Deyu Lin, Quan Wang and Pengfei Yang (November 5th 2018). The Game Theory: Applications in the Wireless Networks, Game Theory - Applications in Logistics and Economy, Danijela Tuljak-Suban, IntechOpen, DOI: 10.5772/intechopen.79508. 

New Zealand

AAMAS 2020

Summer Internship Matching with Funding Constraints

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). 

technical paper

Autonomous Shape Formation and Morphing in a Dynamic Environment by a Swarm of Robots

Explainability in Human-Agent Systems

Computational Methods for Simulating Biased Agents

We call for attention to climate change research as a domain of application for multiagent technologies. The multiagent nature of climate change challenges and successful application of multiagent methods in decentralized power grid systems, market organization, and industrial engineering, could improve our ability to address decarbonization (climate change mitigation) and to deal with some unavoidable consequences of global warming (climate change adaptation). We review major challenges to which the community of multiagent systems can contribute, highlight open research problems and argue for the application of multiagent models and solution concepts in a variety of issues related to this global challenge.


Multiagent Climate Change Research

Ethics is inherently a multiagent concern. However, research on AI ethics today is dominated by work on individual agents: (1) how an autonomous robot or car may harm or (differentially) benefit people in hypothetical situations (the so-called trolley problems) and (2) how a machine learning algorithm may produce biased decisions or recommendations. The societal framework is largely omitted.

To develop new foundations for ethics in AI, we adopt a sociotechnical stance in which agents (as technical entities) help autonomous social entities or principals (people and organizations). This multiagent conception of a sociotechnical system (STS) captures how ethical concerns arise in the mutual interactions of multiple stakeholders. These foundations would enable us to realize ethical STSs that incorporate social and technical controls to respect stated ethical postures of the agents in the STSs. The envisioned foundations require new thinking, along two broad themes, on how to realize (1) an STS that reflects its stakeholders’ values and (2) individual agents that function effectively in such an STS.


New Foundations of Ethical Multiagent Systems

We present an argumentation-supported explanation generating system, called Schedule Explainer, that assists with makespan scheduling. Our stand-alone generic tool explains to a lay user why a resource allocation schedule is good or not, and offers actions to improve the schedule given the user’s constraints. Schedule Explainer provides actionable textual explanations via an interactive graphical interface. We illustrate our system with a proof-of-concept application tool in a nurse rostering scenario whereby a shift-lead nurse aims to account for unexpected events by rescheduling some patient procedures to nurses and is aided by the system to do so.


AI-assisted Schedule Explainer for Nurse Rostering

In this demo, we present a Consensus-based Group Decision Support System that makes use of a Multi-Agent Microservices approach. The proposed system comprises a Client App, an API Gateway and a set of Microservices where different Artificial Intelligence methods are implemented. It allows to create and manage Multiple Criteria Decision Problems and to support dispersed decisionmakers, allowing them to take advantage of the benefits associated to face-to-face Group Decision-Making processes.


A Consensus-based Group Decision Support System using a Multi-Agent MicroServices Approach

Agents that adapt to their user need to have knowledge of their user and expertise on how best to adapt to that type of user. In this paper we describe the addition of an agent’s expertise and collection of machine-learnt user profiles to the proposed extended FAtiMA (Fearnot AffecTive Mind Architecture) cognitive agent architecture. A study to evaluate the extended architecture is presented which compares the benefit (i.e. reduced stress and increased rapport) of tailoring dialogue (i.e. empathic or neutral) to the specific user.


User-Models to drive an Adaptive Virtual Advisor

Agents that adapt to their user need to have knowledge of their user and expertise on how best to adapt to that type of user. In this paper we describe the addition of an agent’s expertise and collection of machine-learnt user profiles to the proposed extended FAtiMA (Fearnot AffecTive Mind Architecture) cognitive agent architecture. A study to evaluate the extended architecture is presented which compares the benefit (i.e. reduced stress and increased rapport) of tailoring dialogue (i.e. empathic or neutral) to the specific user.

We will introduce interactive, integrative, quality assurance and live observability facilities to help on collaborative programming of multi-agent systems.


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Autonomous Shape Formation and Morphing in a Dynamic Environment by a Swarm of Robots

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MGpi: A Computational Model of Multiagent Group Perception and Interaction

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Next from AAMAS 2020

Autonomous Shape Formation and Morphing in a Dynamic Environment by a Swarm of Robots

Similar lecture

MGpi: A Computational Model of Multiagent Group Perception and Interaction

Downloads