Description: Sequential Decision Making is a process in which each decision made influences future decisions, creating a chain of interdependent choices. This approach is fundamental in the fields of artificial intelligence and reinforcement learning, where agents must evaluate the consequences of their actions in a dynamic environment. Unlike static decision-making, where options are considered in isolation, sequential decision-making requires continuous and adaptive evaluation. Agents must learn to anticipate how their decisions will affect the state of the environment and, consequently, their future options. This process involves the use of algorithms that allow modeling and optimizing decisions over time, considering both immediate and long-term rewards. The relevance of this approach lies in its ability to tackle complex problems where decisions are not independent, such as in games, robotics, and recommendation systems, where each action can open or close future opportunities. Sequential decision-making is based on Markov theory and optimization models, enabling systems to learn and adapt through accumulated experience.
History: Sequential Decision Making has its roots in decision theory and game theory, which were developed in the mid-20th century. One significant milestone was Richard Bellman’s work in the 1950s, who introduced the concept of dynamic programming, a mathematical approach to solving sequential decision problems. As computing advanced, these principles began to be applied in artificial intelligence, particularly in reinforcement learning, where agents learn to make decisions through interaction with their environment.
Uses: Sequential Decision Making is used in various fields, including robotics, where systems must make real-time decisions based on their environment. It is also applied in recommendation systems, where decisions about what content to display are based on past user interactions. In the gaming domain, it is used to develop optimal strategies in complex games like chess or Go.
Examples: An example of Sequential Decision Making is the AlphaGo algorithm, which uses reinforcement learning techniques to play Go, making decisions based on previous moves and anticipating the opponent’s responses. Another example is the use of control systems in autonomous vehicles, where each decision about direction and speed affects future decisions in a dynamic environment.
