Antonio J. R. Neves, Jose Luis Azevedo, Bernardo Cunha, Nuno Lau, Joao Silva, Frederico Santos, Gustavo Corrente, Daniel A. Martins, Nuno Figueiredo, Artur Pereira, Luis Almeida, Luis Seabra Lopes, Armando J. Pinho, Joao Rodrigues and Paulo Pedreiras (January 1st 2010). CAMBADA Soccer Team: from Robot Architecture to Multiagent Coordination, Robot Soccer, Vladan Papi&#263;, IntechOpen, DOI: 10.5772/7353.

Zool Hilmi Ismail and Nohaidda Sariff (November 5th 2018). A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions, Applications of Mobile Robots, Efren Gorrostieta Hurtado, IntechOpen, DOI: 10.5772/intechopen.79337.

Dmitry Osipov (March 1st 2010). Novel Multiple Access Models and Their Probabilistic Description, Trends in Telecommunications Technologies, Christos J Bouras, IntechOpen, DOI: 10.5772/8483. 

New Zealand

Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions. For agents whose objectives are aligned, and where one agent can help another achieve greater reward by sacrificing some of its own reward, their cooperative commitment should be chosen to maximize their joint reward. We present a solution to the problem of how cooperative agents can efficiently find an (approximately) optimal commitment by querying about carefully selected commitment choices. We prove structural properties of the agents&#39; values as functions of the parameters of the commitment specification, and develop a greedy method for composing a query with provable approximation bounds, that we empirically show can find nearly optimal commitments in a fraction of the time that methods that lack our insights require.

AAMAS 2020

Efficient Querying for Cooperative Commitments

 **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. 

Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions. For agents whose objectives are aligned, and where one agent can help another achieve greater reward by sacrificing some of its own reward, their cooperative commitment should be chosen to maximize their joint reward. We present a solution to the problem of how cooperative agents can efficiently find an (approximately) optimal commitment by querying about carefully selected commitment choices. We prove structural properties of the agents' values as functions of the parameters of the commitment specification, and develop a greedy method for composing a query with provable approximation bounds, that we empirically show can find nearly optimal commitments in a fraction of the time that methods that lack our insights require.

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). 

Optimal Swarm Strategy for Dynamic Target Search and Tracking

"We present VerSecTis -- a new experimental tool for the verification of timed security protocols' (TSP) modelled by Timed Interpreted Systems (TIS).
In addition to the TSP's time-independent properties, our tool can also examine the time dependencies of the TSP's executions on which their security depends.
The verification method consists of a new TSPs' modelling method and
a translation of the reachability problem for TIS to the Satisfiability Modulo Theories problem. We also deliver nineteen TSPs to verify, and we plan to expand the tool with further protocols."

VerSecTis - An Agent Based Model Checker for Security Protocols (Demo)

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**Abstract:**

Hurricanes are devastating natural disasters. In deciding how to respond to a hurricane, in particular whether and when to evacuate, a decision-maker must weigh often highly uncertain and contradictory information about the future path and intensity of the storm. To effectively plan to help people during a hurricane, it is crucial to be able to predict and understand this evacuation decision. To this end, we propose a computational model of human sequential decision-making in response to a hurricane based on a Partial Observable Markov Decision Process (POMDP) that models concerns, uncertain beliefs about the hurricane, and future information. We evaluate the model in two ways. First, hurricane data from 2018 was used to evaluate the model's predictive ability on real data. Second, a simulation study was conducted to qualitatively evaluate the sequential aspect of the model. The evaluation with hurricane data shows that our proposed features are significant predictors and they can predict the data well, within and across distinct hurricane datasets. The simulation results show that, across different setups, our model generates predictions on the sequential decisions making aspect that align with expectations qualitatively and suggests the importance of modeling information.

A Computational Model of Human Hurricane Evacuation Decision

We propose a new perspective on adversarial attacks against deep reinforcement learning agents. Our main contribution is CopyCAT, a targeted attack able to consistently lure an agent into following an outsider's policy. It is pre-computed, therefore fast inferred, and could thus be usable in a real-time scenario. We show its effectiveness on Atari 2600 games in the novel read-only setting. In this setting, the adversary cannot directly modify the agent's state - its representation of the environment - but can only attack the agent's observation - its perception of the environment. Directly modifying the agent's state would require a write-access to the agent's inner workings and we argue that this assumption is too strong in realistic settings.

