Faris Kurniawan
Papers
1
Total Citations
4
H-Index
1
About
Faris Kurniawan is a researcher at the intersection of artificial intelligence and game theory, with a primary focus on developing intelligent agents for strategic gameplay. His most cited work, "Evolutionary Neural Network for Othello Game" (2012), explores how evolutionary algorithms can train neural networks to master complex board games, demonstrating a novel approach to creating adaptive AI opponents. This foundational paper, with 4 citations, showcases his early contribution to combining machine learning with game-playing strategies—a field that bridges cognitive science and computational intelligence. Kurniawan’s research addresses the fundamental challenge of mimicking human decision-making processes in AI, particularly how machines can learn optimal moves through iterative evolution rather than brute-force computation. His work resonates with students and researchers interested in neuroevolution, reinforcement learning, and the practical application of AI in entertainment and simulation. While his citation count is modest, his study remains a relevant reference for those exploring lightweight, evolutionary approaches to game AI, offering a clear example of how neural networks can be optimized without extensive datasets.
Research Focus
Key Achievements
Top Papers
- 1Evolutionary Neural Network for Othello Game4 citations · 2012