New Zealand

Danilo Saft and Volker Nissen (September 13th 2017). A Toolbox to Analyze Emergence in Multiagent Simulations, Multi-agent Systems, Jorge Rocha, IntechOpen, DOI: 10.5772/intechopen.69749.

Konstantin Aksyonov, Eugene Bykov, Leonid Dorosinskiy, Elena Smoliy and Olga Aksyonova (April 1st 2011). Decision Support based on Multi-Agent Simulation Algorithms with Resource Conversion Processes Apparatus Application, Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies, Faisal Alkhateeb, Eslam Al Maghayreh and Iyad Abu Doush, IntechOpen, DOI: 10.5772/15619. 

Azah Mohamed, Mohammed Abdol Slaem and Mohammad Fuad Faisal (August 18th 2010). Power Quality Disturbance Detection and Source Prediction Using Advanced Signal Processing Techniques, Modelling, Simulation and Identification, Azah Mohamed, IntechOpen, DOI: 10.5772/10012. 

Electric power sharing among households based on the bidding method is studied as a future service. In order to verify the feasibility of such a service, a new multi-agent simulation model has been designed. We validated this model through some evaluations. For example, it is confirmed that the market price on this service stably changes according to the supply-demand balance between both sold and purchased bid volumes. In addition to that, the results of the household profit and contract rate of this service showed that the design for bid strategies works as intended in most cases.


AAMAS 2020

Design and Evaluations of Multi-Agent Simulation Model for Electric Power Sharing among Households

electric power sharing

multi-agent simulation

artificial market

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

We present a reinforcement learning-based approach for generating a timetable for trains traversing through a railway station.
We demonstrate how the reinforcement learning approach is used to learn heuristics, which in turn are used to generate a train movement plan.
We compare the quality of the plan generated using ‘rules used in practice’ and the plan generated by the ‘heuristics learned using the RL approach’, under different congestion scenarios
"


Reinforcement Learning for Train Movement Planning at Railway Stations

We study proportionality in approval-based multi- winner elections with a variable number of winners, where both the size and identity of the winning committee are informed by voters’ opinions. While proportionality has been studied in multi- winner elections with a fixed number of winners, it has not been considered in the variable number of winners setting. The measure of proportionality we consider is average satisfaction (AS), which intuitively measures the number of agreements on aver- age between sufficiently large and cohesive groups of voters and the output of the voting rule. First, we show an upper bound on the AS that any deterministic rule can provide, and that straightforward adaptations of deterministic rules from the fixed number of winners setting do not achieve better than a 1/2 approximation to AS even for large numbers of candidates. We then prove that a natural randomized rule achieves a 29/32 approximation to AS.


Proportionality in Approval-Based Elections With a Variable Number of Winners

Condorcet extensions have long held a prominent place in social choice theory. A Condorcet extension will return the Condorcet winner as the unique winner whenever such an alternative exists. However, the definition of a Condorcet extension does not take into account possible manipulation by the voters. A profile where all agents vote truthfully may have a Condorcet winner, but this alternative may not end up in the set of winners if agents are acting strategically. Focusing on the class of tournament solutions, we show that many natural social choice functions in this class, such as the well-known Copeland and Slater rules, cannot guarantee the preservation of Condorcet winners when agents behave strategically. Our main result in this respect is an impossibility theorem that establishes that no tournament solution satisfying a very weak decisiveness requirement can provide such a guarantee. On the bright side, we identify several indecisive but otherwise attractive tournament solutions that do guarantee the preservation of Condorcet winners under strategic manipulation.


Preserving Condorcet Winners under Strategic Manipulation

We present MsATL: the first tool for deciding the satisfiability of Alternating-time Temporal Logic (ATL) with imperfect information. MsATL combines SAT Modulo Monotonic Theories solvers with existing ATL model checkers: MCMAS and STV. The tool can deal with various semantics of ATL, including perfect and imperfect information, and can handle additional practical requirements. MsATL can be applied for synthesis of games that conform to a given specification, with the synthesised game often being minimal.


MsATL: a Tool for SAT-Based ATL Satisfiability Checking

Mechanism Design for School Choice with Soft Diversity Constraints


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.


Multiple Levels of Importance in Matching with Distributional Constraints

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.

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Reinforcement Learning for Train Movement Planning at Railway Stations

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Reinforcement Learning for Train Movement Planning at Railway Stations

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Goal-driven Adaptation of MOISE Organizations for Workflow Enactment

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