New Zealand

Vojtěch Grün, Marta Gimunová and Hana Válková (June 26th 2019). Cardiorespiratory Fitness and Intellectual Disability, Cardiorespiratory Fitness, Hasan Sözen, IntechOpen, DOI: 10.5772/intechopen.86824.

Adroaldo Guimarães Rossetti, Almir dos Santos Albuquerque, Vasco Pinto da Silva Filho and Rogério Cid Bastos (April 1st 2011). Multimedia Authorship Tool for the Teaching of Foreign Languages and Distance Learning in a Multiagent Environment, Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications, Faisal Alkhateeb, Eslam Al Maghayreh and Iyad Abu Doush, IntechOpen, DOI: 10.5772/15743.

Dennis Barrios-Aranibar and Luiz M. G. Gon&#231;alves (January 1st 2009). Influence Value Q-Learning: A Reinforcement Learning Algorithm for Multi Agent Systems, Theory and Novel Applications of Machine Learning, Meng Joo Er and Yi Zhou, IntechOpen, DOI: 10.5772/6675.

In many multiagent domains, and particularly in tightly coupled domains, teasing an agent&#39;s contribution to the system performance based on a single episodic return is difficult. This well-known difficulty hits state-to-action mapping approaches such as neural networks trained by evolutionary algorithms particularly hard. This paper introduces fitness critics, which leverage the expected fitness to evaluate an agent&#39;s performance. This approach turns a sparse performance metric (policy evaluation) into a dense performance metric (state-action evaluation) by relating the episodic feedback to the state-action pairs experienced during the execution of that policy. In the tightly-coupled multi-rover domain (where multiple rovers have to perform a particular task simultaneously), only teams using fitness critics were able to demonstrate effective learning on tasks with tight coupling while other coevolved teams were unable to learn at all.


AAMAS 2020

Multi-level Fitness Critics for Coevolution

cooperative coevolution

critics

fitness

multiagent learning

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

In many multiagent domains, and particularly in tightly coupled domains, teasing an agent's contribution to the system performance based on a single episodic return is difficult. This well-known difficulty hits state-to-action mapping approaches such as neural networks trained by evolutionary algorithms particularly hard. This paper introduces fitness critics, which leverage the expected fitness to evaluate an agent's performance. This approach turns a sparse performance metric (policy evaluation) into a dense performance metric (state-action evaluation) by relating the episodic feedback to the state-action pairs experienced during the execution of that policy. In the tightly-coupled multi-rover domain (where multiple rovers have to perform a particular task simultaneously), only teams using fitness critics were able to demonstrate effective learning on tasks with tight coupling while other coevolved teams were unable to learn at all.


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). 

We consider exploration tasks in which an autonomous mobile robot incrementally builds maps of initially unknown indoor environments. In such tasks, the robot makes a sequence of decisions on where to move next that, usually, are based on knowledge about the observed parts of the environment. 
In this paper, we present an approach that exploits a prediction of the geometric structure of the unknown parts of an environment to improve exploration performance. In particular, we leverage an existing method that reconstructs the layout of an environment starting from a partial grid map and that predicts the shape of partially observed rooms on the basis of geometric features representing the regularities of the indoor environment. Then, we originally employ the predicted layout to estimate the amount of new area the robot would observe from candidate locations in order to inform the selection of the next best location and to early stop the exploration when no further relevant area is expected to be discovered. Experimental activities show that our approach is able to effectively predict the layout of partially observed rooms and to use such knowledge to speed up the exploration.

Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms

Using Cognitive Models to Train Big Data Models with Small Data

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.

SwarmTalk - Towards Benchmark Software Suites for Swarm Robotics Platforms

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.


Downloads

Next from AAMAS 2020

Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms

Similar lecture

A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

.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

Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms

Similar lecture

A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads