Yan Han and Koubao Ding (April 1st 2010). Advanced Simulation for ESD Protection Elements, Advances in Solid State Circuit Technologies, Paul K Chu, IntechOpen, DOI: 10.5772/8637. 

Lelio R. Soares Jr. and Victor H. Casanova Alcalde (April 1st 2010). A Modeling and Simulation Platform for Robot Kinematics Aiming Visual Servo Control, Visual Servoing, Rong-Fong Fung, IntechOpen, DOI: 10.5772/8551. 

André Filipe de Moraes Batista, Maria das Graças Bruno Marietto, Wagner Tanaka Botelho, Guiou Kobayashi, Brunno dos Passos Alves, Sidney de Castro and Terry Lima Ruas (April 1st 2011). Principles of Agent-Oriented Programming, Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications, Faisal Alkhateeb, Eslam Al Maghayreh and Iyad Abu Doush, IntechOpen, DOI: 10.5772/14248.

New Zealand

AAMAS 2020

Exploiting Simulation For Mass Programming and Engineering - The JaCaMo-SIM Platform

 **RTSS 2021** has successfully concluded. Hope you have enjoyed it, and look forward to seeing you next year in person.
    Announcement for the Awards:
 * Best Paper Award goes to: Monte Carlo Response-Time Analysis, Sergey Bozhko, Georg von der Brüggen, and Björn Brandenburg
 * Best Student Paper Award goes to: Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems, Mohsen Shekarisaz, Lothar Thiele, and Mehdi Kargahi.
 * Best Solution Award for Industry Session goes to: Toward Real-Time Guaranteed Scheduling for Autonomous Driving Systems. Jinghao Sun, Tianyi Wang, Kailu Duan, Bin Lu, Jiankang Ren, Zhishan Guo, and Guozhen Tan.



IEEE RTSS 2021

The IEEE Real-Time Systems Symposium (RTSS) is the premier conference in the field of real-time systems, and is a venue for researchers and practitioners to showcase innovations covering all aspects of real-time systems, including theory, design, analysis, implementation, evaluation, and experience. 

technical paper

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). 

Learning Correlation Functions on Mixed Data Sequences for Computer Architecture Applications

Personal Agents (PAs) have longly been explored as assistants to support users in their daily activities. Surprisingly, few works refer to the adoption of PAs in the healthcare domain, where they can assist physicians’ activities reducing medical errors. Although literature proposes different approaches for modelling and engineering PAs, none of them discusses how they can be integrated with cognitive services in order to empower their reasoning capabilities. In this paper we present an integration model, specifically devised for healthcare applications, that enhances Belief-Desire-Intention agents rea- soning with advanced cognitive capabilities. As a case study, we adopt this integrated model in the critical care path of trauma resuscitation, stepping forward to the vision of Smart Hospitals.

Complementing Agents with Cognitive Services: a case study in Healthcare

The collection of large-scale people opinions through discussion support system is an emerging approach for SDGs cooperation that aims to solve wicked problems in the 21st century by facilitating the discussion and making collective decision. Previous research centered on gathering large-scale people opinions considering a stable society like Japan. Here, we focus on usage and application of crowd discussion support system into fragile and conflict-affected city like Kabul, Afghanistan. Our paper is especially focused on the international experiment transfer of “D-Agree” usage and application for town meeting and city issues in five selected district of Kabul city, being presented with an opportunity of experimental transfer by Industry-Academia-Government collaboration. Our action research is based on the soft system methodology utilizing online crowd discussion system in which people can discuss their municipal district issues online. The content includes the background, process and formalization of the collaboration and reports on the outline of the societal experiment of D-Agree with public citizens officials at KM and private citizen in selected 5 districts of Kabul city. The final objective of our study is to evaluate facilitation of citizens discussion to district issues and evaluate AI-based facilitation of our system.

Usage & application of AI-based facilitation discussion support system for city issues and town meeting: Kabul experimental view

One of the fundamental challenges of governance is deciding when and how to intervene in multi-agent systems in order to impact group-wide metrics of success. This is particularly challenging when proposed interventions are novel and expensive. For example, one may wish to modify a building’s layout to improve the efficiency of its escape route. Evaluating such interventions would generally require access to an elaborate simulator, which must be constructed ad-hoc for each environment, and can be prohibitively costly or inaccurate. Here we examine a simple alternative: Optimize By Observational Extrapolation (OBOE). The idea is to use observed behavioural trajectories, without any interventions, to learn predictive models mapping environment states to individual agent outcomes, and then use these to evaluate and select changes. We evaluate OBOE in socially complex gridworld environments and consider novel physical interventions that our models were not trained on. We show that neural network models trained to predict agent returns on baseline environments are effective at selecting among the interventions. Thus, OBOE can provide guidance for challenging questions like: “which wall should I tear down in order to minimize the Gini index of this group?”

Should I tear down this wall? Optimizing social metrics by evaluating novel actions

Automatic Classification of Freely-Offered Opinions

Explain how to extract discussion structure which is not abstract

Extracting Discussion Structures for Automated Facilitation Agents

Researchers have long been interested in the role that norms can play in governing agent actions in multi-agent systems. Much work has been done on formalising normative concepts from human society and adapting them for the government of open software systems, and on the simulation of normative processes in human and artificial societies. However, there has been comparatively little work on applying normative MAS mechanisms to understanding the norms in human society. This work investigates this issue in the context of international politics. Using the GDELT dataset, containing machine-encoded records of international events extracted from news reports, we extracted bilateral sequences of inter-country events and applied a Bayesian norm mining mechanism to identify norms that best explained the observed behaviour. A statistical evaluation showed that the normative model fitted the data significantly better than a probabilistic discrete event model.

Mining International Political Norms from the GDELT Database

This paper proposes an approach that aims to extract online discussions structure and to reduce the extraction model size simultaneously. The ultimate goal is the development of an automated facilitation agent that automatically facilitates online discussions. In large-scale online discussions, it becomes necessary to have a facilitator. However, facilitates large-scale online discussions with hundreds or thousands of participants is an intricate problem, and in dynamic discussions, it be- comes impossible for human facilitators to keep an eye on the flow of discussions. As a result, we propose the development of an automated facilitator agent that automatically facilitates online discussions. In order for the automated facilitation agent to function properly, it needs to dynamically extract the structure of online discussions. Towards this end, we use Bidirectional Encoder Representative from Transformers(BERT) and deep neural networks for extracting the structure of online discus- sions. In order to evaluate the proposed approach, we use a dataset based on extracting different posts from a large-scale online discussion system called D-Agree. The experimental results show that the proposed approach is efficient for extracting the structure of online discussions where the link extraction model size is much smaller than the existing methods.

Deep Bidirectional Transformers for Online Discussion Understanding

The enactment of dynamic workflows may take advantage of the multi-agent system paradigm. The approach presented in this pa- per allows exploiting a high-level BPMN definition to generate several agent organisations that can enact the BPMN workflow. The availabil- ity of different solutions (i.e. organisations) enables the optimisation and adaptation features of the approach. The mapping of the initial workflow to organisations starts with the automatic generation of goals from the BPMN, and it exploits a metamodeling approach to generate MOISE organisation definitions.

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Learning Correlation Functions on Mixed Data Sequences for Computer Architecture Applications

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