Larysa Globa, Oleksandr Stryzhak, Nataliia Gvozdetska and Volodymyr Prokopets (October 23rd 2019). Intelligent Workload Scheduling in Distributed Computing Environment for Balance Between Energy Efficiency and Performance [Online First], IntechOpen, DOI: 10.5772/intechopen.86874. 

Wen-Jyi Hwang (July 19th 2017). Introductory Chapter: The Newest Research in Parallel and Distributed Computing, Recent Progress in Parallel and Distributed Computing, Wen-Jyi Hwang, IntechOpen, DOI: 10.5772/intechopen.69201.

Ignacio Aravena, Anthony Papavasiliou and Alex Papalexopoulos (July 19th 2017). A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids, Recent Progress in Parallel and Distributed Computing, Wen-Jyi Hwang, IntechOpen, DOI: 10.5772/67791.

New Zealand

&quot;Hierarchical clustering is a stronger extension of one of today&#39;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.&#39;s scalable and empirically successful Affinity Clustering [NeurIPs 2017], we introduce Affinity&#39;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&#39;s revenue (the dual to Dasgupta&#39;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.&quot;

AAMAS 2020

Matching Affinity Clustering: Improved Hierarchical Clustering at Scale with Guarantees

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

"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."

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). 

Option-Critic in Cooperative Multi-agent Systems

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

Designing Truthful Contexual Multi-Armed Bandits based Sponsored Search Auctions

Reinforcement learning algorithms have been shown to converge to the classic replicator dynamics of evolutionary game theory, which describe the evolutionary process in the limit of an infinite population. However, it is not clear how to interpret these dynamics from the perspective of a learning agent. In this paper we propose a data-inefficient batch-learning algorithm for temporal difference Q learning and show that it converges to a recently proposed deterministic limit of temporal difference reinforcement learning. In a second step, we state a data-efficient learning algorithm, that uses a form of experience replay, and show that it retains core features of the batch learning algorithm. Thus, we propose an agent-interpretation for the learning dynamics: What is the infinite population limit of evolutionary dynamics is the infinite memory limit of learning dynamics.

Reinforcement Learning Dynamics in the Infinite Memory Limit

Agent-Based Explanations in AI: Towards an Abstract Framework

On-line Estimators for Ad-hoc Task Allocation

"Cooperative cargo transport (e.g., two agents moving a table) is trivial for humans, but poses exceptional challenges to robots. One challenge is learning the dynamics properties of unknown cargo, which is critical for safe operations. We present an algorithm to estimate the inertial parameters of an object grasped by one or more robots in real-time. 
We model each robot's N sub-body system--- considering external and joint actuation--- using the Recursive Newton-Euler equations. A constrained Unscented Kalman Filter estimates the grasped object's mass, center of mass and moments of inertia. Our approach is validated through simulation using Astrobee, a free-flying robot."

Cooperative Real-Time Inertial Parameter Estimation

The problem of disinformation in online social networks has recently received a considerable amount of attention from the research community. It has been shown that online social networks are extensively getting exploited to alter public opinion and individuals’ stance on a wide-range of topics. This study proposes an agent-based model that simulates a disinformation campaign by a group of organized users called conspirators, targeting a susceptible population, which are then opposed by a parallel organized group of users referred to as inouclators that try to act as a barrier to the spread of disinformation. The results of this study indicate that the process of inoculating a susceptible population against disinformation is mostly at the price of further polarizing the population.

Modeling Disinformation and the Effort to Counter It: A Cautionary Tale of When the Treatment Can Be Worse Than the Disease

target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark. Moreover, we find the residual algorithm an effective approach to the distribution mismatch problem in model-based planning. Compared with the existing TD(k) method, our residual-based method makes weaker assumptions about the model and yields a greater performance boost.

Deep Residual Reinforcement Learning

**Please click on the button bellow to see this lecture on SlidesLive:**

[![](https://assets.underline.io/uploads/markdown_image/1/image/08e10ad349922f32e7322b77b8df9019.png)](https://slideslive.com/38946802/boston-dynamics)

**Abstract:**

Crowdsourcing harnesses human effort to solve computer-hard problems. Such tasks often have different levels of difficulty and workers have varying levels of skill at completing them. With a limited budget, it is important to wisely allocate the spend among the tasks and workers such that the overall outcome is as good as possible. Most existing work addresses this budget allocation problem by assuming that workers have a single level of ability for all tasks. However, this neglects the fact that tasks can belong to a variety of diverse categories and workers may have varying abilities across them. To incorporating such category-awareness, we model the interaction between the crowdsource campaign initiator and the workers as a procurement auction and propose a computationally efficient mechanism, INCARE, to achieve high-quality outcomes given a limited budget. In this presentation, we will present INCARE in detail and show the performance of INCARE on a standard real-world data set.

Matching Affinity Clustering: Improved Hierarchical Clustering at Scale with Guarantees

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Matching Affinity Clustering: Improved Hierarchical Clustering at Scale with Guarantees

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Next from AAMAS 2020

Option-Critic in Cooperative Multi-agent Systems

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Fixed-Weight Difference Target Propagation

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