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Validating a Novel Conflict Resolution Strategy Selection Method (ConfRSSM) Via Multi-Agent Simulation
2017
International Journal of Advanced Computer Science and Applications
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Selecting a suitable conflict resolution strategy when conflicts appear in multi-agent environments is a hard problem. ...
strengths and confidence levels of the conflicting agents. ...
resolution in multi agent systems [24] . e) To validate (d) using agent-based simulation. ...
doi:10.14569/ijacsa.2017.080824
fatcat:uiruya4wlra6zigq2uptcxbgem
Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning
2021
Journal of Open Source Software
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Abmarl is a package for developing Agent-Based Simulations and training them with Multi-Agent Reinforcement Learning (MARL). ...
We define an Agent-Based Simulation Interface and Simulation Manager, which control which agents interact with the simulation at each step. ...
Multi-Agent Reinforcement Learning (MARL). ...
doi:10.21105/joss.03424
fatcat:irxecsdxjncyzf2watdatjq5qi
Educational Resources Recommendation System for a heterogeneous Student Group
2016
Advances in Distributed Computing and Artificial Intelligence Journal
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In this sense, multi-agent system (MAS) can be used to simulate the features of the students in the group, including their learning style, in order to help the professor find the best resources for your ...
Advances in Distributed Computing and Artificial Intelligence Journal ©Ediciones Universidad de Salamanca / cc by-nc-nd 21 KEYWORD ABSTRACT Educational resources; Metadata; Multi-agent systems; Recommendation ...
agent is the basis for creating the simulation of each student. ...
doi:10.14201/adcaij2016532130
fatcat:rljkhx6ng5dejchn2qeselg5ci
Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
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In particular, the algorithm called Asynchronous Advantage Actor-Critic has been extended to a multi-agent scenario in which every agent needs to learn to interact with other similar agents. ...
In this paper, this problem is addressed by developing a microscopic simulator using a Deep Reinforcement Learning Algorithm based on a combination of visual frames, representing the perception around ...
Agents are collectively trained in a multi-agent fashion so that cooperative behaviors can emerge, gradually inducing agents to learn to interact with each other. ...
arXiv:1903.01365v1
fatcat:ldklue4w7vfuxcm2grazbwuw74
A Teaching System for Hands-on Quadcopter Control
2019
IFAC-PapersOnLine
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Section 2 describes the multi-agent software system we developed to enable a distributed, flexible, and easyto-manage environment for teaching quadcopter control. ...
Section 2 describes the multi-agent software system we developed to enable a distributed, flexible, and easyto-manage environment for teaching quadcopter control. ...
doi:10.1016/j.ifacol.2019.08.120
fatcat:bsclj32ycbhkdfwnx7hsfksziu
Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect Environment Models
[article]
2020
arXiv
pre-print
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Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environment model. ...
However, it is often difficult to learn accurate models of environment dynamics, and even small errors may result in failure of Dyna agents. ...
In contrast, Uniterated One-step Predecessor and Multi-step Predecessor never update toward a simulated state. Their learning curves show superior performance to Q-learning. ...
arXiv:2006.04363v1
fatcat:ldzykfdgpran7bqtt2io4oyraa
A context-aware adaptive learning system using agents
2011
Expert systems with applications
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The proposed architecture is based upon multi-agent systems and uses both Sharable Content Object Reference Model (SCORM) 2004 and semantic Web ontology for learning content storage, sequencing and adaptation ...
This system has been implemented upon a well known open-source LMS and its functionalities are demonstrated through the simulation of a scenario mimicing the real life conditions. ...
The proposed system is based on multi-agent concepts. ...
doi:10.1016/j.eswa.2010.08.113
fatcat:6bcscawevfc4zdufgnsbmegl2a
An Artificial Neural Network (ANN)-Based Learning Agent for Classifying Learning Styles in Self-Regulated Smart Learning Environment
2021
International Journal of Emerging Technologies in Learning (iJET)
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Based on these skills, this paper develops a learning agent to classify students' learning styles using artificial neural networks (ANN), which mapped to Felder-Silverman Learning Style Model (FSLSM) as ...
