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Sequential decision-making
Definition:
Sequential decision-making refers to the process of making a series of decisions over time, where each decision is influenced by previous actions and outcomes. In the context of computer science and artificial intelligence, sequential decision-making often involves optimizing actions in dynamic environments to achieve specific goals or outcomes. Techniques such as reinforcement learning are commonly used to address problems involving sequential decision-making.
Understanding Sequential Decision-Making in Artificial Intelligence
Sequential decision-making is a fundamental concept in the field of artificial intelligence that involves making a series of decisions over time with the goal of achieving a specific objective. In various AI applications such as robotics, game playing, and autonomous vehicles, agents must make decisions in a sequential manner to maximize their overall performance.
Key Components of Sequential Decision-Making
There are three key components involved in sequential decision-making:
1. States: States represent the current situation or configuration in which the decision-maker finds itself. These states can be represented in various forms depending on the application, such as game boards, sensor readings, or environmental conditions.
2. Actions: Actions are the possible choices that the decision-maker can take from a given state. These actions will lead to different outcomes, affecting the subsequent states and rewards received by the agent.
3. Rewards: Rewards are the feedback signals that indicate how well the decision-maker is performing. The goal of sequential decision-making is to maximize the cumulative reward over time by selecting the best sequence of actions.
Reinforcement Learning and Markov Decision Processes
In the context of artificial intelligence, reinforcement learning and Markov decision processes (MDPs) are commonly used frameworks for modeling and solving sequential decision-making problems.
Reinforcement Learning: Reinforcement learning is a type of machine learning approach where an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions, enabling it to learn the optimal policy for maximizing long-term rewards.
Markov Decision Processes: MDPs provide a formal mathematical framework for modeling sequential decision-making under uncertainty. In an MDP, the agent perceives the environment in states and takes actions to transition between states, aiming to maximize the expected cumulative reward. The Markov property assumes that the future state depends only on the current state and action, making MDPs well-suited for modeling dynamic decision-making scenarios.
Overall, sequential decision-making plays a crucial role in enabling artificial intelligence systems to act intelligently in a wide range of complex environments. By incorporating principles from reinforcement learning and Markov decision processes, AI agents can effectively navigate uncertain and dynamic decision spaces to achieve desired objectives.
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