The vision components of the system will be discussed in some detail, especially the navigation architecture of the indoor robot available to MAIA. We will conclude outlining some initial learning problems that will be approached within the MAIA project, such as learning to recognize faces and learning to update the map of the Institute used for indoor navigation.     

Multi-scale correlations in different futures markets

In the present work we investigate the multiscale nature of the correlations for high frequency data (1 min) in different futures markets over a period of two years, starting on the 1st of January 2003 and ending on the 31st of December 2004. In particular, by using the concept of local Hurst exponent, we point out how the behaviour of this parameter, usually considered as a benchmark for persistency/antipersistency recognition in time series, is largely time-scale dependent in the market context. These findings are a direct consequence of the intrinsic complexity of a system where trading strategies are scale-adaptive. Moreover, our analysis points out different regimes in the dynamical behaviour of the market indices under consideration.     

Dispersive Calculation of the Massless Multi-loop Sunrise Diagram

The massless sunrise diagram with an arbitrary number of loops is calculated in a simple but formal manner. The result is then verified by rigorous mathematical treatment. Pitfalls in the calculation with distributions are highlighted and explained. The result displays the high energy behaviour of the massive sunrise diagrams, whose calculation is involved already for the two-loop case.     

Relation between grain shape and fractal properties in random Apollonian packing with grain rotation

We extend the common theme of random Apollonian packing of circles to consider orientable grains with a noncircular shape. Systems of up to 10(6) grains are examined for a range of polygonal and elliptical shapes using both the random Apollonian packing model and the new rotational random Apollonian packing model which takes into account the extra rotational degree of freedom of noncircular grains. We identify the constraining length D_c that limits growth of the grain during the packing process and find that a universal relation exists between grain shape and the scaling properties of the system.