Since the emergence of deep neural network (DNN), it has achieved excellent performance
in various research areas. As the combination of DNN and reinforcement learning, deep …

Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates
on pseudo-random hypervectors, an information-rich, hardware-efficient representation that …

In recent years, one of the highest challenges in the field of artificial intelligence has been
the creation of systems capable of learning how to play classic games. This paper presents …

As a popular game around the world, Snakes has multiple modes with different settings. In
this work, we are dedicated to the 3v3 Snakes, which is characterized by a complex mixture …

To perform well, Deep Reinforcement Learning (DRL) methods require significant memory
resources and computational time. Also, sometimes these systems need additional …

Answer set programming is a well-understood and established problem-solving and
knowledge representation paradigm. It has become more prominent amongst a wider …

We describe the design and implementation of an autonomous prototype vehicle which finds
an empty parking slot in a parking area, and parks itself in the empty parking slot, using …

Neurocomputational modelling of long-term memory is a core topic in computational
cognitive neuroscience, which is essential towards self-regulating brain-like AI systems. In …

Nowadays, many control systems rely on model-based approaches, which require an effort
increasing with the complexity of the tackled issue. An alternative to these approaches is …

This paper proposes a zonation method for training the two reinforcement learning agents.
We demonstrate the method's effectiveness in the double snake game. The game consists of …