How to Complete Regulations in Multi-agent Systems

In this paper, we deal with regulations that may exist in multiagent systems in order to regulate agent behaviour. More precisely, we discuss two properties of regulations, consistency and ompleteness. After defining what consistency and completeness mean, we propose a way to consistently complete incomplete regulations. This contribution considers that regulations are expressed in a first order deontic logic. We will focus on particular regulations: information exchange policies

Detection and resolution of normative conflicts in multi-agent systems : a literature survey

Peer reviewedPostprin

Severity-sensitive norm-governed multi-agent planning

This research was funded by Selex ES. The software developed during this research, including the norm analysis and planning algorithms, the simulator and harbour protection scenario used during evaluation is freely available from doi:10.5258/SOTON/D0139Peer reviewedPublisher PD

Resilience, reliability, and coordination in autonomous multi-agent systems

Acknowledgements The research reported in this paper was funded and supported by various grants over the years: Robotics and AI in Nuclear (RAIN) Hub (EP/R026084/1); Future AI and Robotics for Space (FAIR-SPACE) Hub (EP/R026092/1); Offshore Robotics for Certification of Assets (ORCA) Hub (EP/R026173/1); the Royal Academy of Engineering under the Chair in Emerging Technologies scheme; Trustworthy Autonomous Systems “Verifiability Node” (EP/V026801); Scrutable Autonomous Systems (EP/J012084/1); Supporting Security Policy with Effective Digital Intervention (EP/P011829/1); The International Technology Alliance in Network and Information Sciences.Peer reviewedPostprin

A Framework for Formal Modeling and Analysis of Organizations

A new, formal, role-based, framework for modeling and analyzing both real world and artificial organizations is introduced. It exploits static and dynamic properties of the organizational model and includes the (frequently ignored) environment. The transition is described from a generic framework of an organization to its deployed model and to the actual agent allocation. For verification and validation of the proposed model, a set of dedicated techniques is introduced. Moreover, where most computational models can handle only two or three layered organizational structures, our framework can handle any arbitrary number of organizational layers. Henceforth, real-world organizations can be modeled and analyzed, as illustrated by a case study, within the DEAL project line. © Springer Science+Business Media, LLC 2007

COIN@AAMAS2015

COIN@AAMAS2015 is the nineteenth edition of the series and the fourteen papers included in these proceedings demonstrate the vitality of the community and will provide the grounds for a solid workshop program and what we expect will be a most enjoyable and enriching debate.Peer reviewe

Enhancing Smart-Home Environments using Magentix2

[EN] Multi-agent system paradigm has been envisioned as an appropriate solution for challenges in the area of smart-environments. Specifically, MAS add new capabilities such as adaption, reorganization, learning, coordination, etc. These features allow to deal with open issues in the context of smart-homes such as multi-occupancy, activity tracking or profiling activities and behaviors from multiple residents. In this paper, we present Magentix2 as a suitable MAS platform for the development of dynamic smart environments. Specifically, the use of Magentix2 (http://gti-ia.upv.es/sma/tools/magentix2/index.php) facilitates the management of the multiple occupancy in smart living spaces. Normative virtual organizations provide the possibility of defining a set of norms and organizational roles that facilitate the regulation and control of the actions that can be carried out by internal and external agents depending on their profile. Moreover, Magentix2 provides a tracing service to keep track of activities carried out in the system. We illustrate the applicability and benefits of Magentix2 in a set of scenarios in the context of smart-homes.This work is supported by the Spanish government grants PROMETEOII/2013/019,TIN2014-55206-R, TIN2015-65515-C4-1-R, H2020-ICT-2015-688095.Valero Cubas, S.; Del Val Noguera, E.; Alemany-Bordera, J.; Botti, V. (2017). Enhancing Smart-Home Environments using Magentix2. Journal of Applied Logic. 24:32-44. https://doi.org/10.1016/j.jal.2016.11.022S32442

Splee:A declarative information-based language for multiagent interaction protocols

The Blindingly Simple Protocol Language (BSPL) is a novel information-based approach for specifying interaction protocols that can be enacted by agents in a fully decentralized manner via asynchronous messaging. We introduce Splee, an extension of BSPL. The extensions fall into two broad categories: multicast and roles. In Splee, a role binding is information that is dynamically generated during protocol enactment, potentially as the content (payload) of communication between two agents. Multicast communication is the idea that a message is sent to a set of agents. The two categories of extensions are interconnected via novel features such as set roles (the idea that a role binding can be a set of agents) and subroles (the idea that agents playing a role must be a subset of agents playing another role). We give the formal semantics of Splee and give small model characterizations of the safety and liveness of Splee protocols. We also introduce the pragmatic idea of query attachments for messages. Query attachments take advantage of Splee's information-orientation, and can help restrict the information (parameter bindings) communicated in a message