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Jinan Fiaidhi, Sabah Mohammed, Lyle Chamarette and David Thomas
The common understanding of e-learning has shifted over the last decade from the traditional learning objects portals to learning paradigms that enforces constructivism, discovery learning and social collaboration. Such type of learning takes place outsi...
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Anestis Fachantidis, Matthew E. Taylor and Ioannis Vlahavas
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Yujie Wei, Hongpeng Zhang, Yuan Wang and Changqiang Huang
Maneuver decision-making is essential for autonomous air combat. However, previous methods usually make decisions to aim at the target instead of hitting the target and use discrete action spaces instead of continuous action spaces. While these simplific...
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Pedro Almeida, Vitor Carvalho and Alberto Simões
Reinforcement Learning is one of the many machine learning paradigms. With no labelled data, it is concerned with balancing the exploration and exploitation of an environment with one or more agents present in it. Recently, many breakthroughs have been m...
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Smita Mahajan, Shruti Patil, Moinuddin Bhavnagri, Rashmi Singh, Kshitiz Kalra, Bhumika Saini, Ketan Kotecha and Jatinderkumar Saini
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN). This is a first step towards the greater goal of developing fast-learning and robust RL age...
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Yusef Savid, Reza Mahmoudi, Rytis Maskeliunas and Robertas Dama?evicius
Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancem...
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Sara de Freitas, Victoria Uren, Kristian Kiili, Manuel Ninaus, Panagiotis Petridis, Petros Lameras, Ian Dunwell, Sylvester Arnab, Stephen Jarvis and Kam Star
Feedback is a critical aspect of optimised learning design, but there are few, if any, feedback models that map different types of feedback and how they may assist students to increase performance and enhance their learning experience. This research pape...
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Khuong Tran, Maxwell Standen, Junae Kim, David Bowman, Toby Richer, Ashlesha Akella and Chin-Teng Lin
Organised attacks on a computer system to test existing defences, i.e., penetration testing, have been used extensively to evaluate network security. However, penetration testing is a time-consuming process. Additionally, establishing a strategy that res...
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José Santos, Luís Duarte, João Ferreira, Ana Alves and Hugo Gonçalo Oliveira
This paper describes how we tackled the development of Amaia, a conversational agent for Portuguese entrepreneurs. After introducing the domain corpus used as Amaia?s Knowledge Base (KB), we make an extensive comparison of approaches for automatically ma...
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Dan Ezequiel Kröhling, Omar Chiotti, Ernesto Martínez
Pág. 135 - 149
Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. The behavior of a negotiation agent depends significantly on the influence of environmental conditions or contextual variables, since they ...
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Samah Felemban, Michael Gardner and Victor Callaghan
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Safaie Mangir,Zakirah Othman,Zulkifli Mohamed Udin
Pág. 270 - 279
The objective of this paper is to develop a framework on e-learning acceptance among agricultural extension agents in Malaysian agricultural sector. E-learning is viewed as a solution in response to the increasing need for learning and training. This pap...
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Collins N Udanor,Ogbonna U. Oparaku
Pág. pp. 10 - 18
Mobile devices have emerged as our daily companion whose applicability evolves as the day unfolds. One of such applications is in the area of learning, called mobile learning (M-learning). However, as with all new technologies, M-learning is faced with t...
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David GRIOL,Jose Manuel MOLINA,Araceli SANCHÍS DE MIGUEL
Pág. 13 - 26
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Leonor BECERRA-BONACHE,M. Dolores JIMÉNEZ LÓPEZ
Pág. 67 - 87
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Nina Kuchuk, Roman Artiukh, Artem Nechausov
Pág. 62 - 69
The subject matter of the article is semantic networks of distributed search in e-learning. The goal is to synthesize a decision tree and a stratified semantic network that allows network intelligent agents in the e-learning to construct inference mechan...
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Charl Maree and Christian Omlin
The increased complexity of state-of-the-art reinforcement learning (RL) algorithms has resulted in an opacity that inhibits explainability and understanding. This has led to the development of several post hoc explainability methods that aim to extract ...
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Yoshinari Motokawa and Toshiharu Sugawara
In this paper, we propose an enhanced version of the distributed attentional actor architecture (eDA3-X) for model-free reinforcement learning. This architecture is designed to facilitate the interpretability of learned coordinated behaviors in multi-age...
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Saad Awadh Alanazi, Maryam Shabbir, Nasser Alshammari, Madallah Alruwaili, Iftikhar Hussain and Fahad Ahmad
The research area falls under the umbrella of affective computing and seeks to introduce intelligent agents by simulating emotions artificially and encouraging empathetic behavior in them, to foster emotional empathy in intelligent agents with the overar...
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Yihan Niu, Feixiang Zhu, Moxuan Wei, Yifan Du and Pengyu Zhai
Maritime Autonomous Surface Ships (MASS) are becoming of interest to the maritime sector and are also on the agenda of the International Maritime Organization (IMO). With the boom in global maritime traffic, the number of ships is increasing rapidly. The...
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