Joaquim Bento Cavalcante-Neto

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About

Joaquim Bento Cavalcante-Neto is a researcher at the forefront of artificial life and autonomous virtual characters, exploring how behavior emerges from the interplay between an agent’s internal dynamics and its environment. His work is grounded in Embodied and Enactive Artificial Intelligence, challenging traditional top-down control by advocating for intrinsic autonomy. In his most cited paper, "Evolving Plastic Neuromodulated Networks for Behavior Emergence of Autonomous Virtual Characters" (2013, 7 citations), Cavalcante-Neto demonstrates how neuromodulated neural networks can evolve to produce natural, adaptive behaviors in virtual agents without explicit programming. This is complemented by "Emergence of Autonomous Behaviors of Virtual Characters through Simulated Reproduction" (2013, 5 citations), which uses simulated evolution to generate self-organizing behaviors. His more recent work, "Towards intrinsic autonomy through evolutionary computation" (2019, 5 citations), synthesizes these ideas into a framework for designing agents that are truly autonomous—acting from internal drives rather than external commands. Though his citation counts are modest, Cavalcante-Neto’s contributions are conceptually significant, offering a blueprint for creating lifelike, self-motivated characters in virtual worlds. His research is a must-read for students and researchers interested in evolutionary robotics, artificial life, and the philosophical underpinnings of agency.