New Zealand

Joseph Zalaket (October 1st 2008). Extending AI Planning to Solve more Realistic Problems, Frontiers in Robotics, Automation and Control, Alexander Zemliak, IntechOpen, DOI: 10.5772/6315. 

George Dekoulis (December 6th 2017). Introductory Chapter: Introduction to Roboethics: The Legal, Ethical and Social Impacts of Robotics, Robotics - Legal, Ethical and Socioeconomic Impacts, George Dekoulis, IntechOpen, DOI: 10.5772/intechopen.71170.

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. 

AAMAS 2020

A Multi-Robot Platform for the Autonomous Operation and Maintenance of Offshore Wind Farms

multi-agency

ai planning

robotics

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). 

technical paper

The DALI system aims at making existing conventional traffic control systems autonomous and smart. We achieve this goal by plugging-in a software agent into each existing intersection controller which becomes “the brain” of the controller. The agents analyze the traffic data, communicate with each other directly, and collaborate to execute a timing strategy that improves traffic flow.

DALI: An Agent-Plug-In System to “Smartify” Conventional Traffic Control Systems

Deep Reinforcement Learning for Market Making

This video presentation presents the main work we have done for investigating of Multi-agent Reinforcement Learning for Multi-traffic Light Control.

Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization

Agents that are tailored to appear and behave as members of a particular culture are more acceptable by and persuasive to members of that culture than agents that are not tailored. We report a study that systematically unpacks two tailoring components—appearance and argumentation—for virtual exercise coaches designed for the Indian and American cultures. Indian participants who interacted with an agent whose argumentation was tailored to their culture were significantly more satisfied with the agent irrespective of the agent’s appearance.

Argumentation is More Important than Appearance for Designing Culturally Tailored Virtual Agents

Restricted Domains of Dichotomous Preferences with Possibly Incomplete Information

BitcoinF: Achieving Fairness for Bitcoin in Transaction-Free-Only Model

Games are an increasingly useful tool for training and testing learning algorithms. Recent examples include GANs, AlphaZero and the AlphaStar league. However, multi-agent learning can be extremely difficult to predict and control. Learning dynamics can yield chaotic behavior even in simple games. In this paper, we present basic mechanism design tools for constructing games with predictable and controllable dynamics. We present a robust framework for dimensionality reduction arguments in large network games.

Robust Self-Organization in Games: Symmetries, Conservation Laws and Dimensionality Reduction

The open-source Unity platform, where agents can be trained using hierarchical or non-hierarchical reinforcement learning, supports the use of games and simulations as environments for multiple- agent interactions. In this demonstration, we present hierarchical and non-hierarchical multi-agent interactions based on Unity reinforcement learning, specifically, hierarchical reinforcement learning that sets different levels of agent’s observations to achieve the goal. We created four multi-agent scenarios in the Unity environment, namely, Crawler, Tennis, Banana Collector, and Soccer, to test the interaction performances of hierarchical and non- hierarchical reinforcement learning. The simulation-interaction performances show that hierarchical reinforcement learning can be applied to multi-agent environments and can compete with agents trained via non-hierarchical reinforcement learning. 


Peer Reviewing in Participatory Guarantee Systems

Axiomatic approaches are an appealing method for designing fair algorithms, as they provide formal structure for reasoning about and rationalizing individual decisions. However, to make these algorithms useful in practice, their axioms must appropriately capture social norms. We explore this tension between fairness axioms and socially acceptable decisions in the context of cooperative game theory. We use two crowdsourced experiments to study people's impartial reward divisions in cooperative games, focusing on games that systematically vary the values of the single-player coalitions. Our results show that people select rewards that are remarkably consistent, but place much more emphasis on the single-player coalitions than the Shapley value does. Further, their reward divisions violate both the null player and additivity axioms, but support weaker axioms. We argue for a more general methodology of testing axioms against experimental data, retaining some of the conceptual simplicity of the axiomatic approach while still using people’s opinions to drive the design of fair algorithms.

Testing Axioms Against Human Reward Divisions in Cooperative Games

Recent years have seen various designs of strategyproof mechanisms in the facility location game and the obnoxious facility game, by considering the facility as a point. In this paper, we extend that point to be an interval and study a novel activity scheduling game to schedule an activity in the time domain [0, 1] based on all agents’ time reports. The activity lasts for a time period of d with 0 <=d<=1, and each agent i wants his private time 𝑡𝑖 to be within the activity duration [y,y+d] or at least as close as possible. Thus his cost is the time difference between his time t_i and the activity duration [y, y+d]. The social cost is the summation of all agents’ costs. Our objective is to choose the activity starting time 𝑦 so that the mechanisms are strategyproof (truthful) and efficient. We design a mechanism outputting an optimal solution and prove it is group strategyproof. For minimizing the maximum cost, we also design a strategyproof mechanism with approximation ratio 2. In the obnoxious activity scheduling game, each agent prefers his conflict time t_i to be far away from the activity duration [y,y+d]. We respectively design deterministic and randomized group strategyproof mechanisms with provable approximation ratios and also show the lower bounds. Besides, for extension, we consider the cost/utility as the characteristic function and find group strategyproof mechanisms for minimizing the social cost and maximizing the social utility.

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DALI: An Agent-Plug-In System to “Smartify” Conventional Traffic Control Systems

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Next from AAMAS 2020

DALI: An Agent-Plug-In System to “Smartify” Conventional Traffic Control Systems

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Reconfigurable Interaction for MAS Modelling

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