Multilanguage Semantic Interoperability in Distributed Applications

JOSI is a software framework that tries to simplify the development of such kinds of applications both by providing the possibility of working on models for representing such semantic information and by offering some implementations of such models that can be easily used by software developers without any knowledge about semantic models and languages. This software library allows the representation of domain models through Java interfaces and annotations and then to use such a representation for automatically generating an implementation of domain models in different programming languages (currently Java and C++). Moreover, JOSI supports the interoperability with other applications both by automatically mapping the domain model representations into ontologies and by providing an automatic translation of each object obtained from the domain model representations in an OWL string representation

Unsupervised Continual Learning From Synthetic Data Generated with Agent-Based Modeling and Simulation: A preliminary experimentation

Continual Learning enables to learn a variable number of tasks sequentially without forgetting knowledge obtained from the past. Catastrophic forgetting usually occurs in neural networks for their inability to learn different tasks in sequence since the performance on the previous tasks drops down in a significant way. One way to solve this problem is providing a subset of the previous examples to the model while learning a new task. In this paper we evaluate the continual learning performance of an unsupervised model for anomaly detection by generating synthetic data using an Agent-based modeling and simulation technique. We simulated the movement of different types of individuals in a building and evaluate their trajectories depending on their role. We collected training and test sets based on their trajectories. We included, in the test set, negative examples that contain wrong trajectories. We applied a replay-based continual learning to teach the model how to distinguish anomaly trajectories depending on the users’ roles. The results show that using ABMS synthetic data it is enough a small percentage of synthetic data replay to mitigate the Catastrophic Forgetting and to achieve a satisfactory accuracy on the final binary classification (anomalous / non-anomalous)

High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation

This paper presents an approach for the modeling and the simulation of the spreading of COVID-19 based on agent-based modeling and simulation (ABMS). Our goal is not only to support large-scale simulations but also to increase the simulation resolution. Moreover, we do not assume an underlying network of contacts, and the person-to-person contacts responsible for the spreading are modeled as a function of the geographical distance among the individuals. In particular, we defined a commuting mechanism combining radiation-based and gravity-based models and we exploited the commuting properties at different resolution levels (municipalities and provinces). Finally, we exploited the high-performance computing (HPC) facilities to simulate millions of concurrent agents, each mapping the individual’s behavior. To do such simulations, we developed a spreading simulator and validated it through the simulation of the spreading in two of the most populated Italian regions: Lombardy and Emilia-Romagna. Our main achievement consists of the effective modeling of 10 million of concurrent agents, each one mapping an individual behavior with a high-resolution in terms of social contacts, mobility and contribution to the virus spreading. Moreover, we analyzed the forecasting ability of our framework to predict the number of infections being initialized with only a few days of real data. We validated our model with the statistical data coming from the serological analysis conducted in Lombardy, and our model makes a smaller error than other state of the art models with a final root mean squared error equal to 56,009 simulating the entire first pandemic wave in spring 2020. On the other hand, for the Emilia-Romagna region, we simulated the second pandemic wave during autumn 2020, and we reached a final RMSE equal to 10,730.11

A DHT-Based Multi-Agent System for Semantic Information Sharing. In

Abstract. This paper presents AOIS, a multi-agent system that supports the sharing of information among a dynamic community of users connected through the Internet thanks to the use of a well-known DHT-based peer-to-peer platform: BitTorrent. In respect to Web search engines, this system enhances the search through domain ontologies, avoids the burden of publishing the information on the Web and guaranties a controlled and dynamic access to the information. The use of agent technologies has made the realization of three of the main features of the system straightforward: i) filtering of information coming from different users, on the basis of the previous experience of the local user, ii) pushing of some new information that can be of interest for a user, and iii) delegation of access capabilities, on the basis of a reputation network, built by the agents of the system on the community of its users. The use of BitTorrent will allow us to offer the AOIS systems to the hundreds of millions of users that already share documents though the BitTorrent platform

