165,696 research outputs found
An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control.
With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment
Design of a Multi-Agent System for Process Monitoring and Supervision
New process monitoring and control strategies are developing every day together with process automation strategies to satisfy the needs of diverse industries. New automation systems are being developed with more capabilities for safety and reliability issues. Fault detection and diagnosis, and process monitoring and supervision are some of the new and promising growth areas in process control. With the help of the development of powerful computer systems, the extensive amount of process data from all over the plant can be put to use in an efficient manner by storing and manipulation. With this development, data-driven process monitoring approaches had the chance to emerge compared to model-based process monitoring approaches, where the quantitative model is known as a priori knowledge. Therefore, the objective of this research is to layout the basis for designing and implementing a multi-agent system for process monitoring and supervision. The agent-based programming approach adopted in our research provides a number of advantages, such as, flexibility, adaptation and ease of use. In its current status, the designed multi-agent system architecture has the three different functionalities ready for use for process monitoring and supervision. It allows: a) easy manipulation and preprocessing of plant data both for training and online application; b) detection of process faults; and c) diagnosis of the source of the fault. In addition, a number of alternative data driven techniques were implemented to perform monitoring and supervision tasks: Principal Component Analysis (PCA), Fisher Discriminant Analysis (FDA), and Self-Organizing Maps (SOM). The process system designed in this research project is generic in the sense that it can be used for multiple applications. The process monitoring system is successfully tested with Tennessee Eastman Process application. Fault detection rates and fault diagnosis rates are compared amongst PCA, FDA, and SOM for different faults using the proposed framework
Multi-Agent Deep Reinforcement Learning with Human Strategies
Deep learning has enabled traditional reinforcement learning methods to deal
with high-dimensional problems. However, one of the disadvantages of deep
reinforcement learning methods is the limited exploration capacity of learning
agents. In this paper, we introduce an approach that integrates human
strategies to increase the exploration capacity of multiple deep reinforcement
learning agents. We also report the development of our own multi-agent
environment called Multiple Tank Defence to simulate the proposed approach. The
results show the significant performance improvement of multiple agents that
have learned cooperatively with human strategies. This implies that there is a
critical need for human intellect teamed with machines to solve complex
problems. In addition, the success of this simulation indicates that our
multi-agent environment can be used as a testbed platform to develop and
validate other multi-agent control algorithms.Comment: 2019 IEEE International Conference on Industrial Technology (ICIT),
Melbourne, Australi
Whole-Chain Recommendations
With the recent prevalence of Reinforcement Learning (RL), there have been
tremendous interests in developing RL-based recommender systems. In practical
recommendation sessions, users will sequentially access multiple scenarios,
such as the entrance pages and the item detail pages, and each scenario has its
specific characteristics. However, the majority of existing RL-based
recommender systems focus on optimizing one strategy for all scenarios or
separately optimizing each strategy, which could lead to sub-optimal overall
performance. In this paper, we study the recommendation problem with multiple
(consecutive) scenarios, i.e., whole-chain recommendations. We propose a
multi-agent RL-based approach (DeepChain), which can capture the sequential
correlation among different scenarios and jointly optimize multiple
recommendation strategies. To be specific, all recommender agents (RAs) share
the same memory of users' historical behaviors, and they work collaboratively
to maximize the overall reward of a session. Note that optimizing multiple
recommendation strategies jointly faces two challenges in the existing
model-free RL model - (i) it requires huge amounts of user behavior data, and
(ii) the distribution of reward (users' feedback) are extremely unbalanced. In
this paper, we introduce model-based RL techniques to reduce the training data
requirement and execute more accurate strategy updates. The experimental
results based on a real e-commerce platform demonstrate the effectiveness of
the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge
Managemen
Intelligent Agents for Disaster Management
ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains
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