New Zealand

In the multidimensional stable roommate problem, agents have to be allocated to rooms and have preferences over sets of potential roommates. We study the complexity of finding good allocations of agents to rooms under the assumption that agents have diversity preferences [13]: each agent belongs to one of the two types (e.g., juniors and seniors, artists and engineers), and agents’ preferences over rooms depend solely on the fraction of agents of their own type among their potential roommates. We consider various solution concepts for this setting, such as core and exchange stability, Pareto optimality and envy-freeness. On the negative side, we prove that envy-free, core stable or (strongly) exchange stable outcomes may fail to exist and that the associated decision problems are NP-complete. On the positive side, we show that these problems are in FPT with respect to the room size, which is not the case for the general stable roommate problem.


AAMAS 2020

Stable Roommate Problem with Diversity Preferences

 **RTSS 2021** has successfully concluded. Hope you have enjoyed it, and look forward to seeing you next year in person.
    Announcement for the Awards:
 * Best Paper Award goes to: Monte Carlo Response-Time Analysis, Sergey Bozhko, Georg von der Brüggen, and Björn Brandenburg
 * Best Student Paper Award goes to: Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems, Mohsen Shekarisaz, Lothar Thiele, and Mehdi Kargahi.
 * Best Solution Award for Industry Session goes to: Toward Real-Time Guaranteed Scheduling for Autonomous Driving Systems. Jinghao Sun, Tianyi Wang, Kailu Duan, Bin Lu, Jiankang Ren, Zhishan Guo, and Guozhen Tan.



IEEE RTSS 2021

The IEEE Real-Time Systems Symposium (RTSS) is the premier conference in the field of real-time systems, and is a venue for researchers and practitioners to showcase innovations covering all aspects of real-time systems, including theory, design, analysis, implementation, evaluation, and experience. 

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). 

In this paper, we relate influence maximisation (IM) for the voting dynamics to models of network control in which external controllers interact with the intrinsic dynamics of opinion spread. In contrast to previous literature, which has mostly explored the discrete
setting, our focus is on continuous allocations of control. We develop an algorithm to numerically solve our IM problem via gradient ascent. We explore optimal allocations for leader-follower type networks for different budget scenarios and observe that optimal
allocations do not systematically target hub nodes, as it has been found in previous literature. Conversely, strategies are strongly opponent-depend, avoiding nodes targeted by the opponent if the opponent has a larger budget, while shadowing the opponent’s
allocation otherwise, i.e. targeting the same nodes as them.


Continuous Influence Maximisation for the Voter Dynamics: Is Targeting High-Degree Nodes a Good Strategy?

Distributed, Automated Calibration of Agent Based Model Parameters And Agent Behaviours

This paper presents a multi-robot long-term planning approach under uncertainty on the duration of tasks. The proposed method- ology takes advantage of generalized stochastic Petri nets to model multi-robot teams. It allows for unified modeling of action selection and uncertainty on duration of action execution. Goals are specified through the use of transition rewards and rewards per time unit. Our approach exploits the semantics provided by Markov reward automata in order to synthesize policies that optimize the long-run average reward. We provide an empirical evaluation on a simulated multi-robot monitoring problem, showing that the synthesized pol- icy outperforms a carefully hand-crafted policy.


Long-Run Multi-Robot Planning With Uncertain Task Durations

Fair division of land is an important practical problem that is commonly handled either by hiring assessors or by selling and dividing the proceeds. A third way to divide land fairly is via algorithms for fair cake-cutting. However, the current theory of fair cake-cutting is
not yet ready to optimally share a plot of land and such algorithms are seldom used in practical land-division.
We attempt to narrow the gap between theory and practice by performing extensive simulations of a classic cake-cutting algorithm on real land-value data. We improve the practical performance of this algorithm using heuristics we developed, and show their effectiveness on real land-value maps compared to actual assessment and sale data on various performance metrics. The cake-cutting algorithms perform better in most metrics.
We further examined the cake cutting algorithm with respect to strategic gain of an agent relative to a truthful agent. The strategic gain was found to be insignificant effect in cake-cutting algorithms.


