Gunawan Gunawan

Institut Sains dan Teknologi Terpadu Surabaya

Papers

Total Citations

H-Index

About

Gunawan Gunawan’s research lies at the intersection of artificial intelligence, game theory, and evolutionary computation, with a particular focus on developing intelligent agents for strategic gameplay. His most-cited work, “Evolutionary Neural Network for Othello Game” (2012), demonstrates a pioneering approach to combining neural networks with evolutionary algorithms, enabling AI systems to learn and adapt their strategies autonomously. By training networks to play Othello—a classic board game requiring deep positional reasoning—Gunawan showed how evolutionary methods could optimize decision-making without explicit programming, a contribution that has garnered 4 citations and influenced subsequent studies in game AI. This work underscores his broader interest in mimicking human cognitive processes through computational models, a theme that runs through his research. While his citation count is modest, his early adoption of evolutionary neural networks for game playing marks him as a thoughtful contributor to the field, offering a foundation for students and researchers exploring adaptive AI in constrained environments. His work remains a useful reference for those investigating how machine learning can replicate strategic human thought.