Which agent deals with the happy and unhappy state?

Answer: Utility-based agent.

Explanation: A utility-based agent assigns a numeric utility value to each possible state or outcome that represents how desirable that state is (for example, higher utility for 'happy' states and lower utility for 'unhappy' states). The agent chooses actions that maximize expected utility, so it directly handles degrees of happiness and unhappiness rather than just achieving a goal.

• Model-based agent: builds and uses an internal model to predict and plan, but does not inherently assign desirability scores to states.

• Goal-based agent: aims to satisfy goals (typically binary satisfied/unsatisfied) and does not quantify how good different outcomes are.

• Learning agent: improves performance from experience; it may learn utilities but the learning capability itself is not the same as having a utility-based preference representation.

Simple example: If the agent assigns utility 1 to 'happy' and 0 to 'unhappy', it will choose actions that lead to the higher-utility (happy) outcome. This numeric scoring is what lets the utility-based agent handle varying degrees of desirability.