New Zealand

Kunikazu Kobayashi, Koji Nakano, Takashi Kuremoto and Masanao Obayashi (February 1st 2010). Objective-based Reinforcement Learning System for Cooperative Behavior Acquisition, Application of Machine Learning, Yagang Zhang, IntechOpen, DOI: 10.5772/8615. 

Daniela P. Alves, Li Weigang and Bueno B. Souza (January 1st 2008). Reinforcement Learning to Support Meta-Level Control in Air Traffic Management, Reinforcement Learning, Cornelius Weber, Mark Elshaw and Norbert Michael Mayer, IntechOpen, DOI: 10.5772/5293. 

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
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AAMAS 2020

Reinforcement Learning for Train Movement Planning at Railway Stations

junction planning

train planning

reinforcement learning

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). 

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
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technical paper

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.

Multimodal Representation Learning for Robotic Cross-Modality Policy Transfer

A Game Theoretic Approach For k-Core Minimization

Hierarchical Reinforcement Learning (HRL) is a promising approach to solve more complex tasks which may be challenging for the traditional reinforcement learning. HRL achieves this by decomposing a task into shorter-horizon subgoals as targets to achieve. Autonomous discovery of such subgoals is an important part of HRL. Recently, end-to-end HRL methods have been used to reduce the overhead from offline subgoal discovery by seeking the useful subgoals while simultaneously learning optimal policies in a hierarchy. However, these methods may still suffer from slow learning when the search space used by a high level policy to find the subgoals is large. We propose LIDOSS, an end-to-end HRL method with an integrated heuristic for subgoal discovery, to address this problem.

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

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Optimising a Real-Time Scheduler for Railway Lines using Policy Search

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Next from AAMAS 2020

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

Similar lecture

Optimising a Real-Time Scheduler for Railway Lines using Policy Search

Downloads