CopyCAT: Taking Control of Neural Policies with Constant Attacks

In scenarios that require teamwork, we usually have at hand a variety of specific tasks, for which we need to form a team in order to carry out each one. Here we target the problem of matching teams with tasks within the context of education, and specifically in the context of forming teams of students and allocating them to internship programs. We provide a formalization of the problem, and show the computational hardness of solving it optimally.
Thereafter, we propose a heuristic anytime algorithm that generates an initial team allocation which later on attempts to improve in an iterative process.
Moreover, we present the results of our evaluation, and compare the results with a state-of-the-art optimizer.

TAIP: an anytime algorithm for allocating student teams to internship programs

Theory of Mind is commonly defined as the ability to attribute mental states (e.g., beliefs, goals) to oneself, and to others. A large body of previous work---from the social sciences to artificial intelligence---has observed that Theory of Mind capabilities are central to providing an explanation to another agent or when explaining that agent’s behaviour. In this paper, we build and expand upon previous work by providing an account of explanation in terms of the beliefs of agents and the mechanism by which agents revise their beliefs given possible explanations. Our account satisfies a number of important desiderata which we posit must be met by accounts of explanation that purport to employ Theory of Mind, in contrast to previous work which has addressed some but not all of these desiderata.

Towards the Role of Theory of Mind in Explanation

Maximizing Plan Legibility in Stochastic Environments (Extended Abstract)

In this work, the Distributed Bayesian (D-Bay) algorithm is applied to an autonomous search use case. Within the use case multiple unmanned aerial vehicles equipped with cameras cooperatively search an area and minimize the required time. The use case is mod- eled within the continuous Distributed Constraint Optimization Problem (DCOP) framework. This framework extends the (discrete) DCOP framework by allowing variables with continuous domains. Compared to similar DCOP solvers, the characteristics of the D- Bay algorithm are well suited for the use case and allow for the implementation on autonomous vehicles with limited resources (computational power, memory, and communication bandwidth). Experimental results are given and these results are used to vali- date a simulation environment. Within the simulation environment various scenarios are implemented. The D-Bay algorithm was able to find solutions within 3.5 % of the optimal solution with a limited amount of samples per agent.

The Distributed Bayesian algorithm: simulation and experimental results

Multirobot systems for covering environments are increasingly used in applications like cleaning, industrial inspection, patrolling, and precision agriculture. The problem of covering a given environment using multiple robots can be naturally formulated and studied as a multi-Traveling Salesperson Problem (mTSP). In a mTSP, the environment is represented as a graph and the goal is to find tours (starting and ending at the same depot) for the robots in order to visit all the vertices with minimum global cost, namely the length of the longest tour. The mTSP is an NP-hard problem for which several approximation algorithms have been proposed. These algorithms usually assume generic environments, but tighter approximation bounds can be reached focusing on specific environments. In this paper, we address the case of environments composed of sub-parts, called modules, that can be reached from each other only through some linking structures. Examples are multi-floor buildings, in which the modules are the floors and the linking structures are the staircases or the elevators, and floors of large hotels or hospitals, in which the modules are the rooms and the linking structures are the corridors. We focus on linear modular environments, with the modules organized sequentially, presenting an efficient (with polynomial worst-case time complexity) algorithm that finds a solution for the mTSP whose cost is within a bounded distance from the cost of the optimal solution. The main idea of our algorithm is to allocate disjoint "blocks" of adjacent modules to the robots, in such a way that each module is covered by only one robot. We experimentally compare our algorithm against some state-of-the-art algorithms for solving mTSPs in generic environments and show that it is able to provide solutions with lower makespan and spending a computing time several orders of magnitude shorter.

Efficient Querying for Cooperative Commitments

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Efficient Querying for Cooperative Commitments

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Optimal Swarm Strategy for Dynamic Target Search and Tracking

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

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