Eduardo G. Hernández-Martínez and Eduardo Aranda-Bricaire (April 1st 2011). Convergence and Collision Avoidance in Formation Control: A Survey of the Artificial Potential Functions Approach, Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications, Faisal Alkhateeb, Eslam Al Maghayreh and Iyad Abu Doush, IntechOpen, DOI: 10.5772/14142.

New Zealand

Coalition formation games aim at predicting the cooperative behavior of agents when forming alliances. Agents entertain preferences over coalition structures, and the goal is to find a coalition structure that is good for both individual agents and the society as an entity. We measure the quality of partitions in terms of Pareto optimality, individual rationality, and various notions of popularity. We give both efficient algorithms and hardness results for computing partitions that satisfy these properties for additively separable hedonic games and give an outlook to results on other classes of coalition formation games, including roommate games and flatmate games.

AAMAS 2020

Desirable Partitions in Coalition Formation Games

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. 
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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.

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). 

The advent of data marketplaces and the increasing awareness of data privacy call for data privacy pricing mechanisms which can set prices of data properly while assuring privacy preservation. Moreover, the trade-off between the privacy of data and the accuracy of results need to be considered. Additionally, data marketplaces have different structures, including buy-sided market, sell-sided market, two-sided market, and two-sided platform. I will explore data privacy pricing mechanisms under these four structures.


Incentive Mechanisms for Data Privacy Preservation and Pricing

In Boolean games, each agent controls a set of Boolean variables and has a
goal represented by a propositional formula. We initiate a study of inference in Boolean games assuming the presence of a principle who has the
ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We show how taxation schemes can also be used to infer agents' goals. In our formulation, agents' goals are assumed to be unknown and the objective of the principle is to infer the goals of all the agents using appropriate taxation queries. Using an undirected graph (called the goal
overlap graph) associated with a Boolean game, we establish necessary and sufficient conditions for the existence of a Nash equilibrium for any
taxation query. Using these conditions, we develop an algorithm that uses taxation queries to learn agents' goals. Using a valid node coloring of the goal overlap graph, we show that goals of many agents can be inferred simultaneously. We also present more efficient (in terms of number of queries) goal inference algorithms for two special classes of Boolean functions, namely threshold and symmetric functions. 


Boolean Games: Inferring Agents' Goals Using Taxation Queries

We introduce ballooning multi-armed bandits (BL-MAB), a novel extension to the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. The regret in a BL-MAB setting is computed with respect to the best available arm at each time. We first observe that the existing stochastic MAB algorithms are not regret-optimal for the BL-MAB model. We show that if the best arm is equally likely to arrive at any time, a sub-linear regret cannot be achieved, irrespective of the arrival of the other arms. We further show that if the best arm is more likely to arrive in the early rounds, one can achieve sub-linear regret. Making reasonable assumptions on the arrival distribution of the best arm in terms of the thinness of the distribution’s tail, we prove that the proposed algorithm achieves sub-linear instance-independent regret. We further quantify explicit dependence of regret on the arrival distribution parameters.


Ballooning Multi Armed Bandits

"Chore division is a class of fair division problems in which some undesirable “resource” must be shared among a set of participants, with each participant wanting to get as little as possible. Typically the set of participants is fixed and known at the outset. This paper introduces a novel variant, called sequential online chore division (SOCD), in which participants arrive and depart online, while the chore is being performed: both the total number of participants and their arrival/departure times are initially unknown. In SOCD, exactly one agent must be performing the chore at any give time (e.g. keeping lookout), and switching the performer incurs a cost. In this paper, we propose and analyze
three mechanisms for SOCD: one centralized mechanism using side payments, and two distributed ones that seek to balance the participants’ loads. Analysis and results are presented in a domain motivated by autonomous vehicle convoy formation, where the chore is leading the convoy so that all followers can enjoy reduced wind resistance."


Sequential Online Chore Division for Autonomous Vehicle Convoy Formation

Voting for ensemble classifiers

Implementation of the Multi-Agent Microservices concept using the CArtAgO framework to represent virtual resources exposed by the agent, with a Linked Data represenation of each resource.


Delivering Multi-Agent MicroServices using CArtAgO

This work presents an extension to a graph-based evolutionary algorithm, called Genetic Network Programming with Reinforcement Learning (GNP-RL) to make it more amenable for solving coordinated multi-agent path-planning tasks in dynamic environments. We improve the algorithm’s ability to evolve meta-level reasoning strategies in three aspects: genetic composition, search and learning strategies, using optimal search algorithm, constraint conformance and task prioritization techniques.


Evolving Meta-Level Reasoning with Reinforcement Learning  and A* for Coordinated Multi-Agent Path-planning

Fear of Punishment Promotes the Emergence of Cooperation and Enhanced Social Welfare in Social Dilemmas

Hemaspaandra [22] celebrated the quite close relationship between computational social choice and computational complexity as a two-way street from which both areas benefited in the past, and expressed his hope that the areas become best friends forever. Later on, Rothe [38] celebrated the prominent Borda voting rule and surveyed recent advances on the complexity of problems related to the three most fundamental models of tampering with elections—namely, via manipulation, control, and bribery—and even related to using Borda beyond voting: in fair division and coalition formation in hedonic games. But now the party is over: no more celebration! Instead, we present a common criticism regarding computational social choice persistently making use of worst-case complexity. To overcome this shortcoming, we propose our blue sky idea of applying to problems from computational social choice the method of smoothed analysis due to Spielman and Teng [43, 44] and also
used by Bläser and Manthey [7], as some sort of a middle ground between the worst-case and the average-case analysis of algorithms.

Towards Reality: Smoothed Analysis in Computational Social Choice

Forecasting the future is a notoriously difficult task. One way to address this challenge is to "hybridize" the forecasting process, combining forecasts from a crowd of humans, as well as one or more machine models. However, an open challenge remains in how to optimally aggregate inputs from these pools into a single forecast. We proposed anchor attention for this type of sequence summary problem. Each forecast is represented by a trainable embedding vector. An anchor attention score is used to determine input weights. We show that our method learned a good representation for both forecaster and question, and outperforms the current state-of-the-art aggregation approaches.

Desirable Partitions in Coalition Formation Games

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Incentive Mechanisms for Data Privacy Preservation and Pricing

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Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation

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Desirable Partitions in Coalition Formation Games

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Next from AAMAS 2020

Incentive Mechanisms for Data Privacy Preservation and Pricing

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Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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