How the cognitive capabilities of humans are mapped on the underlying computational framework is far to be completely understood. Despite it is an open problem, some evidences show that the interaction among the cortex, the amygdala and the thalamus is involved in the composition of elementary behaviours to solve complex tasks. The cortex is well-known to have a certain degree of plasticity to categorise the processed information whereas the other two brain areas are involved in the association of an emotional state to the incoming sensory information. In the specific, the amygdala is involved in the generation of innate affective states whereas the thalamus deals with the developing of new goals. The interaction among these three brain structures plays an important role in the emerging of a middle level of cognition. We claim that the cognitive model could fill the gap between the high-level reasoning and low level sensory-motor coordination. In this paper, firstly we model the above mentioned brain areas and second we investigate the interaction of these areas, producing a goal-generating architecture. Finally, the goal-generating system is tested on a NAO robot, in order to validate and extend some previously published results.

Encouraged by recent legislations all over the world, aimed to protect human health and environment, in silico techniques have proved their ability to assess the toxicity of chemicals. However, they act often like a black-box, without giving a clear contribution to the scientific insight; such over-optimized methods may be beyond understanding, behaving more like competitors of human experts' knowledge, rather than assistants. In this work, a new Structure-Activity Relationship (SAR) approach is proposed to mine molecular fragments that act like structural alerts for biological activity. The entire process is designed to fit with human reasoning, not only to make its predictions more reliable, but also to enable a clear control by the user, in order to match customized requirements. Such an approach has been implemented and tested on the mutagenicity endpoint, showing marked prediction skills and, more interestingly, discovering much of the knowledge already collected in literature as well as new evidences. The achieved tool is a powerful instrument for both SAR knowledge discovery and for activity prediction on untested compounds.

Five years ago, this journal published an obituary of Professor Marco Somalvico (1941–2002). This was in Vol. 34, No. 4–5, 2003, pp. 409–411. It appeared inside a special issue guest-edited by Ephr...

In this paper we present a human-oriented approach to the study of the biped gait for a humanoid robot. Starting from the analysis of the human lower-limbs, we figured out which features of the human legs are fundamental for a correct walking motion and can be adopted in the mechanical design of a humanoid robot. In particular we focus here on the knee, designed as a compliant human-like knee instead of a classical pin-joint. For the foot we tried to reproduce in a simple mechanical device the mobility and lightness of the human foot, which is very different from a flat surface and has a big impact on walking. We complete the presentation with considerations about the energy consumption of our humanoid design. In our approach the robot gains in adaptability and energetic efficiency, which are the most challenging issues for a biped robot.

In many real-world applications simple classifiers are too weak to have predictive power. Ensemble techniques, or mixture of experts, are a possible solution. We illustrate why mixture of experts are a natural choice in domains such as the prediction of environmental toxicity for chemicals, when a structural approach is pursued. The real data here used are derived from peer reviewed experiments, and are publicly available, but are difficult to model. We used them to predict aquatic toxicity for fish. Chemical information was coded into a set of about 160 descriptors; after reducing the dimensions of the feature vector through different techniques, we developed multivariate regression to build a model of the toxic effects of chemicals. Defining toxicity as a category, as in European Union (EU) regulations, we extended the study to predict toxicity class. Problems with poor predictive power of this simple approach have led us to reconsider the problem from a more theoretical angle. We have respected locality criterion to build different local classifiers, one for each chemical class, to achieve better results. Then we combined the classifiers to get a complete system to predict any chemical for the chemical classes studied.

The goal of toxicity prediction is to describe the relationship between chemical properties, on the one hand, and biological and toxicological processes, on the other. Knowledge about the causes of toxicity is incomplete. No single property can satisfy the requirement to model the toxic activity. In this study, we consider a different method, viz. building up models that are useful for aquatic toxicity prediction. Our study is in the tradition of SAR (structure-activity relationship) and QSAR (quantitative SAR) methods, but it tries to predict a category. Due to the variability of the toxicity phenomenon, classification methods may have advantages, because they refer to intervals of the observed toxic effect. Furthermore, the classification of compounds according to their toxicity has a direct application to the regulation of chemicals. In this paper, we report results obtained from the preparation and study of a data set of different classes of chemicals. Starting from recursive partitioning algorithms, we test their results against clustering and classifiers.

We introduce POINTY, an interactive system for constructing world-model-based programs for robots. POINTY combines an interactive programming environment with the teaching-by-guiding methodology that has been successful in industrial robotics. Owing to its ability to control robots in real time, and to interact with the user, POINTY provides a friendly and powerful programming environment for robot applications. In the past few years, POINTY has been in use at Stanford to write, test, and debug various robot programs.