Style dimensions. ...
The authors compared the simulated experiment with Felder-Silverman and Kolb's learning styles, multi-layer perceptron, and decision tree. ...
doi:10.3991/ijet.v16i18.24251
fatcat:garllrx3vzdbhhbikkjhefmurq
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
[article]
2021
arXiv
pre-print
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Together, this allows the user to easily run thousands of concurrent multi-agent simulations and train on extremely large batches of experience. ...
As such, WarpDrive is a fast and extensible multi-agent RL platform to significantly accelerate research and development. ...
Use imperative and stateful code to build complex multi-agent simulation logic with interacting agents. 7. ...
arXiv:2108.13976v3
fatcat:c2myxz6265fevj4xq6xryszd2a
Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition
[chapter]
2015
Lecture Notes in Computer Science
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, e.g., multi-modal, hybrid. ...user modeling and communication through learning and adaptation.
...interaction styles: Action-centered, embodied, multi-modal. ...knowledge repositories: Different levels ...
including other agents). ...
Knowledge as... ...multi-layered phenomenon appearing at different levels of abstraction. ...promoting interaction between levels. ...influenced by interaction between agent and environment (potentially ...
doi:10.1007/978-3-319-21365-1_4
fatcat:w5vi2xkbjjanlckvfhaumssmiq
VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
[article]
2019
arXiv
pre-print
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In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual ...
We propose this as a lightweight, flexible, and efficient solution for visual control, as 1) no extra transfer steps are required during the expensive training of DRL agents in simulation; 2) the trained ...
or unseen features of real images back into the simulated style, which the agents have already learned how to deal with during training in the simulation. ...
arXiv:1802.00265v4
fatcat:xefllbypnvdqhbrequ32dydqpy
Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football
[article]
2021
arXiv
pre-print
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We develop Markov chain Monte Carlo and deep learning-based algorithms that make use of the fluent objectives in order to learn from prior games and other games in the environment and increase the teams ...
Simulations of our approach using real-world datasets from 760 matches shows that by using optimised tactics with our fluent objective and prior games, we can on average increase teams mean expected finishing ...
We can learn a set of weights W that relate to how effective given style/formation pairs (actions that are made in the multi-step games) that we select in our games are against given oppositions style/ ...
arXiv:2102.09469v1
fatcat:ljhmw2m6ofez7mjkduvwvbqasu
Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning
2021
Engineering, Technology & Applied Science Research
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In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. ...
The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students' needs. ...
We have tried to propose a system that takes into consideration the learning styles, knowledge level, and disabilities of the students by applying the multi-agent approach and reinforcement learning to ...
doi:10.48084/etasr.3905
fatcat:n4vy5awym5cy3hrkj5tk3u4yhu
Improvising in Creative Symbolic Interaction
[chapter]
2016
Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore
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while "keeping in style". ...
Performers improvising along with Symbolic Interaction systems experiment a unique artistic situation where they interact with musical (and possibly multi-modal) agents which develop in their own ways ...
It will be possible in this way to construct intelligent multi-agent systems well equipped for dealing credibly with more complex musical situations involving a variety of styles. ...
doi:10.1142/9789813140103_0004
fatcat:zz2avfpjxbapfakzpkesy54pga
Ineffective Organizational Practices at NASA: A Dynamic Network Analysis
2005
Social Science Research Network
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As such, multi-agent network models can capture the complexities of NASA structure at various levels. ...
for the representation of individual cognitive agents who can take action, learn and alter the network -organizational adaptation. ...
Dynamic network analysis combines multi-level, multi-mode social network analysis with cognitive science and multi-agent simulation to provide a methodology for modeling the dynamics of complex and adaptive ...
doi:10.2139/ssrn.2726789
fatcat:iofoiik37zgbrfmlwrx2vhjozu
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