[HTML][HTML] The neuroscience of spatial navigation and the relationship to artificial intelligence
Recent advances in artificial intelligence (AI) and neuroscience are impressive. In AI, this
includes the development of computer programs that can beat a grandmaster at GO or …
includes the development of computer programs that can beat a grandmaster at GO or …
Artificial intelligence meets radar resource management: A comprehensive background and literature review
A multi‐function radar is designed to perform disparate functions, such as surveillance,
tracking, fire control, amongst others, within a limited resource (time, frequency, and energy) …
tracking, fire control, amongst others, within a limited resource (time, frequency, and energy) …
Learning to predict consequences as a method of knowledge transfer in reinforcement learning
The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-
error learning. To be capable of efficient, long-term learning, RL agents should be able to …
error learning. To be capable of efficient, long-term learning, RL agents should be able to …
Transfer-based DRL for task scheduling in dynamic environments for cognitive radar
S Akbar, RS Adve, Z Ding… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Cognitive radars sense, interact with, and learn from the environment continuously. This
paradigm can be applied to a multifunction radar (MFR), which performs multiple functions …
paradigm can be applied to a multifunction radar (MFR), which performs multiple functions …
[HTML][HTML] Hippocluster: an efficient, hippocampus-inspired algorithm for graph clustering
Random walks can reveal communities/clusters in networks, because they are more likely to
stay within a cluster than leave it. Thus, one family of community detection algorithms uses …
stay within a cluster than leave it. Thus, one family of community detection algorithms uses …
[HTML][HTML] A bio-inspired reinforcement learning model that accounts for fast adaptation after punishment
Humans and animals can quickly learn a new strategy when a previously-rewarding strategy
is punished. It is difficult to model this with reinforcement learning methods, because they …
is punished. It is difficult to model this with reinforcement learning methods, because they …
Brain-Inspired Agents for Quantum Reinforcement Learning
In recent years, advancements in brain science and neuroscience have significantly
influenced the field of computer science, particularly in the domain of reinforcement learning …
influenced the field of computer science, particularly in the domain of reinforcement learning …
Parametrized Quantum Circuits for Reinforcement Learning
EM Andrés Núñez - 2024 - digibug.ugr.es
Quantum Computing (QC) is currently undergoing significant research despite being in its
early stage and facing limitations during the NISQ (Noisy Intermediate-Scale Quantum) era …
early stage and facing limitations during the NISQ (Noisy Intermediate-Scale Quantum) era …
Reinforcement Learning with Brain-Inspired Modulation Improves Adaptation to Environmental Changes
Developments in reinforcement learning (RL) have allowed algorithms to achieve
impressive performance in complex, but largely static problems. In contrast, biological …
impressive performance in complex, but largely static problems. In contrast, biological …
[BOOK][B] Artificial intelligence and soft computing
This volume constitutes the proceedings of the 21st International Conference on Artificial
Intelligence and Soft Computing ICAISC 2022, held in Zakopane, Poland, on June 19–23 …
Intelligence and Soft Computing ICAISC 2022, held in Zakopane, Poland, on June 19–23 …