There are at least two ways to interpret numerical degrees of belief in terms of betting: (1) you can offer to bet at the odds defined by the degrees of belief, or (2) you can judge that a strategy for taking advantage of such betting offers will not multiply the capital it risks by a large factor. Both interpretations can be applied to ordinary additive probabilities and used to justify updating by conditioning. Only the second can be applied to Dempster-Shafer degrees of belief and used to justify Dempster's rule of combination.

A client-server architecture to simultaneously solve multiple learning tasks from distributed datasets is described. In such architecture, each client is associated with an individual learning task and the associated dataset of examples. The goal of the architecture is to perform information fusion from multiple datasets while preserving privacy of individual data. The role of the server is to collect data in real-time from the clients and codify the information in a common database. The information coded in this database can be used by all the clients to solve their individual learning task, so that each client can exploit the informative content of all the datasets without actually having access to private data of others. The proposed algorithmic framework, based on regularization theory and kernel methods, uses a suitable class of mixed effect kernels. The new method is illustrated through a simulated music recommendation system.

Human decisional processes result from the employment of selected quantities of relevant information, generally synthesized from environmental incoming data and stored memories. Their main goal is the production of an appropriate and adaptive response to a cognitive or behavioral task. Different strategies of response production can be adopted, among which haphazard trials, formation of mental schemes and heuristics. In this paper, we propose a model of Boolean neural network that incorporates these strategies by recurring to global optimization strategies during the learning session. The model characterizes as well the passage from an unstructured/chaotic attractor neural network typical of data-driven processes to a faster one, forward-only and representative of schema-driven processes. Moreover, a simplified version of the Iowa Gambling Task (IGT) is introduced in order to test the model. Our results match with experimental data and point out some relevant knowledge coming from psychological domain.

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.

I introduce an algorithm for estimating parameters from multidimensional data based on forward modelling. In contrast to many machine learning approaches it avoids fitting an inverse model and the problems associated with this. The algorithm makes explicit use of the sensitivities of the data to the parameters, with the goal of better treating parameters which only have a weak impact on the data. The forward modelling approach provides uncertainty (full covariance) estimates in the predicted parameters as well as a goodness-of-fit for observations. I demonstrate the algorithm, ILIUM, with the estimation of stellar astrophysical parameters (APs) from simulations of the low resolution spectrophotometry to be obtained by Gaia. The AP accuracy is competitive with that obtained by a support vector machine. For example, for zero extinction stars covering a wide range of metallicity, surface gravity and temperature, ILIUM can estimate Teff to an accuracy of 0.3% at G=15 and to 4% for (lower signal-to-noise ratio) spectra at G=20. [Fe/H] and logg can be estimated to accuracies of 0.1-0.4dex for stars with G<=18.5. If extinction varies a priori over a wide range (Av=0-10mag), then Teff and Av can be estimated quite accurately (3-4% and 0.1-0.2mag respectively at G=15), but there is a strong and ubiquitous degeneracy in these parameters which limits our ability to estimate either accurately at faint magnitudes. Using the forward model we can map these degeneracies (in advance), and thus provide a complete probability distribution over solutions. (Abridged)

The RADAR project developed a large multi-agent system with a mixed-initiative user interface designed to help office workers cope with email overload. Most RADAR agents observe experts performing tasks and then assist other users who are performing similar tasks. The interaction design for RADAR focused on developing user interfaces that allowed the intelligent functionality to improve the user's workflow without frustrating the user when the system's suggestions were either unhelpful or simply incorrect. A large evaluation of RADAR demonstrated that novice users confronted with an email overload test performed significantly better, achieving a 37% better overall score when assisted by RADAR.

This case study article describes the iterative design process of an adaptive, mixed-initiative calendaring tool with embedded artificial intelligence. We establish the specific types of assistance in which the target user population expressed interest, and we highlight our findings regarding the scheduling practices and the reminding preferences of these users. These findings motivated the redesign and enhancement of our intelligent system. Lessons learned from the study--namely, highlighting the merits of usability toward widespread adoption and retention, and that simple problems that perhaps do not necessitate complex AI-based solutions should not go unattended merely due to their inherent simplicity--conclude the article, along with a discussion of the importance of the iterative design process for any user adaptive system.

The conference Robotics: Science and Systems was held at the Swiss Federal Institute of Technology (ETH) in Zurich Switzerland, from June 25 to June 28, 2008. More than 280 international researchers attended this single track conference to learn about the most exciting robotics research and most advanced robotic systems. The program committee, led by sixteen area chairs, selected 40 papers out of 163 submissions. The program also included seven invited talks and two early career spotlight presentations. The plenary presentations were complemented by thirteen workshops. 

The development of intelligent environments is considered an important step toward the realization of the ambient intelligence vision. Greece, served as program chairs. The previous four editions of the IE conference have been held at the University of Essex, UK (in 2005), at the National Technical University of Athens, Greece (in 2006), at the University of Ulm, Germany (in 2007), and at the University of Washington campus in Seattle, Washington, USA (in 2008). The development of intelligent environments is About 120 delegates attended the workshops considered the first and primary step toward the and the conference. These included representatives realization of the ambient intelligence vision.

Although AI and HCI explore computing and intelligent behavior and the fields have seen some cross-over, until recently there was not very much. This article outlines a history of the fields that identifies some of the forces that kept the fields at arm’s length. AI was generally marked by a very ambitious, long-term vision requiring expensive systems, although the term was rarely envisioned as being as long as it proved to be, whereas HCI focused more on innovation and improvement of widely-used hardware within a short time-scale. These differences led to different priorities, methods, and assessment approaches.  A consequence was competition for resources, with HCI flourishing in AI winters and moving more slowly when AI was in favor. The situation today is much more promising, in part because of platform convergence: AI can be exploited on widely-used systems.