Extending BDI plan selection to incorporate learning from experience

An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. We describe a novel BDI execution framework that models context conditions as decision trees, rather than boolean formulae, allowing agents to learn the probability of success for plans based on experience. By using a probabilistic plan selection function, the agents can balance exploration and exploitation of their plans. We extend earlier work to include both parameterised goals and recursion and modify our previous approach to decision tree confidence to include large and even non-finite domains that arise from such consideration. Our evaluation on a pre-existing program that relies heavily on recursion and parametrised goals confirms previous results that naive learning fails in some circumstances, and demonstrates that the improved approach learns relatively well

Istituzioni ecclesiastiche e potere regio nel mediterraneo medievale. Scritti per Salvatore Fodale

Il volume è un omaggio degli allievi al professore Salvatore Fodale. Il filo conduttore sono le Istituzioni ecclesiastiche e il potere regio tra l’età normanna e la fine del Medioevo. Uno spazio particolare è dedicato alla città di Messina, al centro dei saggi sulla religiosità tra Oriente e Occidente e su Raimondo Puyolis, arcivescovo catalano nel Trecento. Il rapporto tra Istituzioni ecclesiastiche e potere regio emerge negli articoli su Papato e propaganda di crociata nel Duecento e sul monastero di Santa Maria del Bosco di Calatamauro. Viene, inoltre, esaminata la complessa relazione tra Martino il Giovane e la comunità ebraica siciliana. Il regno di Alfonso il Magnanimo fa da sfondo ai saggi sul memoriale della Camera reginale, al tempo della moglie Maria di Castiglia, e sulla Descendencia dominorum Regni Sicilie di Pau Rossell, codice commissionato a Valencia tra il 1437 e il 1438. In un mondo a parte sembrano vivere i La Grua, baroni di Carini, non inseriti nel sistema di potere della monarchia

Integrating Learning into a BDI Agent for Environments with Changing Dynamics

We propose a framework that adds learning for improving plan selection in the popular BDI agent programming paradigm. In contrast with previous proposals, the approach given here is able to scale up well with the complexity of the agent's plan library. Technically, we develop a novel confidence measure which allows the agent to adjust its reliance on the learning dynamically, facilitating in principle infinitely many (re)learning phases. We demonstrate the benefits of the approach in an example controller for energy management

Casein haplotype variability in Sicilian dairy goat breeds

In the Mediterranean region, goat milk production is an important economic activity. In the present study, 4 casein genes were genotyped in 5 Sicilian goat breeds to 1) identify casein haplotypes present in the Argentata dell'Etna, Girgentana, Messinese, Derivata di Siria, and Maltese goat breeds; and 2) describe the structure of the Sicilian goat breeds based on casein haplotypes and allele frequencies. In a sample of 540 dairy goats, 67 different haplotypes with frequency >or=0.01 and 27 with frequency >or=0.03 were observed. The most common CSN1S1-CSN2-CSN1S2-CSN3 haplotype for Derivata di Siria and Maltese was FCFB (0.17 and 0.22, respectively), whereas for Argentata dell'Etna, Girgentana and Messinese was ACAB (0.06, 0.23, and 0.10, respectively). According to the haplotype reconstruction, Argentata dell'Etna, Girgentana, and Messinese breeds presented the most favorable haplotype for cheese production, because the casein concentration in milk of these breeds might be greater than that in Derivata di Siria and Maltese breeds. Based on a cluster analysis, the breeds formed 2 main groups: Derivata di Siria, and Maltese in one group, and Argentata dell'Etna and Messinese in the other; the Girgentana breed was between these groups but closer to the latter

Applications of Monte Carlo methods to special radiotherapeutic techniques

Monte Carlo (MC) methods are considered one of the most powerful and precise approaches to study and solve medical physics issues. They, indeed, can be applied in all the situations where to use deterministic algorithms is infeasible or impossible. Surprising improvements in computer technology have promoted a wide diffusion of this technique, giving rise to the born of several Monte Carlo codes, such as the GEANT4 toolkit. In this paper we show some of the applications we developed using GEANT4. In particular, the simulation of two different radiotherapy techniques, such as proton/ion therapy and stereotactic radiosurgery will be discussed. In the first case we show the main features of our last public version of the GEANT4 Hadrontherapy program, also discussing the issues related to the nuclear fragmentation. In the second case, we show the procedures followed for the simulation of a Gamma Knife device, in order to validate the Treatment Planning System (TPS) used for the dose computation