A transition model in artificial intelligence explains how systems move from one state to another after actions are performed. It plays a critical role in planning, search algorithms, robotics, and enterprise AI decision systems.
Skolemization in artificial intelligence is a core logic transformation that removes existential quantifiers from first-order logic using Skolem functions or constants. It plays a central role in automated reasoning, theorem proving, and symbolic AI systems.
Semantic nets in artificial intelligence help machines understand knowledge through connected concepts and relationships. They remain essential for explainable AI, enterprise reasoning, and knowledge-driven systems.
Bayes Rule in artificial intelligence helps machines update probabilities when new evidence appears. It powers spam filters, fraud detection, diagnosis systems, and explainable AI models.
Matching in artificial intelligence helps systems compare data, detect relevance, and connect information for search, reasoning, and decision-making across enterprise applications.
Yash Singh
Yash Singh
