New Zealand

Yogeswaran M. and Ponnambalam S. G. (November 2nd 2010). Swarm Robotics: An Extensive Research Review, Advanced Knowledge Application in Practice, Igor Fuerstner, IntechOpen, DOI: 10.5772/10361. 

Mihai Datcu, Gottfried Schwarz and Corneliu Octavian Dumitru (February 25th 2020). Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures, Recent Trends in Artificial Neural Networks - from Training to Prediction, Ali Sadollah and Carlos M. Travieso-Gonzalez, IntechOpen, DOI: 10.5772/intechopen.90910.

Sofronov Vasily, Zhmailo Vadim and Yanilkin Yury (July 11th 2018). Benchmarks for Non-Ideal Magnetohydrodynamics, Numerical Simulations in Engineering and Science, Srinivas P. Rao, IntechOpen, DOI: 10.5772/intechopen.75713. 

With nearly every new swarm robotic platform built, the designers develop its software stack, from low-level drivers to high-level algorithmic implementations. And while the different software stacks frequently share components, especially in robot-to-robot communication, these common components are also developed from scratch time and again. We present SwarmTalk, a new communication library that can be quickly ported to new and existing swarm hardware. SwarmTalk adopts a publish-subscribe communication model that satisfies the severe hardware constraints found in many swarms, and provides an easy-to-use programming interface. We port our SwarmTalk prototype to two hardware swarm platforms and two simulator-based platforms, and implement commonly-used swarm algorithms on these four platforms. We present the design and implementation of SwarmTalk, discuss some of the system challenges in implementation and cross-platform porting, and report on our initial experiences as a common communication abstraction for a community benchmarking suite.

AAMAS 2020

SwarmTalk - Towards Benchmark Software Suites for Swarm Robotics Platforms

swarm robotics

benchmarks

communication

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

ELESSAR: Ethics in Norm-Aware Agents

Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows service robots or autonomous vehicles to operate at varying levels of autonomy and offer safety in situations that require human judgment. We propose an introspective model of autonomy that is learned and updated online through experience and dictates the extent to which the agent can act autonomously in any given situation. We define a competence-aware system (CAS) that explicitly models its own proficiency at different levels of autonomy and the available human feedback. A CAS learns to adjust its level of autonomy based on experience to maximize overall efficiency, factoring in the cost of human assistance. We analyze the convergence properties of CAS and provide experimental results for robot delivery and autonomous driving domains that demonstrate the benefits of the approach.


Learning to Optimize Autonomy in Competence Aware Systems

In this work, we present a competitive and cooperative heterogeneous deep reinforcement learning framework called C2HRL. C2HRL aims to learn a superior agent that exceeds the capabilities of the individual agent in an agent pool through two agent management mechanisms: one competitive, the other cooperative.

Competitive and Cooperative Heterogeneous Deep Reinforcement Learning (C2HRL)

Fair division is the problem of allocating a set of items among a set of agents in a fair and efficient way. It arises naturally in a wide range of real-life settings. Competitive equilibrium (CE) is a central solution concept in economics to study markets, and due to its remarkable fairness and efficiency properties (e.g., envy-freeness, proportionality, Pareto optimality), it is also one of the most preferred mechanisms for fair division even though there is no money involved.

The vast majority of work in fair division focuses on the case of disposable goods, which all agents like or can throw away at no cost. In this paper, we consider the case of mixed manna under linear utilities where some items are positive goods liked by all agents, some are bads (chores) that no one likes, an the remaining some agents like and others dislike. The recent work of Bogomolnaia et al. Econometrica '17, initiated the study of CE in mixed manna. They establish that a CE always exists and maintains all nice properties found in the case of all goods. However, computing a CE of mixed manna is genuinely harder than in the case all goods due to the non-convex and disconnected nature of the CE set. Our main result is a polynomial-time algorithm for computing a CE of mixed manna when the number of agents or items is constant.


