Which one is not a machine learning method? = Hill climbing method = Breadth first search = Binary search 3. Self driving AI agent represent = Continious 4. Which is our Current AI = General AI 5. Type of component in AI agent = 3 type 6. Which kind of agent have problem generator agent = learning Agent 7. key task Problem solving Agent = Solve the given problem and reach to goal & To find out which sequence of action will get it to the goal state both. 8. What will be the another name of blind search? = Uninformed search 9. State space are the combination of = both decission making and learning 10. which can be consider as initial state and goal state = Problem instance 11. which concept we are hiding the details representation = Abstraction 12. How many types of AI agents are avilable? = 5 types 2 number question 1. difference between two different type of agent in AI. There are several types of agents in artificial intelligence (AI), and each type of agent has different characteristics and capabilities. Here are some of the main differences between different types of agents: Reactive agents vs. deliberative agents: Reactive agents are designed to respond directly to their environment, without any internal model of the world or the ability to plan ahead. Deliberative agents have an internal model of the world, which they use to reason about their environment and plan their actions accordingly. Simple reflex agents vs. model-based reflex agents: Simple reflex agents operate based on a set of stimulus-response rules, which are pre-programmed to respond to certain environmental inputs with specific actions. Model-based reflex agents have an internal model of the world, which they use to update their rules and respond to changes in the environment. Goal-based agents vs. utility-based agents: Goal-based agents are designed to achieve specific goals, and they select actions that are likely to achieve those goals. Utility-based agents are designed to maximize a numerical utility function, which assigns values to different outcomes based on their desirability. Learning agents vs. rule-based agents: Learning agents are designed to learn from their experiences and update their behavior accordingly. Rule-based agents operate based on a set of pre-defined rules, and they do not adapt their behavior based on experience. Hybrid agents: Some agents combine different approaches to achieve more complex behavior. For example, a hybrid agent might use a reactive component for fast responses to changes in the environment, and a deliberative component for long-term planning. 2. universal connective In logic and artificial intelligence (AI), the universal connective is a logical operator that is used to express the universal quantification of a statement. The universal quantification expresses that a statement is true for all values of a variable in a given domain. In symbolic logic, the univers