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In this paper we discuss the new version of PITP, a proce- dure to decide propositional intuitionistic logic, which turns out at the moment to be the best propositional prover on ILTP. The changes in the strategy and implementation make the new version of PITP faster and capable of deciding more formulas than the previous one. We give a short account both of the old optimizations and the changes in the strategy with respect to the previous version. We use ILTP library and random generated formulas to compare the implementation described in this paper to the other provers (including our old version of PITP).

Cloud/Grid systems are composed of nodes that individually manage local resources, and when a client request is submitted to the system, it is necessary to find the most suitable nodes to satisfy that request. In a competitive scenario, each node is in competition with each other to obtain the assignment of available Tasks. In such a situation, it is possible that a node, in order to obtain the Assignment of a task, can lie when declaring its own capability. Therefore, lying nodes will need to require the collaboration of other nodes to complete the task and consequently the problem arises of finding the most promising collaborators. In such a context, to make effective this selection, each node should have a trust model for accurately choosing its interlocutors. In this paper, a trust-based approach is proposed to make a node capable of finding the most reliable interlocutors. This approach, in order to avoid the exploration of the whole node space, exploits a P2P resource finding approach for clouds/grids, capable of determining the admissible region of nodes to be considered for the search of the interlocutors.

The modelling of biochemical systems requires the knowledge of several quantitative parameters (e.g. reaction rates) which are often hard to measure in laboratory experiments. Furthermore, when the system involves small numbers of molecules, the modelling approach should also take into account the effects of randomness on the system dynamics. In this paper, we tackle the problem of estimating the unknown parameters of stochastic biochemical systems by means of two optimization heuristics, genetic algorithms and particle swarm optimization. Their performances are tested and compared on two basic kinetics schemes: the Michaelis-Menten equation and the Brussellator. The experimental results suggest that particle swarm optimization is a suitable method for this problem. The set of parameters estimated by particle swarm optimization allows us to reliably reconstruct the dynamics of the Michaelis-Menten system and of the Brussellator in the oscillating regime. © Springer-Verlag Berlin Heidelberg 2009.

Given a computational model , and a "reasonable" encoding function that encodes any computation device M of as a finite bit string, we define the description size of M (under the encoding ) as the length of . The description size of the entire class (under the encoding ) can then be defined as the length of the shortest bit string that encodes a universal device of . In this paper we propose the description size as a complexity measure that allows to compare different computational models. We compute upper bounds to the description size of deterministic register machines, Turing machines, spiking neural P systems and UREM P systems. By comparing these sizes, we provide a first partial answer to the following intriguing question: what is the minimal (description) size of a universal computation device?

In this paper we describe the ontological model developed within the NEUROWEB project, a consortium of four European excellence centers for the diagnosis and treatment of the Ischemic Stroke pathology. The aim of the project is the development of a support system for association studies, intended as the search for statistical correlations between a feature (e.g., a genotype) and the clinical phenotype. Clinical phenotypes are assessed through the diagnostic activity, performed by clinical experts operating within different neurovascular sites. These sites operate according to specific procedures, but they also conform to the minimal requirements of international guidelines, displayed by the adoption of a common standard for the patient classification. We developed a central model for the clinical phenotypes (the NEUROWEB Reference Ontology), able to reconcile the different methodologies into a common classificatory system. To support the integrative analysis of genotype-phenotype relations, the Reference Ontology was extended to handle concepts of the genomic world. We identified the general theory of biological function as the common ground between the clinical medicine and molecular biosciences; therefore we decomposed the clinical phenotypes into elementary phenotypes with a homogeneous physiological background, and we connected them to the biological processes, acting as the elementary units of the genomic world.

The importance of mutual monitoring in recommender systems based on learning agents derives from the consideration that a learning agent needs to interact with other agents in its environment in order to Improve its individual performances. In this paper we present a novel framework, called EVA, that introduces a strategy to improve the performances of recommender agents based on a dynamic computation of the agent's reputation. Some preliminary experiments on real users show that our approach, implemented on the top of some well-known recommender systems, introduces significant improvements in terms of effectiveness.

