Dr. András Németh, 2026. "Computational Serendipity: Engineering Unexpected Discovery Through Machine Learning", Journal of Machine Learning and Computational Intelligence (JMLCI) 1(1): 90-109.

It has become a new research paradigm, referred to as Computational Serendipity, which aims to design scenarios that allow intelligent systems to experience unexpected but meaningful discoveries. Historically, serendipitous discoveries have had been accidental observations, fortunate mistakes and unexpected connections that led to important advances in science, technology and the arts. But, the accelerated pace of development in Artificial Intelligence, Machine Learning, Cognitive Computing, and Autonomous Reasoning systems provides opportunities to change serendipity from a predominantly accidental event into a process that is computer-supported. Computational Serendipity aims at designing intelligent systems that can explore hidden relationships, weak signals and latent opportunities in complex information environments thereby enabling organizations to discover unconventional knowledge pathways that may have never been thought of or discovered otherwise. Instead of passing or optimizing only selected known objectives, such systems explore solution spaces that are unknown and deliver novel insights that drives invention and discovery.
This research explores the newly launched field of Computational Serendipity and how advanced machine learning architectures facilitate transdisciplinary unexpected discovery in scientific, industrial, educational, and creative domains. It explores theoretical principles of discovery intelligence, neuro-symbolic reasoning architectures, latent pattern mining systems, artificial curiosity agents, autonomous generation of hypotheses and semantic divergence networks for knowledge emergence. It especially focuses on exploring how this machine-learning "goes beyond prediction and classification" towards exploratory intelligence that can generate both novel and unexpected results.
The research delves deeper into the work and role of Generative AI, Foundation Models, Cognitive Digital Twins, Multi Agent Discovery Ecosystems, and Quantum-Inspired Knowledge Navigation frameworks to accelerate computational discovery processes. They offer the computational backbone needed for large-scale exploration of complex knowledge spaces while retaining contextual and adaptive learning capabilities needed. The paper also tackles challenges related to explainability, trust calibration, ethical governance, and reasonable finishing of discovery-oriented AI systems.The results indicate that Computational Serendipity is a powerful development in the field of intelligent system design, where machines behave as discoverers rather than mere analytical tools. Combining curiosity-driven learning, semantic reasoning, and autonomous exploration abilities could make future intelligent systems able to produce innovative breakthroughs that transform science and expand human knowledge in ways never before thought possible.

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