This document discusses contextualization in artificial intelligence and describes several key concepts: 1. Contextualization is important for AI to understand complex user decisions and preferences based on natural language. Various algorithms will be developed to create and manage contexts using graph data structures and decision theory. 2. Natural language processing techniques like natural language understanding and natural language generation are discussed which allow AI systems to understand and generate human languages. 3. Personalization is described where systems learn individual user traits and preferences from their requests to provide customized responses and recommendations. 4. The paper concludes that a contextualization-based system will be developed using various graph algorithms to create a generalized system for decision making.