Hendrawan Armanto
Papers
1
Total Citations
4
H-Index
1
About
Hendrawan Armanto is a researcher in artificial intelligence and game theory, with a focus on developing intelligent agents for strategic gameplay. His most cited work, "Evolutionary Neural Network for Othello Game" (2012), explores the intersection of evolutionary algorithms and neural networks to create adaptive AI capable of competing against human players. By modeling human cognitive processes and encoding them into machine learning frameworks, Armanto contributes to the broader goal of building more autonomous and responsive game-playing systems. While his citation count is modest, his research addresses foundational questions in AI—how machines can learn, adapt, and simulate human decision-making in constrained environments like board games. His work is particularly relevant for students and researchers interested in neuroevolution, game AI, and the application of biological principles to computational intelligence. Armanto’s approach underscores the importance of blending evolutionary strategies with neural architectures to solve complex, real-time strategic problems.
Research Focus
Key Achievements
Top Papers
- 1Evolutionary Neural Network for Othello Game4 citations · 2012