New Zealand

Jeffrey Too Chuan Tan, Feng Duan, Ryu Kato and Tamio Arai (April 1st 2010). Collaboration Planning by Task Analysis in Human-Robot Collaborative Manufacturing System, Advances in Robot Manipulators, Ernest Hall, IntechOpen, DOI: 10.5772/9543.

Igor Goncharenko, Mikhail Svinin, Shigeyuki Hosoe and Sven Forstmann (April 1st 2010). On the Influence of Hand Dynamics on Motion Planning of Reaching Movements in Haptic Environments, Advances in Haptics, Mehrdad Hosseini Zadeh, IntechOpen, DOI: 10.5772/8693. 

Yusie Rizal (August 8th 2019). Computer Simulation of Human-Robot Collaboration in the Context of Industry Revolution 4.0, Becoming Human with Humanoid - From Physical Interaction to Social Intelligence, Ahmad Hoirul Basori, Ali Leylavi Shoushtari and Andon Venelinov Topalov, IntechOpen, DOI: 10.5772/intechopen.88335.

AAMAS 2020

Maximizing Plan Legibility in Stochastic Environments (Extended Abstract)

planning; legibility; human-robot collaboration

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

Conditional Updates of Answer Set Programming and its Applicationin Explainable Planning

Optimal Swarm Strategy for Dynamic Target Search and Tracking

In this demonstration, we introduce a system that facilitates trading agent competitions. Competitions mirror a Walrasian Exchange Economy. Each agent is endowed with a set of digital assets and preferences over them. Agents then search for sellers of the assets they demand and buyers of the assets they sell. They negotiate one- on-one to arrive at an optimal trade, and if successful, settle their transaction trustlessly on an emulated permissionless blockchain. This system is a precursor to a trading platform for digital assets and crypto-tokens in which agents trade on behalf of their users.


Trading Agent Competition with Autonomous Economic Agents

"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 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.

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