Computing Competitive Equilibria of Mixed Manna

Conventional wisdom believes cryptocurrency miners should always work on particular token at their full power.
In this paper, however, we show that miners' equilibrium strategy deviates from it, 
which affects the system security and energy-efficiency.
Specifically, we model mining as a game where each miner has limited mining power and compete for multiple tokens. 
We analyze both pure Nash-equilibrium and Stackelberg-equilibrium of this game, deriving their closed-forms. 
It is suggested that miners might not exert full power, which, compared with fully-powered mining, provides less mining power for a token and thus makes it more vulnerable to attacks, while it helps to reduce energy consumption. 
Simulation results show that with more disparate capacity, this effect is more significant. 
Our results also show that miners should disperse power among all compatible tokens instead of only one, which matches realistic statistics well.


Games of Miners

In full-knowledge multi-robot adversarial patrolling, a group of robots has to detect an adversary who knows the robots' strategy. The adversary can easily take advantage of any deterministic patrolling strategy, which necessitates the employment of a randomised strategy. While the Markov decision process has been the dominant methodology in computing the penetration detection probabilities, we apply enumerative combinatorics to characterise the penetration detection probabilities. It allows us to provide the closed formulae of these probabilities and facilitates characterising optimal random defence strategies. Comparing to iteratively updating the Markov transition matrices, our methods significantly reduces the time and space complexity of solving the problem. We focus on one of four penetration configurations to highlight the technique.


Multi-Robot Adversarial Patrolling Strategies via Lattice Paths

Human language and thought are characterized by the ability to systematically generate a potentially infinite number of complex structures (e.g., sentences) from a finite set of familiar components (e.g., words). Recent works in emergent communication have discussed the propensity of artificial agents to develop a systematically compositional language through playing co-operative referential games. The degree of structure in the input data was found to affect the compositionality of the emerged communication protocols. Thus, we explore various structural priors in multi-agent communication and propose a novel graph referential game. We compare the effect of structural inductive bias (bag-of-words, sequences and graphs) on the emergence of compositional understanding of the input concepts measured by topographic similarity and generalization to unseen combinations of familiar properties. We empirically show that graph neural networks induce a better compositional language prior and a stronger generalization to out-of-domain data. We further perform ablation studies that show the robustness of the emerged protocol in graph referential games.


Analyzing structural priors in multi-agent communication

While reinforcement learning (RL) agents have the remarkable ability to learn by interacting with their environments, this process is often slow and data inefficient. Because environment interaction is typically expensive, many approaches have been studied to speed up RL. One popular method for doing so is to leverage human knowledge via imitation learning (IL), in which a demonstrator provides an example of the desired behavior, and the agent seeks to imitate. In this in-progress work, we propose a new way of integrating IL and deep RL, which we call corrected self imitation learning, where an agent provided with demonstration can learn faster compared to an agent with no demonstration. Our method does not increase the number of environmental interactions compared to a baseline RL method, and works well even in the case when the demonstrator is not an expert. We evaluate our method in the Atari game of Ms. Pac-Man and achieve promising results indicating our method has the potential to speed up deep RL algorithms.


Work-in-progress: Corrected Self Imitation Learning via Demonstrations

Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary RL research is to discover how to learn with less data. Previous work has shown that domain information can be successfully used to shape the reward; by adding additional reward information, the agent can learn with much less data. Furthermore, if the reward is constructed from a potential function, the optimal policy is guaranteed to be unaltered. While such potential-based reward shaping (PBRS) holds promise, it is limited by the need for a well-defined potential function. Ideally, we would like to be able to take arbitrary advice from a human or other agent and improve performance without affecting the optimal policy. The recently introduced dynamic potential based advice (DPBA) method tackles this challenge by admitting arbitrary advice from a human or other agent and improves performance without affecting the optimal policy. The main contribution of this paper is to expose, theoretically and empirically, a flaw in DPBA. Alternatively, to achieve the ideal goals, we present a simple method called policy invariant explicit shaping (PIES) and show theoretically and empirically that PIES succeeds where DPBA fails.



Downloads

Next from AAMAS 2020

ELESSAR: Ethics in Norm-Aware Agents

Similar lecture

Optimal Swarm Strategy for Dynamic Target Search and Tracking

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAMAS 2020

ELESSAR: Ethics in Norm-Aware Agents

Similar lecture

Optimal Swarm Strategy for Dynamic Target Search and Tracking

Downloads