CALL FOR PAPERS OF INTEREST
1. CALL FOR PAPERS: Pattern Recognition Letters - Special Issue: Self-Learning Systems and Pattern Recognition and Exploitation (SeLSPRE)
Self-Learning Systems aim to achieve a goal - without being pre-programmed - in an environment that may be completely unknown initially. Self-learning algorithms are inspired by neuroscience and mimic the way the brain achieves cognition: they explore the environment following a try-and-error approach, or acquire knowledge from demonstrations provided by experts. The development of such a kind of system is pushed forward by AI technologies such as Reinforcement Learning, Inverse Reinforcement Learning, and Learning by Demonstration. Their application spams from robotics and autonomous driving up to healthcare and precision medicine.
This special issue focuses on pattern recognition and their successive exploitation by Self-Learning Systems. The way Inverse Reinforcement Learning or Learning by Demonstration extract patterns from ‘demonstrated trajectories’, and how such patterns are successively exploited by a self-learning algorithm to optimize its policy or fasten its learning process, is of interest of this special issue.
Topics of interest:
Inverse Reinforcement Learning
Learning-by-Demonstration and Imitation Learning
Pattern Recognition via Inverse Reinforcement Learning
Pattern Recognition from Demonstrations
Pattern exploitation in Self-Learning Systems
Pattern recognition in partially observable environments
Action-State trajectories analysis for pattern recognition and reward engineering
Pattern recognition and exploitation in Multi-Agent Self-Learning Systems
Pattern recognition and exploitation in Hierarchical Self-Learning Systems
Prospective authors should upload their submissions during the submission period through the Editorial Manager System
(https://www.editorialmanager.com/PRLETTERS/default.aspx), with the article type selected as “SeLSPRE". All submissions should be prepared by adhering to the PRLetters guidelines by taking into account that VSI papers follow the same submission rules as regular articles. The submissions should be original and technically sound, and they should not have been published previously, nor be under consideration for publication elsewhere. If the submissions are extended works of previously published papers, the original works should be quoted in the References and a description of the changes that have been made should be provided. All templates for preparing the submissions are available on the journal web site (https://www.elsevier.com/journals/pattern-recognition-letters/0167-8655/guide-for-authors)
Submission Period: 1-20 October 2021
1st Round Review: 15 December 2021
Revised Submission: 31 January 2022
2nd Round Review (if required): 15 March 2022
Final submission: 15 April 2022
Final decision: 1 May 2022
Giovanna Di Marzio
2. CALL FOR PAPERS: Frontiers in Artificial Intelligence has launched a new Research Topic entitled Self-Learning Systems.
Self-learning systems are artificial agents able to acquire and renew knowledge over the time by themselves, without any hard coding. These are adaptive systems whose functions improve by a learning process based - typically - on the method of trial and error, which is a learning paradigm inspired by neurosciences.
A self-learning system interacts with its users or surrounding environment initially by attempts and observes the changes produced by its actions.
The development of such a kind of systems is pushed forward by AI technologies such as Reinforcement Learning, Inverse Reinforcement Learning, and Learning by Demonstration. Nowadays, many application fields such as gaming, finance, banking, autonomous vehicles, healthcare and robotics are benefiting from the adoption of this paradigm.
This Research Topic aims at bringing together the most contemporary achievements and breakthroughs obtained by academia and industry in the fields of Self-Learning.
It falls within this Research Topic any new method or any development of well-known methods that allow to improve the performance, scalability, or generalization of such a kind of solution. As well, any successful application of self-learning methods to critical applications is of interest to this research topic.
Topics of interest:
Self-Learning enabling technologies: Reinforcement Learning, Inverse Reinforcement Learning and Learning-by-Demonstration
Partially observable environments
Uncertain or non-stationary environments
Exploitation VS Exploration trade-off
Multi-Agent Reinforcement Learning
Hierarchical Reinforcement Learning
Application fields: Automotive, Robotics, Healthcare, Finance, Gaming, Business management, Resource management, IoT and Industry 4.0
A detailed description of the Research Topic, as well as information about the submission, can be found at:
Submission is open. The official submission deadline for this research topic is on May 10th, 2021.