New Zealand

Anthony Asher and John De Ravin (April 8th 2020). The Age Pension Means Tests: Contorting Australian Retirement [Online First], IntechOpen, DOI: 10.5772/intechopen.91856. 

We introduce and analyze new envy-based fairness concepts for agents with weights that quantify their entitlements in the allocation of indivisible items. We propose two variants of weighted envy-freeness up to one item (WEF1): strong, where the envy can be eliminated by removing an item from the envied agent’s bundle, and weak, where the envy can be eliminated either by removing an item as in the strong version or by replicating an item from the envied agent’s bundle in the envying agent’s bundle. We prove that for additive valuations, an allocation that is both Pareto optimal and strongly WEF1 always exists; however, an allocation that maximizes the weighted Nash social welfare may not be strongly WEF1 but always satisfies the weak version of the property. Moreover, we establish that a generalization of the round-robin picking sequence algorithm produces in polynomial time a strongly WEF1 allocation for an arbitrary number of agents; for two agents, we can efficiently achieve both strong WEF1 and Pareto optimality by adapting the adjusted winner procedure. Our work exhibits several aspects in which weighted fair division is richer and more challenging than its unweighted counterpart.


AAMAS 2020

Weighted Envy-Freeness in Indivisible Item Allocation

fair allocation of indivisible items; weighted envy-freeness; pareto efficiency; weighted round-robin; weighted nash welfare

weighted envy-freeness

unequal entitlements

fair allocation of indivisible items

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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). 

Mean field theory provides an effective way of scaling multiagent reinforcement learning algorithms to environments with many agents that can be abstracted by a virtual mean agent. In this paper, we extend mean field multiagent algorithms to multiple types. The types enable the relaxation of a core assumption in mean field games, which is that all agents in the environment are playing almost similar strategies and have the same goal. We conduct experiments on three different testbeds for the field of many agent reinforcement learning, based on the standard MAgents framework. We consider two different kinds of mean field games: a) Games where agents belong to predefined types that are known a priori and b) Games where the type of each agent is unknown and therefore must be learned based on observations. We introduce new algorithms for each type of game and demonstrate their superior performance over state of the art algorithms that assume that all agents belong to the same type and other baseline algorithms in the MAgent framework.


Multi Type Mean Field Reinforcement Learning

Multi-agent reinforcement learning has generally been studied under an assumption inherited from classical reinforcement learning: that the reward function is the exclusive property of the environment, and is only altered by external factors. In this work, we break free of this assumption and introduce peer rewarding, in which agents can deliberately influence each others' reward function. We formalize this more general setting and discuss its properties in depth. We also empirically study gifting, a peer rewarding mechanism which allows agents to reward other agents as part of their action space. We demonstrate that this approach can greatly improve learning progression in a resource appropriation setting and provide a preliminary analysis of the complex effects of gifting on the learning dynamics.


Gifting in Multi-Agent Reinforcement Learning

Model-based reinforcement learning algorithms make decisions by building and utilizing a model of the environment. However, none of the existing algorithms attempts to infer the dynamics of any state-action pair from known state-action pairs before meeting it for sufficient times. We propose a new model-based method called Greedy Inference Model (GIM) that infers the unknown dynamics from known dynamics based on the internal spectral properties of the environment. In other words, GIM can "learn by analogy". We further introduce a new exploration strategy which ensures that the agent rapidly and evenly visits unknown state-action pairs. GIM is much more computationally efficient than state-of-the-art model-based algorithms, as the number of dynamic programming operations is independent of the environment size. Lower sample complexity could also be achieved under mild conditions compared against methods without inferring. Experimental results demonstrate the effectiveness and efficiency of GIM in a variety of real-world tasks.


Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning

While game-theoretic models and algorithms have been developed to combat illegal activities, such as poaching and over-fishing, in green security domains, none of the existing work considers the crucial aspect of community engagement: community members are recruited by law enforcement agencies as informants and can provide valuable tips, e.g., the location of ongoing illegal activities, to assist patrols. We fill this gap and (i) introduce a novel two-stage security game model for community engagement, with a bipartite graph representing the informant-attacker social network and a level-$\kappa$ response model for attackers inspired by cognitive hierarchy; (ii) provide complexity results and exact, approximate, and heuristic algorithms for selecting informants and allocating patrollers against level-$\kappa$ ($\kappa<\infty$) attackers; (iii) provide a novel algorithm to find the optimal defender strategy against level-$\infty$ attackers, which converts the problem of optimizing a parameterized fixed-point to a bi-level optimization problem, where the inner level is just a linear program, and the outer level has only a linear number of variables and a single linear constraint. We also evaluate the algorithms through extensive experiments.