Intelligent Agents for Human Behavior modeling as Support to operations

Goal of the present chapter is providing support to operations planning and management in complex scenarios. The authors are mainly focused on South Asia region, which is subject of experimental analysis by running an Intelligent Agents \u2014driven HLA Federation. Simulation of investments and operations over an asymmetric mission environment with several parties, insurgents, terrorists and dynamic social framework is the aim. The scenario has various degrees of freedom and M&S enables evaluation of human behavior evolution and socio-psychological aspects. The presented models include Computer Generated Forces (CGF) driven by Intelligent Agents (IAs) that represents not only units on the battlefield, but also people and interest groups (i.e. Middle Class, Nomads, Clans). The study is focused on Civil Military Co-operations (CIMIC) and Psychological Operations (PSYOPs). The simulation is based on specific architecture that involves various federates playing different roles. Verification, Validation and Accreditation (VV&A) has been applied along the whole life cycle of the research, in order to determine the correctness and effectiveness of the results. The paper proposes experimental results obtained during the dynamic test of the federations

Emotion-based analysis of programming languages on Stack Overflow

n/

Multi-Agents Corporate Memory Management System

International audienceThis paper presents an approach to design a multi-agent system managing a corporate memory in the form of a distributed semantic web and describes the resulting architecture. The system was designed during the CoMMA European project (Corporate MemoryManagement through Agents) and aims at helping users in the management of a corporate memory, facilitating the creation, dissemination, transmission and reuse of knowledge in an organisation. The implementation integrated several emerging technologies: multi-agents system technology (using the JADE FIPA-compliant platform), knowledge modelling and XML technology for information retrieval (using the CORESE semantic search engine) and machine learning techniques. Here, we describe the agent roles and interactions, we explain the design rationale for the agent societies and we discuss the configuration and implementation issues

Fine-Grained Agent-Based Modeling to Predict Covid-19 Spreading and Effect of Policies in Large-Scale Scenarios

Modeling and forecasting the spread of COVID-19 remains an open problem for several reasons. One of these concerns the difficulty to model a complex system at a high resolution (fine-grained) level at which the spread can be simulated by taking into account individual features such as the social structure, the effects of the governments’ policies, age sensitivity to Covid-19, maskwearing habits and geographical distribution of susceptible people. Agent-based modeling usually needs to find an optimal trade-off between the resolution of the simulation and the population size. Indeed, modeling single individuals usually leads to simulations of smaller populations or the use of meta-populations. In this article, we propose a solution to efficiently model the Covid-19 spread in Lombardy, the most populated Italian region with about ten million people. In particular, the model described in this paper is, to the best of our knowledge, the first attempt in literature to model a large population at the single-individual level. To achieve this goal, we propose a framework that implements: i. a scale-free model of the social contacts combining a sociability rate, demographic information, and geographical assumptions; ii. a multi-agent system relying on the actor model and the High-Performance Computing technology to efficiently implement ten million concurrent agents. We simulated the epidemic scenario from January to April 2020 and from August to December 2020, modeling the government’s lockdown policies and people’s maskwearing habits. The social modeling approach we propose could be rapidly adapted for modeling future epidemics at their early stage in scenarios where little prior knowledge is available

GARFIELD + RCo Digital Upgrade: a Modern Set-up for Mass and Charge Identification of Heavy Ion Reaction Products

An upgraded GARFIELD + Ring Counter (RCo) apparatus is presented with improved performances as far as electronics and detectors are concerned. On one side fast sampling digital read out has been extended to all detectors, allowing for an important simplification of the signal processing chain together with an enriched extracted information. On the other side a relevant improvement has been made in the forward part of the setup (RCo): an increased granularity of the CsI(Tl) crystals and a higher homogeneity in the silicon detector resistivity. The renewed performances of the GARFIELD + RCo array make it suitable for nuclear reaction measurements both with stable and with Radioactive Ion Beams (RIB), like the ones foreseen for the SPES facility, where the Physics of Isospin can be studied.Comment: 13 pages, 19 figures - paper submitted to Eur. Phys. J.