New Zealand

Roel C. Rabara, Marilyn C. Ferrer, Jennifer Jara-Rabara and Rachel C. Sotto (September 19th 2018). Securing Diversity for Food Security: The Case of Conservation and Use of Rice Genetic Resources, New Visions in Plant Science, Özge Çelik, IntechOpen, DOI: 10.5772/intechopen.77216.

In this paper, we study the two-sided matching problem with soft diversity constraints in which each student belongs to one type and each school imposes soft targets on each type. We first identify limitations of type-specific quotas in a previous model and introduce a new general model that takes different levels of importance of types into account. Then we propose a new algorithm that yields a non-wasteful and fair outcome with respect to different levels of importance.


AAMAS 2020

Multiple Levels of Importance in Matching with Distributional Constraints

regional quotas

refugee resettlement

distributional constraints

two-sided matching

two-sided matching; diversity constraints; soft quotas

school choice

mechanism design

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

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). 

Algorithms and machine learning models, including the decisions made by these models, are becoming ubiquitous in our daily life, including hiring. We make no value judgment regarding this development; rather, we simply acknowledge that it is quickly becoming reality that automation plays a role in hiring. Increasingly, these technologies are used in all of the small decisions that make up the modern hiring pipeline: from which resumes get selected for a first screen to who gets an on site interview. Thus, these algorithms and models may potentially amplify bias and (un)fairness issues for many historically marginalized groups. While there is a rapidly expanding literature on algorithmic decision making and fairness, there has been limited work on fairness specifically for online, multi-stakeholder decision making processes such as those found in hiring. We outline broad challenges including formulating definitions for fair treatment and fair outcomes in hiring, and incorporating these definitions into the algorithms and processes that constitute the modern hiring pipeline. We see the AAMAS community as uniquely positioned to address these challenges.

We Need Fairness and Explainability in Algorithmic Hiring

Coordination of Prosumer Agents via Distributed Optimal Power Flow

In this thesis, we aim at endowing robots with mechanisms to learn multimodal representations from sensory data and to allow them to execute tasks considering different subsets of available perceptions. We address the learning of these representations from supervised, unsupervised and reinforcement learning methodologies in the context of virtual agents and robots. We hope that, by achieving the proposed goals, the contributions of this thesis might prompt future research on applications of multimodal representations in robots and other artificial agents.

Multimodal Representation Learning for Robotic Cross-Modality Policy Transfer

A Game Theoretic Approach For k-Core Minimization

Hierarchical Reinforcement Learning (HRL) is a promising approach to solve more complex tasks which may be challenging for the traditional reinforcement learning. HRL achieves this by decomposing a task into shorter-horizon subgoals as targets to achieve. Autonomous discovery of such subgoals is an important part of HRL. Recently, end-to-end HRL methods have been used to reduce the overhead from offline subgoal discovery by seeking the useful subgoals while simultaneously learning optimal policies in a hierarchy. However, these methods may still suffer from slow learning when the search space used by a high level policy to find the subgoals is large. We propose LIDOSS, an end-to-end HRL method with an integrated heuristic for subgoal discovery, to address this problem.

Hierarchical Reinforcement Learning with Integrated Discovery of Salient Subgoals

Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors

This paper studies the complexity of two microbribery problems under the model of group identification. In these problems, we are given a subset of distinguished individuals, and the questions are whether these individuals can be made socially qualified or whether they can be made exactly the socially qualified individuals, respectively, by modifying a limited number of entries in the qualifications-profile. For consent rules, the consensus-start-respecting rule, and the liberal-start-respecting rule, we obtain many $\np$-hardness results as well as polynomial-time solvability results. We also study the problems in $r$-profiles where each individual qualifies exactly $r$ individuals.

Microbribery in Group Identification

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents from their start locations to their goal locations without collisions. We study the lifelong variant of MAPF where agents are constantly engaged with new goal locations, such as in warehouses. We propose a new framework for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of agents only within a finite time horizon and ignores collisions beyond it. Our framework is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We evaluate our framework with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents, significantly outperforming existing methods.

Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

"Hierarchical clustering is a stronger extension of one of today's most influential unsupervised learning methods: clustering. The goal of this method is to create a hierarchy of clusters, thus constructing cluster evolutionary history and simultaneously finding clusterings at all resolutions. We propose four traits of interest for hierarchical clustering algorithms: (1) empirical performance, (2) theoretical guarantees, (3) balance (the minimum ratio between cluster sizes), and (4) scalability. While a number of algorithms are designed to achieve one to two of these traits at a time, there exist none that achieve all four.



Inspired by Bateni et al.'s scalable and empirically successful Affinity Clustering [NeurIPs 2017], we introduce Affinity's successor, Matching Affinity Clustering. Like its predecessor, Matching Affinity Clustering maintains strong empirical performance, even outperforming Affinity when the dataset is size 2^n and clusters are balanced, and uses Massively Parallel Communication as its distributed model. Designed to maintain provably balanced clusters, we show that our algorithm achieves a (1/3-epsilon)-approximation for Moseley and Wang's revenue (the dual to Dasgupta's cost) when the data set is of size 2^n, and a (1/9-epsilon)-approximation in general. We prove the former approximation is tight, and also that Affinity Clustering cannot do better than a 1/O(n)-approximation. In addition, we see that our algorithm empirically performs similarly to Affinity Clustering and k-Means, outperforming many state-of-the-art serial algorithms. Along the way, we also introduce an efficient k-sized maximum matching algorithm in the MPC model."

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We Need Fairness and Explainability in Algorithmic Hiring

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