Green Security Game with Community Engagement

We study a general multi-dueling bandit problem, where an agent compares multiple options simultaneously and aims to minimize the regret due to selecting suboptimal arms. This setting generalizes the traditional two-dueling bandit problem and finds many real-world applications involving subjective feedback on multiple options. We start with the two-dueling bandit setting and propose two efficient algorithms, DoublerBAI and MultiSBM-Feedback.DoublerBAI provides a generic schema for translating known results on best arm identification algorithms to the dueling bandit problem, and achieves a regret bound of $O(\ln T)$. MultiSBM-Feedback not only has an optimal $O(\ln T)$ regret, but also reduces the constant factor by almost a half compared to benchmark results. Then, we consider the general multi-dueling case and develop an efficient algorithm MultiRUCB. Using a novel finite-time regret analysis for the general multi-dueling bandit problem, we show that MultiRUCB also achieves an $O(\ln T)$ regret bound and the bound tightens as the capacity of the comparison set increases. Based on both synthetic and real-world datasets, we empirically demonstrate that our algorithms outperform existing algorithms. 


Dueling Bandits: From Two-dueling to Multi-dueling

Signalized intersections are managed by controllers that assign right of way (green, yellow, and red lights) to non-conflicting directions. Optimizing the actuation policy of such controllers is expected to alleviate traffic congestion and its adverse impact. Given such a safety-critical domain, the affiliated actuation policy is required to be interpretable in a way that can be understood and regulated by a human. This presentation demonstrates and gives an overview of how we approached solving such a problem.

Learning an Interpretable Traffic Signal Control Policy

Distributed Constraint Optimization Problems (DCOPs) are a powerful tool to model multi-agent coordination problems that are distributed by nature. The formulation is suitable for problems where variables are discrete and constraint utilities are represented in tabular form. However, many real-world applications have variables that are continuous and tabular forms thus cannot accurately represent constraint utilities. To overcome this limitation, researchers have proposed the Continuous DCOP (C-DCOP) model, which are DCOPs with continuous variables. But existing approaches usually come with some restrictions on the form of constraint utilities and are without quality guarantees. Therefore, in this paper, we (i) propose an exact algorithm to solve a specific subclass of C-DCOPs; (ii) propose an approximation method with quality guarantees to solve general C-DCOPs; (iii) propose additional C-DCOP algorithms that are more scalable; and (v) empirically show that our algorithms outperform existing state-of-the-art C-DCOP algorithms when given the same communication limitations.

New Algorithms for Continuous Distributed Constraint Optimization Problems

Recent years have witnessed a tremendous improvement of deep reinforcement learning. However, a challenging problem is that an agent may suffer from inefficient exploration, particularly for on-policy methods. Previous exploration methods either rely on complex structure to estimate the novelty of states, or incur sensitive hyper-parameters causing instability. We propose an efficient exploration method, Multi-Path Policy Optimization (MPPO), which does not incur high computation cost and ensures stability. MPPO maintains an efficient mechanism that effectively utilizes a population of diverse policies to enable better exploration, especially in sparse environments. We also give a theoretical guarantee of the stable performance. We build our scheme upon two widely-adopted on-policy methods, the Trust-Region Policy Optimization algorithm and Proximal Policy Optimization algorithm. We conduct extensive experiments on several MuJoCo tasks and their sparsified variants to fairly evaluate the proposed method. Results show that MPPO significantly outperforms state-of-the-art exploration methods in terms of both sample efficiency and final performance. 


Multi-Path Policy Optimization

In this work, we consider a student-project-resource matching-allocation problem, where students have preferences over projects and the projects have preferences over students. Although students are many-to-one matched to projects, indivisible resources are many-to-one allocated to projects whose capacities are endogenously determined by the resources allocated to them. Traditionally, this problem is decomposed into two separate problems: (1) resources are allocated to projects based on expectations (resource allocation problem), and (2) students are matched to projects based on the capacities determined in the previous problem (matching problem). Although both problems are well-understood, if the expectations used in the first are incorrect, we obtain a suboptimal outcome. Thus, this problem must be solved as a whole without dividing it in two parts. We show that no strategyproof mechanism satisfies fairness (i.e., no student has justified envy) and weak efficiency requirements on students' welfare. Given this impossibility result, we develop a new strategyproof mechanism that strikes a good balance between fairness and efficiency and assess it by experiments


Game Theoretic Analysis for Two-Sided Matching with Resource Allocation

The ability to cooperate is one of the key features of many multi- agent systems. In this paper, we extend the well-known model of graph-restricted games due to Myerson to signed graphs, where the link between any two players may be either positive or negative. Hence, in our model, it is possible to explicitly define not only that some players are friends (as in Myerson’s model) but also that some other players are enemies. As such our games can express a wider range of situations, e.g., animosities between political parties. We say that a coalition is feasible if every two players are connected by a path of positive edges and no two players are connected by a negative edge. We define the value for signed graph games using the axiomatic approach that closely follows the celebrated char- acterisation of the Myerson value. Furthermore, we propose an algorithm for computing an arbitrary semivalue, including the one proposed by us. Moreover, we consider signed graph games with a priori defined alliances (unions) between players and propose an algorithm for the extension of the Owen value to this setting.


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Multi Type Mean Field Reinforcement Learning

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Multivariate Algorithmics for Eliminating Envy by Donating Goods

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