In a problem facing agent, a situation can be categorized as different patterns and action can be taken based on the available information (known as method) as oppose to a simple value. Doing so will decrease the variety of situations and actions and as a result simplify the problem. Simple patterns and methods are generated at first but by detecting important patterns and methods and creating similar patterns and methods, the agent will be able to better recognize the situation it's in and find better solutions for the patterns respectively and as a result systematically broaden its knowledge over time. By memorizing feelings (or rewards) and action result (situation) in a pattern, it's possible to make a tree of possible outcomes of an action related to a pattern and choose an action of the pattern that profit us the most by predicting future feelings and calculating the value and we know accuracy of our prediction based on similarity (or consistency) and number of results (or confidence). I've also given my opinion and defined some standards regarding artificial intelligence, reinforcement learning, and designing agent in this paper.