New Zealand

Lena Mariann Garder and Mats Hovin (September 1st 2007). Evolutionary Strategies Combined With Novel Binary Hill Climbing Used for Online Walking Pattern Generation in Two Legged Robot, Bioinspiration and Robotics Walking and Climbing Robots, Maki K. Habib, IntechOpen, DOI: 10.5772/5509.

Takashi Kuremoto, Takaomi Hirata, Masanao Obayashi, Shingo Mabu and Kunikazu Kobayashi (April 3rd 2019). Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting, Time Series Analysis - Data, Methods, and Applications, Chun-Kit Ngan, IntechOpen, DOI: 10.5772/intechopen.85457.

Rogfel Thompson Martinez and Sadek C. Absi Alfaro (March 10th 2020). Data Analysis and Modeling Techniques of Welding Processes: The State-of-the-Art [Online First], IntechOpen, DOI: 10.5772/intechopen.91184. 

We consider Deep Reinforcement Learning (DRL) approaches to devise mapless navigation strategies for mobile platforms. We propose a Genetic Deep Reinforcement Learning (GDRL) method that combines Genetic Algorithms (GA) with discrete and continuous action space DRL approaches. The goal of GDRL is to reduce the sensitivity of DRL approaches to their hyper-parameter tuning and to provide robust exploration strategies. We evaluate GDRL in combination with Rainbow and Proximal Policy Optimization (PPO) in two navigation scenarios: i) a wheeled robot avoiding obstacles in an indoor environment and ii) a water drone that must reach a predefined location in presence of waves. Our empirical evaluation demonstrates that GDRL outperforms state-of-the-art DRL and GA methods as well as a previous hybrid approach.


AAMAS 2020

Genetic Deep Reinforcement Learning for Mapless Navigation

water drones

mapless navigation

evolutionary strategies

deep reinforcement learning

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 2020

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). 

Multi-agent path finding (MAPF), naturally exists in applications like picking-up and dropping-off parcels by automated guided vehicles (AGVs) in the warehouse. Existing algorithms, like conflict-based search (CBS), windowed hierarchical cooperative A* (WHCA), and other A* variants, are widely used to find the shortest paths in different manners. However, in real-world environments, MAPF cases are dynamically generated and need to be solved in real time. In this work, a decentralized multi-agent reinforcement learning (MARL) framework with multi-step ahead tree search (MATS) strategy is proposed to make efficient decisions. Through performing experiments on a 30*30 grid world and a real-world warehouse case, our proposed MARL policy is proved to be capable of: 1) scaling to a large number of agents in real-world environment with online response time within acceptable levels; 2) outperforming existing algorithms with shorter path length and solution time, as the number of agents increases.


Learning to Cooperate: Application of Deep Reinforcement Learning for Online AGV Path Finding

In this talk, we study a variant of hedonic games, called Seat Arrangement. 
The model is defined by a bijection from agents with preferences to vertices in a graph.
The utility of an agent depends on the neighbors in the graph. 
In this talk, we study the price of stability and fairness in Seat Arrangement, and the computational complexity and the parameterized complexity of finding certain desirable seat arrangements, say Maximum Welfare Arrangement, Maximin Utility Arrangement, Stable Arrangement, and Envy-free Arrangement.


Hedonic Seat Arrangement Problems

In order to be useful in the real world, an AI agent needs to plan and act in the presence of other agents, who may be helpful or disruptive. In this paper, we consider the problem where an autonomous agent needs to act in a manner that clarifies its objectives to cooperative agents while simultaneously preventing adversarial agents from inferring those objectives. We call it Mixed-Observer Controlled Observability Planning Problem (MO-COPP). We develop two new solution approaches: one provides an optimal solution to the problem given a fixed time horizon by using an integer programming solver, the other provides a satisficing solution using heuristic-guided forward search to achieve prespecified amount of obfuscation and legibility for adversarial and cooperative agents respectively. 


Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility

In many multi-agent interactions in the real world, agents receive payoffs over multiple distinct criteria; i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may lead to different utilities for each participant. Therefore, it is essential for an agent to learn about the behaviour of other agents in the system. We investigate here the effects of opponent modelling on multi-objective multi-agent interactions with non-linear utilities. Specifically, we consider multi-objective normal form games (MONFGs) with non-linear utility functions under the scalarised expected returns optimisation criterion. We contribute a novel actor-critic formulation to allow reinforcement learning of mixed strategies in this setting, along with an extension that incorporates opponent policy reconstruction using conditional action frequencies. Our empirical results demonstrate that opponent modelling can drastically alter the learning dynamics in this setting.


Opponent Modelling for Reinforcement Learning in Multi-Objective Normal Form Games

Too many coocks: Coordinating multi-agent collaboration through inverse planning

Reinforcement learning and planning have been revolutionized in recent years, due in part to the mass adoption of deep convolutional neural networks and the resurgence of powerful methods to refine decision-making policies. However, the problem of sparse reward signals remains pervasive in many domains. While various reward-shaping mechanisms and imitation learning approaches have been proposed to mitigate this problem, a mathematically rigorous structure of the underlying objective is rarely exploited. In this paper, we resolve this by defining objectives using linear temporal logic over finite traces (LTL_f) and utilize the automaton representation of said objectives in order to define novel reward shaping functions that mitigate the sparse rewards problem within modern Monte Carlo Tree Search (MCTS) methods. We further demonstrate that such verification-guided reward shaping can be utilized to facilitate transfer learning between different environments when the objective is the same.


Verification-Guided Tree Search

This presentation describes the use of CArtAgO artifacts to represent virtual resources exposed by an agent in a hypermedia context as an extension of the Multi-Agent MicroServices architecture.

Explicit Modelling of Resources for Multi-Agent Microservices using the CArtAgo framework

Simulated games have become a dominant platform for multiagent intelligence research in recent years. Previous works have succeeded on arcade, first person shooter (FPS), real-time strategy (RTS), and massive online battle arena (MOBA) games. Our work considers massively multiplayer online role-playing games (MMORPGs or MMOs), which capture several complexities of real-world learning that are not well modeled by any other game genre. We present a massively multiagent game environment inspired by MMOs and demonstrate that simple policy gradient methods produce interesting emergent exploration and specialization behaviors.


Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks

A key assumption in Distributed Constraint Optimization Problem (DCOP) model is that all constraints are fully specified or known a priori, which may not hold in applications where constraints encode preferences of human users. We extend the model to Incom- plete DCOPs (I-DCOPs), where some constraints can be partially specified. User preferences for these partially-specified constraints can be elicited during the execution of I-DCOP algorithms, but they incur some elicitation costs. Additionally, we extend the Synchro- nous Branch-and-Bound (SyncBB) algorithm to solve I-DCOPs. Our model extends the state of the art in distributed constraint reason- ing to better model and solve distributed agent-based applications with user preferences.


Embedding Preference Elicitation Within the Search for DCOP Solutions

Distributed Constraint Optimization Problems (DCOPs) are a suitable formulation for coordinating interactions (i.e. constraints) in cooperative multi-agent systems. The traditional DCOP model deals with variables that can take only discrete values. However, there are many applications where the variables are continuous decision variables. The existing methods for solving DCOPs with continuous variables come with a huge computation and communication overhead. In this paper, we apply continuous non-linear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm. Empirical results show that our algorithm is able to provide high-quality solutions at the expense of small communication cost and execution time.


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Next from AAMAS 2020

Learning to Cooperate: Application of Deep Reinforcement Learning for Online AGV Path Finding

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Deepbots: A Webots-based Deep Reinforcement Learning Framework for Robotics

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