Rational Agent.pptx
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A rational agent is an artificial intelligence agent that has clear preferences, models uncertainty, and acts to maximize its performance based on possible actions. Rational agents are designed to make the right decisions using techniques from game theory and decision theory for real-world problems. An AI agent is considered rational if it selects the best possible action in each situation to receive a positive reward, avoiding wrong actions that result in negative rewards. The rationality of an agent depends on factors like its performance measure, prior knowledge of its environment, available actions, and percepts.
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Rational Agent.pptx
- 1. Rational Agent DR. SUCHITA BHOVAR
- 2. Rational Agent A rational agent is an agent which has clear preference, models uncertainty, and acts in a way to maximize its performance measure with all possible actions. A rational agent is said to perform the right things. AI is about creating rational agents to use for game theory and decision theory for various real-world scenarios. For an AI agent, the rational action is most important because in AI reinforcement learning algorithm, for each best possible action, agent gets the positive reward and for each wrong action, an agent gets a negative reward.
- 3. Rational Agent The rationality of an agent is measured by its performance measure. Rationality can be judged on the basis of following points: Performance measure which defines the success criterion. Agent prior knowledge of its environment. Best possible actions that an agent can perform. The sequence of percepts. Rationality differs from Omniscience because an Omniscient agent knows the actual outcome of its action and act accordingly, which is not possible in reality.
- 4. A rational agent should depend on the following factors at any given time: • Perception: The ability to perceive the current state of the environment and gather relevant information. • Actuators: The ability to take actions within the environment to achieve its goals. • Performance measure: A way to evaluate the success or failure of the agent’s actions. • Information: The agent’s knowledge of its environment, which may come from past experiences or other sources.