In Artificial Intelligence (AI), which agent deals with happy and unhappy…

Answer: Utility based agent

Reason: A utility-based agent uses a utility function that assigns a numeric value to each state representing how desirable or "happy" that state is. The agent selects actions to maximize expected utility, so it explicitly prefers happier states and avoids unhappy ones.

• Utility function: assigns a score to states corresponding to preference or degree of happiness.

• Decision rule: choose actions that maximize expected utility, so the agent actively seeks happier states and avoids unhappy ones.

• Why other agent types are not the best match:

  • Simple reflex agent: acts only on current percepts using fixed rules and does not evaluate how desirable states are.

  • Model-based agent: maintains an internal model of the world, which helps with state estimation but does not by itself represent preferences or happiness.

  • Learning agent: can learn from experience and might learn preferences, but learning capability alone does not guarantee an explicit utility-based representation of happy versus unhappy states.