Agent approach

Agent approach

List the factors that determine the environment and define possible environments based on these.

• The environment refers to the surroundings or context in which an agent (like a robot or computer program) operates.
• Factors that determine the environment include things like the physical space, objects, and other agents present.
• Possible environments can be different settings, such as a room with obstacles, a virtual world, or even a real-world city.

What is the rational agent (by definition) and what is not?

• A rational agent is something (like a robot or program) that makes decisions based on what will lead to the best outcome or achieve its goals.
• It tries to act "smart" and make the best choices given the information it has.
• Something that is not a rational agent would make decisions that don't lead to good outcomes or don't align with its goals.

What does the rationality of an agent depend on (at a given moment)?

• The rationality of an agent depends on how well it achieves its goals in a given situation.
• At a given moment, the rationality depends on whether the agent makes decisions that maximize its chances of success.
• It's about choosing the best course of action based on the available information and its goals.

What are the specialties of the simple reflex agent?

• A simple reflex agent makes decisions based only on the current percept (sensory input).
• It doesn't have memory or the ability to plan for the future.
• It follows simple rules like "If I see X, then do Y" without considering the bigger picture.

What are the specialties of the model-based agent?

• A model-based agent not only considers the current percept but also maintains an internal model of the world.
• It can use this model to make predictions about how the world will change and plan accordingly.
• It can think ahead and make decisions based on its understanding of the environment.

What are the specialties of the goal-based agent?

• A goal-based agent has specific goals it wants to achieve.
• It considers the current situation, its goals, and a plan to reach those goals.
• It selects actions that will bring it closer to achieving its desired outcomes.

What are the specialties of the utility-based agent?

• A utility-based agent evaluates its actions based on a measure of "utility" or desirability.
• It assigns values to different outcomes and selects actions that maximize the overall utility.
• It considers the importance or preference of different outcomes when making decisions.

What are the specialties of the learning agent?

• A learning agent improves its performance over time through experience.
• It can adapt and change its behavior based on feedback from the environment.
• It can learn from past actions and observations to make better decisions in the future.

Searching

What are the differences between offline and online problems? (at problem solving agent)

• Offline problems: These are problems where the entire problem is known in advance. You have all the necessary information at the beginning, and you can work on solving it without any time constraints or changing conditions.
• Online problems: These are problems where the information is revealed gradually over time. You have to make decisions as you receive new information, and you may have to adapt your solution as the problem unfolds.

What are the components of a well-defined problem for search tasks?

• A well-defined problem for search tasks has three main components:
  • Initial state: It defines where the problem starts.
  • Actions: These are the possible steps or operations that can be taken to move from one state to another.
  • Goal test: It specifies the condition or criteria to check if a state is the desired goal state.

Describe the differences between single-state, conformant and contingency problems, and give one-one typical example.

• Single-state problem: In this type of problem, the initial state and the goal state are the same. The goal is to reach a specific state from the starting state. Example: Reaching a specific location in a maze.
• Conformant problem: In this type of problem, the agent doesn't have information about the current state. It can only take actions and observe the outcomes, without knowing the state explicitly. Example: A robot navigating in a room without any knowledge of its initial or current state.
• Contingency problem: In this type of problem, the outcomes of actions may not be completely predictable. The agent has to plan for different possible outcomes and be prepared for changes. Example: Route planning for a car considering possible traffic conditions.

How do we specify actions for a search task?

• Actions for a search task are typically defined by stating the conditions under which an action can be applied and the resulting state after applying the action.
• For example, if you have a search problem of navigating a maze, an action could be moving from one room to another, and the conditions would include having a connecting door, and the resulting state would be the room you end up in after taking the action.

What 4-tuple describes a search problem? Give an example!

• A search problem is described by four components:
  • Initial state: The starting point of the problem.
  • Actions: The possible actions or steps the agent can take.
  • Transition model: It specifies the result of applying an action to a state.
  • Goal test: It determines if a state is the desired goal state.

Difference between state and node in search problems:

• State: A state represents a particular configuration or situation in the problem domain. It defines the current condition of the problem.
• Node: A node is a data structure used in search algorithms and contains additional information, such as the parent node, the action taken to reach the current state, and the path cost.

What are the parts of the node's data structure at search problems?

• The node's data structure typically includes:
  • State: The current state in the problem domain.
  • Parent node: The node that leads to the current node.
  • Action: The action taken to transition from the parent node to the current node.
  • Path cost: The cost or distance associated with the path from the initial state to the current node.