he research context of this paper refers to bottom-up approaches to crowd dynamics that is, the study of how and where crowds form and move [1]. Several phenomena like crowd aggregation, dispersion and self-organized movement have been observed and studied by multiple disciplines interested to crowds (e.g. physics, sociology, ethology, social and behavioral psychology, building design, urban planning, security management, among others), each one with its specific viewpoint and ontological setting. SCA4CROWDS is an interdisciplinary research within this context that aims at contributing towards the development of a unifying ontology on crowds allowing the integration of contributions coming from several disciplines and that could be exploited for scientific and applicative issues (e.g. model comparison, validation, calibration). Potential exploitations of SCA4CROWDS results are towards the support of design and management of public crowded spaces and events to improve security, safety and comfort of people. SCA4CROWDS, in particular, aims at developing formal and computational tools to support the design, execution and analysis of crowds' behavior as effect of individual interactions (e.g. physical, social, emotional) according to Situated Cellular Agent (SCA) [2]. SCA is a modeling and simulation framework to model and study crowd dynamics phenomena with an approach based on Multi-Agent Systems (MAS) and Cellular Automata [3] principles. © 2008 Springer-Verlag Berlin Heidelberg.

This paper presents a research in the context of pedestrian dynamics according to Situated Cellular Agent (SCA), a Multi-Agent Systems approach whose roots are on Cellular Automata (CA). The aim of this work is to apply Genetic Programming (GP) approach, a well known Machine Learning method belonging to the family of Evolutionary Algorithms, to generate suitable behavioral rules for pedestrians in an evacuation scenario. The main contribution of this work is in the design of a testset of GP generated behaviors to represent basic behavioral models of evacuees populating a only locally known environment, a typical scenario for CA-based models. © 2008 Springer-Verlag Berlin Heidelberg.

This paper aims to present an architectural model designed to manage the information related to a multi-functional structure offering various types of services through an access control framework. It describes several implementation details of our prototype focusing on the emerging technologies based on radio frequency identification approaches exploited to reveal the presence of customers inside a multi-functional area.

The WWW is actually the most dynamic and attractive information exchange place. Finding useful information is hard due to huge data amount, varied topics and unstructured contents. In this paper we present a web browsing support system that proposes personalized contents. It is integrated in the content management system and it runs on the server hosting the site. It processes periodically site contents, extracting vectors of the most significant words. A topology tree is defined applying hierarchical clustering. During online browsing, viewed contents are processed and mapped in the vector space previously defined. The centroid of these vectors is compared with the topology tree nodes’ centroids to find the most similar; its contents are presented to the user as link suggestions or dynamically created pages. Personal profile is saved after every session and included in the analysis during same user’s subsequent visits, avoiding the cold start problem

This paper illustrates a Knowledge Management framework developed in the context of the P-Truck Project, that aims to support the design and manufacturing of innovative products in a restricted and specific domain (i.e. the design and manufacturing of truck tires) through a Knowledge Based System approach. The domain is characterized by heterogenous knowledge, that can be difficulty captured with generic knowledge engineering methodologies and tools. Thus, a dedicated Knowledge Elicitation tool (KEPT) has been designed and implemented into the P-Truck system for this task. The main feature of KEPT is the possibility to manage the heterogeneous knowledge concerning the different phases of the production process in a centralized fashion, with benefits from the knowledge maintenance and sharing standpoints. Moreover, KEPT allows to support the different types of knowledge based systems (i.e. rule-based and case-based) that within the P-Truck system support domain experts in handling tire design and production.

P systems are computational models inspired by some biological features of the structure and the functioning of real cells. In this paper we introduce a new kind of communication between membranes, based upon the natural budding of vesicles in a cell. We de-ne the operations of gemmation and fusion of mobile membranes, and we use membrane structures and rules over strings of biological inspiration only. We prove that P systems of this type can generate all recursively enumerable languages and, moreover, the Hamiltonian Path Problem can be solved in a quadratic time. Some open problems are also formulated

Cellular Automata model is a powerful instrument used in many applications. In this paper we present a Reactive Path-Planning Algorithm for a non-holonomic robot moving on a 2D surface based on Multilayered Cellular Automata. The robot considered has a preferential motion direction and has to move using smoothed trajectories, without stopping and turning in place, and with a minimum steering radius. We have implemented a new algorithm based on a directional (anisotropic) propagation of repulsive and attracting potential values in a Multilayered Cellular Automata model. The algorithm finds all the optimal trajectories following the minimum valley of a potential hypersurface embedded in a 4D space, built respecting the imposed constraints. Our approach results to be distributed and incremental: whenever changing the initial or the final pose, or the obstacles distribution, the automata start evolving towards a new global steady state, looking for a new set of solutions. Because it reacts to obstacles distribution changes, it can be also used in unknown or dynamical environments in combination with a world modeler. The path-planning algorithm is applicable on a wide class of vehicles kinematics, selected changing a set of weights