SAJaS: enabling JADE-based simulations

Multi-agent systems (MAS) are widely acknowledged as an appropriate modelling paradigm for distributed and decentralized systems, where a (potentially large) number of agents interact in non-trivial ways. Such interactions are often modelled defining high-level interaction protocols. Open MAS typically benefit from a number of infrastructural components that enable agents to discover their peers at run-time. On the other hand, multi-agent-based simulations (MABS) focus on applying MAS to model complex social systems, typically involving a large agent population. Several MAS development frameworks exist, but they are often not appropriate for MABS; and several MABS frameworks exist, albeit sharing little with the former. While open agent-based applications benefit from adopting development and interaction standards, such as those proposed by FIPA, MABS frameworks typically do not support them. In this paper, a proposal to bridge the gap between MAS simulation and development is presented, including two components. The Simple API for JADE-based Simulations (SAJaS) enhances MABS frameworks with JADE-based features. While empowering MABS modellers with modelling concepts offered by JADE, SAJaS also promotes a quicker development of simulation models for JADE programmers. In fact, the same implementation can, with minor changes, be used as a large scale simulation or as a distributed JADE system. In its current version, SAJaS is used in tandem with the Repast simulation framework. The second component of our proposal consists of a MAS Simulation to Development (MASSim2Dev) tool, which allows the automatic conversion of a SAJaS-based simulation into a JADE MAS, and vice-versa. SAJaS provides, for certain kinds of applications, increased simulation performance. Validation tests demonstrate significant performance gains in using SAJaS with Repast when compared with JADE, and show that the usage of MASSim2Dev preserves the original functionality of the system. © Springer-Verlag Berlin Heidelberg 2015

Semantic Web Services for Multi-Agent Systems Interoperability

Agent-based technologies are often used including existing web services. The outputs of some services are also frequently used as inputs for other services, including other MAS. However, while agent-based technologies can be used to provide services, these are not described using the same semantic web technologies web services use, which makes it difficult to discover, invoke and compose them with web services seamlessly. In this paper, we analyse different agent-based technologies and how these can be described using extensions to OWL-S. Additionally, we propose an architecture that facilitates these services’ usage, where services of any kind can be registered and executed (semi-)automatically.The present work has been developed under the PIANISM Project (ANI|P2020 40125) and has received funding from FEDER Funds through NORTE2020 program and from National Funds through Fundação para a Ciência e a Tecnologia (FCT) under the project UID/EEA/00760/2019. Gabriel Santos is supported by national funds through FCT PhD studentship with reference SFRH/BD/118487/2016.info:eu-repo/semantics/publishedVersio

Socially and biologically inspired computing for self-organizing communications networks

The design and development of future communications networks call for a careful examination of biological and social systems. New technological developments like self-driving cars, wireless sensor networks, drones swarm, Internet of Things, Big Data, and Blockchain are promoting an integration process that will bring together all those technologies in a large-scale heterogeneous network. Most of the challenges related to these new developments cannot be faced using traditional approaches, and require to explore novel paradigms for building computational mechanisms that allow us to deal with the emergent complexity of these new applications. In this article, we show that it is possible to use biologically and socially inspired computing for designing and implementing self-organizing communication systems. We argue that an abstract analysis of biological and social phenomena can be made to develop computational models that provide a suitable conceptual framework for building new networking technologies: biologically inspired computing for achieving efficient and scalable networking under uncertain environments; socially inspired computing for increasing the capacity of a system for solving problems through collective actions. We aim to enhance the state-of-the-art of these approaches and encourage other researchers to use these models in their future work

Local scheduling in multi-agent systems: Getting ready for safety-critical scenarios

Multi-Agent Systems (MAS) have been supporting the development of distributed systems performing decentralized thinking and reasoning, automated actions, and regulating component interactions in unpredictable and uncertain scenarios. Despite the scientific literature is plenty of innovative contributions about resource and tasks allocation, the agents still schedule their behaviors and tasks by employing traditional general-purpose scheduling algorithms. By doing so, MAS are unable to enforce the compliance with strict timing constraints. Thus, it is not possible to provide any guarantee about the system behavior in the worst-case scenario. Thereby, as they are, they cannot operate in safety-critical environments. This paper analyzes the agents' local schedulers provided by the most relevant agent-based frameworks from a cyber-physical systems point of view. Moreover, it maps a set of agents' behaviors on task models from the real-time literature. Finally, a practical case-study is provided to highlight how such "MAS reliability" can be achieved

An Agents and Artifacts Metamodel Based E-Learning Model to Search Learning Resources

17th International Conference on Computational Science and its Applications (ICCSA) -- JUL 03-06, 2017 -- Trieste, ITALYWOS: 000451227300040In this paper, an e-learning model based on Agents and Artifacts (A&A) Metamodel to search learning resources from multiple sources is proposed. Multi agent system (MAS) based e-learning models with the same functionality are available in the literature. However, they are mostly developed as standalone systems that contain a single agent responsible for searching and retrieving learning resources. With the highly distributed nature of learning resources over multiple repositories, giving this responsibility to only one agent decreases scalability. The proposed model exploits the A&A Metamodel to overcome this issue. A&A Metamodel focuses on environment modeling in MAS design and models entities in the environment as artifacts, that are first class entities like agents. From the perspective of MAS based e-learning systems, learning resources are the main components in the environment that agents interact with. Thus, an efficient solution can be achieved with an e-learning model that searches learning objects by using an e-learning environment model based on A&A Metamodel. The proposed e-learning system is developed with Jason and the e-learning environment model is implemented with CArtAgO framework. Finally, current limitations and future directions of the proposed approach are discussed.Univ Trieste, Univ Perugia, Univ Basilicata, Monash Univ, Kyushu Sangyo Univ, Univ Minh

The DigForSim Agent Based Simulator of\ua0People Movements in Crime Scenes

Evidence analysis is one of the Digital Forensics tasks and involves examining fragmented incomplete knowledge and reasoning on it, in order to reconstruct plausible crime scenarios. After more than one year of activity within the DigForASP COST Action, the lack of real data about movements of people in crime scenes emerged as a major limitation to the need of testing the DigForASP prototypes that exploit Artificial Intelligence and Automated Reasoning for evidence analysis. In this paper we present DigForSim, an Agent Based Modeling and Simulation tool aimed at producing synthetic, controllable data on the movements of agents in the crime scene, in form of files logging the agents\u2019 position at given time points. These log files serve as benchmarks for the DigForASP reasoning prototypes