New Zealand

Hag Seong Kim (April 1st 2008). Task Space Approach of Robust Nonlinear Control for a 6 DOF Parallel Manipulator, Parallel Manipulators, New Developments, Jee-Hwan Ryu, IntechOpen, DOI: 10.5772/5381. 

 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. 

Chakir Aziza and Chergui Meriyem (April 11th 2020). Using Multi-Agent System to Govern the IT Needs of Stakeholders [Online First], IntechOpen, DOI: 10.5772/intechopen.91440. 

AAMAS 2020

Many to Many Matching of Agents with Nonlinear Utility Function

nonlinear utility space

multi-issue

multi-agent system

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

Social influence is an essential aspect of human social interaction. Influence propagation modeling has been extensively studied and applied in various research fields, such as the maximization of product adoption, the spread of infectious diseases, etc. The primary sources of information in any social network are the influential nodes that propagate this information further. Our study gives a method to identify the strong influencers in a network and dynamically predict the links/interactions made by them during information propagation. Previously, many efforts have been made to solve the link prediction problem in social networks such as the Adamic Adar index, based on the degree of common connecting neighbors; however, they disregard the fact that social networks can be dynamic over time—any network dataset collected by a snapshot at a particular instant of time may be incomplete and not reflect all the previous links formed. This may in turn result in false predictions. Our approach considers the current dynamics of the social network where information propagated by an agent leads to the prediction of new links with other nodes. Taking opinion propagation as an essential feature, we test our algorithm on Stanford’s Facebook network dataset and compare the results with the previous Adamic Adar index. The results obtained cover all the links predicted by Adamic Adar, as well as some new links formed dynamically. This is indicative that considering dynamic link formation is more efficient and helps address the problem of the incompleteness of network datasets reflecting the state at a time instant.

Dynamic Interactions by Strong Influencers in Social Networks using Opinion Propagation

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. The proposed approach circumvents the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need for mutually consistent actions) by relying on the ALMA heuristic as a coordination mechanism for each stage game. ALMA-Learning is decentralized and requires no communication between the agents. The latter only observe their own history of action/reward pairs.

Learning to Persist or Switch: Efficient and Fair Allocations in Large-scale Multi-agent Systems

We consider the problem of forming a testbed of elections to be used
for numerical experiments (such as testing algorithms or estimating
the frequency of a given phenomenon). We seek elections that
come from well-known statistical distributions and are as diverse
as possible. To this end, we define a (pseudo)metric over elections,
generate a set of election instances, and measure distances between
them, to assess how diverse they are. Finally, we show how to use
these elections to test election-related algorithms.

Drawing a Map of Elections

"Autonomous agents promise users of a personalized future, allowing
them to direct their attention to tasks most meaningful to them.
However, the demands of personalization stand unfulfilled by current
agent training paradigms such as machine learning, which
require many orders of data to train agents on a single task. In
sequential decision making domains, Reinforcement Learning (RL)
enables this need, when a priori training of desired behaviors is
intractable. Prior work has leveraged user input to train agents
by mapping them to numerical reward signals. However, recent
approaches have identified inconsistent human feedback as a bottleneck
to achieving best-case performance. In this work, we present
empirical evidence to show that human perception affected by
contrast effects distorts their feedback to Reinforcement Learning
agents. Through a set of studies involving 900 participants from
Amazon Mechanical Turk who were asked to give feedback to RL
agents, we show that participants significantly underrate an agent’s
actions after being exposed to an agent of higher competence on the
same task. To understand the significance of this effect on agent performance
during training, we then simulate trainers that underrate
actions of an agent based on past performance – creating a systematically
skewed feedback signal – integrated into an actor-critic
framework. Our results show that agent performance is reduced by
up to 98% in the presence of systematic skews in human feedback in
Atari environments. Our work provides a conceptual understanding
of a source of inconsistency in human feedback, thus informing the
design of human-agent interactions."

Yesterday’s Reward is Today’s Punishment: Contrast Effects in Human Feedback to Reinforcement Learning Agents

Explainability has been a core research topic in AI for decades and therefore it is surprising that the current concept of Explainable AI (XAI) seems to have been launched as late as 2016. This is a problem with current XAI research because it tends to ignore existing knowledge and wisdom gathered over decades or even centuries by other relevant domains. This paper presents the notion of Contextual Importance and Utility (CIU), which is based on known notions and methods of Decision Theory. CIU extends the notions of importance and utility for the non-linear models of AI systems and notably those produced by Machine Learning methods. CIU provides a universal and model-agnostic foundation for XAI.

Decision Theory meets Explainable AI

"This paper investigates the impact of changes in agents' beliefs coupled with dynamics in agents' meta-roles on the evolution of institutions. The study embeds agents' meta-roles in the BDI architecture. In this context, the study scrutinises the impact of cognitive dissonance in agents due to unfairness of institutions. To showcase our model, two
historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company are simulated. Results show how change in roles of agents coupled with specific institutional characteristics leads to changes of the rules in the system."

Impact of meta-roles on the evolution of institutions

Towards Demystifying Subliminal Persuasivness: Using XAI-Techniques to Highlight Persuasive Markers of Public Speeches

Explaining the influence of prior knowlege on POMCP policies

The goal of multi-objective problems is to find solutions that balance different objectives. When solving multi-objective problems using reinforcement learning linear scalarisation techniques are generally used, however system expertise is required to optimise the weights for linear scalarisation. Thresholded Lexicographic Ordering (TLO) is one technique that avoids the need for an expert to specify weights; instead a system designer can directly specify a preferred ordering over objectives, along with a desired threshold value for each objective. In this presentation we propose a novel algorithm to dynamically set thresholds for use with TLO. We also present the first evaluation of TLO in a complex multi-objective multi-agent problem, the Dynamic Economic Emissions Dispatch domain. Our empirical results demonstrate that TLO with our dynamic thresholding algorithm achieves superior results when compared with a hand-tuned linear scalarisation method from previously published work.

Dynamic Thresholded Lexicographic Ordering

Online social networks are known to lack adequate multi-user privacy support. In this paper we present EXPRI, an agent architecture that aims to assist users in managing multi-user privacy conflicts. By considering the personal utility of sharing content and the individually preferred moral values of each user involved in the conflict, EXPRI identifies the best collaborative solution by applying practical reasoning techniques. Such techniques provide the agent with the cognitive process that is necessary for explainability. Furthermore, the knowledge gathered during the practical reasoning process allows EXPRI to engage in contrastive explanations.

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