Thomas F. Heston (February 5th 2020). Introductory Chapter: Blockchain Technology and Smart Healthcare, Smart Healthcare, Thomas F. Heston, IntechOpen, DOI: 10.5772/intechopen.90633. 

Shouren Huang, Yuji Yamakawa and Masatoshi Ishikawa (November 25th 2019). Dynamic Compensation Framework to Improve the Autonomy of Industrial Robots [Online First], IntechOpen, DOI: 10.5772/intechopen.90169.

Adel Al-Jumaily and Mohamed Al-Jaafreh (December 1st 2006). Multi-Agent System Concepts Theory and Application Phases, Mobile Robotics, Moving Intelligence, Jonas Buchli, IntechOpen, DOI: 10.5772/4730.

New Zealand

In this demonstration, we introduce a system that facilitates trading agent competitions. Competitions mirror a Walrasian Exchange Economy. Each agent is endowed with a set of digital assets and preferences over them. Agents then search for sellers of the assets they demand and buyers of the assets they sell. They negotiate one- on-one to arrive at an optimal trade, and if successful, settle their transaction trustlessly on an emulated permissionless blockchain. This system is a precursor to a trading platform for digital assets and crypto-tokens in which agents trade on behalf of their users.


AAMAS 2020

Trading Agent Competition with Autonomous Economic Agents

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.
Contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods are especially encouraged.

ICPRAM 2020

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.

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.

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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). 

Conditional Updates of Answer Set Programming and its Applicationin Explainable Planning

Summer Internship Matching with Funding Constraints

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.


Trading Agent Competition with Autonomous Economic Agents

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Conditional Updates of Answer Set Programming and its Applicationin Explainable Planning

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Trading Agent Competition with Autonomous Economic Agents

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Next from AAMAS 2020

Conditional Updates of Answer Set Programming and its Applicationin Explainable Planning

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Integrating Generative Modeling into Deep Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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