Michael Mayo, Peter Pfeifer and Chen Hou (March 7th 2012). Reverse-Engineering the Robustness of Mammalian Lungs, Reverse Engineering - Recent Advances and Applications, Alexandru C. Telea, IntechOpen, DOI: 10.5772/34479. 

Ana Estanqueiro (June 1st 2010). The Future Energy Mix Paradigm: How to Embed Large Amounts of Wind Generation While Preserving the Robustness and Quality of the Power Systems?, Wind Power, S M Muyeen, IntechOpen, DOI: 10.5772/8359. 

Vladimir Shikhin (January 16th 2019). Introductory Chapter: Major Automatic Control Challenges in Multi-Agent Systems, Multi-Agent Systems - Control Spectrum, Vladimir Shikhin, IntechOpen, DOI: 10.5772/intechopen.83450. 

New Zealand

Robustness is an important property of software systems, and the availability of proper feedback is seen as crucial to obtain it, especially in the case of systems of distributed and interconnected components.
Multiagent Systems (MAS) are valuable for conceptualizing and implementing distributed systems, but the current design methodologies for MAS fall short in addressing robustness in a systematic way at design time.
In this paper we outline our vision of how robustness in MAS can be granted as a design property. To this end, we exploit the notion of accountability as a mechanism for building reporting frameworks and, then, we describe how robustness is gained.
We exemplify our vision on the JaCaMo agent platform.

AAMAS 2020

Fragility and Robustness in Multiagent Systems

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Fragility and Robustness in Multiagent Systems

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