New Zealand

Salma Ait Fares, Fumiyuki Adachi and Eisuke Kudoh (January 1st 2010). Joint Cooperative Diversity and Scheduling in OFDMA Relay System, Mobile and Wireless Communications Physical Layer Development and Implementatiom, Salma Ait Fares and Fumiyuki Adachi, IntechOpen, DOI: 10.5772/7685. 

Mohamed Othman, and Tg Dian Shahida Raja Mohd Auzar (March 1st 2010). The Development of Crosstalk-Free Scheduling Algorithms for Routing in Optical Multistage Interconnection Networks, Trends in Telecommunications Technologies, Christos J Bouras, IntechOpen, DOI: 10.5772/8479. 

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.


AAMAS 2020

AI-assisted Schedule Explainer for Nurse Rostering

scheduling

explanations

argumentation

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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). 

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.


A Framework for Collaborative and Interactive Agent-oriented Developer Operations

"Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, etc., one often encounters situations with non-stationary environments and in these scenarios, RL methods yield sub-optimal decisions. In this presentation, I talk about the problem of developing RL methods that obtain optimal decisions in a non-stationary environment. The goal of this problem is to maximize the long-term discounted reward accrued when the underlying model of the environment changes over time. To achieve this, we first adapt a change point algorithm to detect change in the statistics of the environment and then develop an RL algorithm that maximizes the long-run reward accrued. I illustrate that our change point method detects change in the model of the environment effectively and thus facilitates the RL algorithm in maximizing the long-run reward. In our work, we further validate the effectiveness of the proposed solution on non-stationary random Markov decision processes, a sensor energy management problem and a traffic signal control problem.
"


Reinforcement Learning Algorithms for Autonomous Adaptive Agents

"Our PhD research is concerned with the task of achieving cooperation in a system of competitive agents which cannot be explicitly controlled. To this end, it examines the problem from the system’s point of view, without restricting the agents’ behavior or requiring specific knowledge about their decision-making.
The governance of the MAS will be achieved via a dynamically adaptive governing policy based on a set of rules, which leaves full autonomy to the individual agents, but reacts to their actions via suitable changes of the environment. The mechanism is designed to lead to system-level cooperation while only assuming that the agents follow their own self-interested motives."


Achieving Emergent Governance in Competitive Multi-Agent Systems

This is an overview of my doctoral work studying inequality in social networks. The focus of this work is to design fairer algorithms that take into account the networks structure as well as community affiliations and individual incentives. I'll show my work in this field in three applications: recommendation algorithms, information diffusion, and partition algorithms. 


Algorithmic Fairness for Networked Algorithms

"Recent years have seen a push towards deploying fully autonomous robots in large, complex domains such as autonomous driving, space exploration, and service robots. However, legal, ethical, or technical constraints have limited the extent of these systems' employable autonomy. In order to successfully achieve their intended goals, these systems must utilize assistance from humans to compensate for their limitations. For such systems to be successful over the course of a long-term deployment, they must both be cognizant of their own competence and have the ability to improve this competence over time in a safe way. 
Motivated by practical concerns faced in industry, 
this thesis provides a formal model for such a human-agent system to reason about its own competence and aims in future work to provide effective ways of safely improving the competence of the system over the course of its deployment."


Competence-Aware Systems for Long-Term Autonomy

Many contributions to the field of computational social choice employ a variety of techniques developed in computer science to study preference aggregation mechanisms. However, there has been little research on how the availability of such techniques can help us reason about collective decision making. In this positional paper I discuss how computer-aided methods can be used to —automatically— reason about the outcomes of a collective decision making process. I also lay down several research directions one could explore to further develop the field in this direction.


Computer-aided Reasoning about Collective Decision Making

The process of determining the appropriate set of norms, referred to as synthesis, for a multiagent system has predominantly been carried out offline by the designers of the system. Of recent, there have been a few approaches that synthesise norms online utilising a centralised mechanism. The research presented here aims to propose a mechanism for decentralised runtime (online) norm synthesis through the use of agents dedicated to synthesising norms based on participating agents' requests.


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Next from AAMAS 2020

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

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NL $\Rightarrow$ Schedule: Evaluate Multitask Scheduling Capability of Large Language Models

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