Fair Cake-Cutting Algorithms with Real Land-Value Data

To transition from control theory to real applications, it is important to study missions such as Swarm Search and Service (SSS) where vehicles are not only required to search an area, but also service all jobs that they find. In SSS missions each type of job requires a group of vehicles to break-off from the swarm for a given amount of time to successfully service it. The required number of vehicles and the service rate are unique to each job type. Once a job has been completed the vehicles are able to return to the swarm for use elsewhere. If not enough vehicles are present in the swarm at the time that the job is identified, that job is dropped without being serviced. In SSS missions that occur in open environments, the arrival rate of jobs varies dynamically as vehicles move in and out of the swarm to service jobs. Human operators as tasked with effectively planning and managing these complex missions. This paper presents a user study that seeks to test the efficacy and ease-of-use of a prediction model known as the Hybrid Model as an aid in planning and monitoring tasks. Results show that the novel computational model aid allows operators to more effectively choose the necessary swarm size to handle expected mission workload, as well as, maintain sufficient situation awareness to evaluate the performance of the swarm during missions.


Human-in-the-loop Planning and Monitoring of Swarm Search and Service Missions

Representing unknown and missing knowledge about the environment is fundamental to leverage robot behavior and improve its performance in completing a task. However, reconstructing spatial knowledge beyond the sensory horizon of the robot is an extremely
challenging task. Existing approaches assume that the environment static and features repetitive patterns (e.g. rectangular rooms) or that it can be all generalized with pre-trained models. Our goal is to remove such assumptions and to introduce a novel methodology that allows the robot to represent unknown spatial knowledge in dynamic and unstructured environments. To this end, we exploit generative learning to (1) learn a distribution of spatial landmarks observed during the robot mission and to (2) generate missing in- formation in real-time. The proposed approach aims at supporting planning and decision-making processes needed for robot behaviors. In this paper, we describe architecture modeling the proposed approach and a first validation on a mobile platform.

GUESs: Generative Modeling of Unknown Environments and Spatial Abstraction for robots

In participatory budgeting, citizens can take part in the decision on which projects a city should spend money. Formally, the input is a set of items, each having a certain cost, while agents can express their preferences. The task is to choose a set of items respecting a given bound. Recently Talmon and Faliszewski introduced a framework for budgeting based on approval votes. This paper revisits the introduced methods axiomatically from an irresolute point of view, especially showing that two of the proposed methods coincide. The study is complemented by approximation results.


Irresolute Approval-based Budgeting

When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group setting, or a factory robot selects a worker to deliver a part. Demonstrating fairness in decision making is essential for such systems to be broadly accepted. We introduce a Multi-Armed Bandit algorithm with fairness constraints, where fairness is defined as a minimum rate that a task or a resource is assigned to a user. The proposed algorithm uses contextual information about the users and the task and makes no assumptions on how the losses capturing the performance of different users are generated. We provide theoretical guarantees of performance and an online user study. We view this as an exciting step towards including fairness constraints in resource allocation decisions.


The Fair Contextual Multi-Armed Bandit

Reinforcement learning under partial observability poses two types of conceptually orthogonal learning challenges; learning a useful representation
of the observable past, and selecting actions based on that representation. While the latter is common to both fully and partially observable control
problems, the former is idiomatic to the partially observable case. Traditional reinforcement learning relies exclusively on the reward signal to train the
representation model, but rewards constitute a weak learning signal in partially observable settings. To help train richer representations, we discuss
and propose an auxiliary self-supervised learning task designed to help the primary reinforcement learning task. We further propose two training schemes
for training with auxiliary tasks, one inspired by the idea of promoting complementary representations. Empirical evaluations show that agents which
exploit auxiliary tasks and complementary representations learn better policies, and converge faster, compared to agents which use common reactive and
recurrent representations trained on the rewards alone.

Learning Complementary Representations of the Past using Auxiliary Tasks in Partially Observable Reinforcement Learning

We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems.

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Next from AAMAS 2020

Continuous Influence Maximisation for the Voter Dynamics: Is Targeting High-Degree Nodes a Good Strategy?

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TimeWall: Enabling Time Partitioning for Real-Time Multicore+Accelerator Platforms

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