Foundations of Artificial Intelligence

<p>They are specific sentences that are not derived from others. (A)</p> Signup and view all the answers

<p>To add new sentences to the knowledge base. (D)</p> Signup and view all the answers

<p>It is the process of deriving conclusions from known information. (C)</p> Signup and view all the answers

<p>It is true in at least one model of the environment. (D)</p> Signup and view all the answers

<p>They focus on exploring the solution space. (A)</p> Signup and view all the answers

<p>Random events (A)</p> Signup and view all the answers

<p>The best strategy regardless of other players' choices (A)</p> Signup and view all the answers

<p>An outcome where players cannot unilaterally improve their payoffs (A)</p> Signup and view all the answers

<p>There are multiple optimal strategies with similar payoffs (C)</p> Signup and view all the answers

<p>It has a finite number of players and strategies with mixed strategies allowed (A)</p> Signup and view all the answers

<p>When players randomize their choices instead of consistently picking the same one (B)</p> Signup and view all the answers

<p>Relying on the predictability of opponents (C)</p> Signup and view all the answers

<p>To create incentives for strategic behavior and ensure fairness (B)</p> Signup and view all the answers

<p>To produce a valid plan quickly under constraints. (A)</p> Signup and view all the answers

<p>It aims for the best solution based on specific criteria, even if it's time-consuming. (A)</p> Signup and view all the answers

<p>It only deals with closed systems with no external factors. (A)</p> Signup and view all the answers

<p>Assessing if actions always produce the same outcomes. (A)</p> Signup and view all the answers

<p>Develop plans that account for multiple potential outcomes. (A)</p> Signup and view all the answers

<p>An admissible heuristic that guarantees optimality must be used. (D)</p> Signup and view all the answers

<p>A specific state of knowledge required to execute an action. (A)</p> Signup and view all the answers

<p>Lacking some information about the state that affects decision-making. (B)</p> Signup and view all the answers

<p>A set of functions, relations, and a set of objects (A)</p> Signup and view all the answers

<p>They refer directly to an object (D)</p> Signup and view all the answers

<p>Planning involves identifying tasks, while scheduling chooses the timing of those actions (D)</p> Signup and view all the answers

<p>It involves autonomous decision-making leading to action sequences for goal achievement (D)</p> Signup and view all the answers

<p>A game AI planner tailored for chess strategies (C)</p> Signup and view all the answers

<p>It contains techniques applicable across different domains (C)</p> Signup and view all the answers

<p>Functions represent properties related to objects, while constant symbols refer to objects (D)</p> Signup and view all the answers

<p>It aims to maximize the probability of obtaining desired outcomes (A)</p> Signup and view all the answers

<p>Exploitation (C)</p> Signup and view all the answers

<p>To find the best action that maximizes rewards over time (C)</p> Signup and view all the answers

<p>Agents interfere with each other's performance (A)</p> Signup and view all the answers

<p>Chess match between two players (A)</p> Signup and view all the answers

<p>The agent follows a fixed policy and evaluates its effectiveness (D)</p> Signup and view all the answers

<p>The best move by simulating moves and countermoves (D)</p> Signup and view all the answers

<p>Balancing the decision to explore uncharted states and exploiting known rewards (A)</p> Signup and view all the answers

<p>To estimate how good a state is when the tree depth is limited (B)</p> Signup and view all the answers

<p>It allows the agent to learn both the policy and value function through exploration (B)</p> Signup and view all the answers

<p>The agent always chooses the action it believes is the best based on prior learning (A)</p> Signup and view all the answers

<p>It skips nodes that won't affect the final decision (A)</p> Signup and view all the answers

<p>The efficacy of a predefined policy (D)</p> Signup and view all the answers

<p>They can lead to multiple states based on probabilities (D)</p> Signup and view all the answers

<p>The potential to miss discovering new states due to a fixed policy (B)</p> Signup and view all the answers

<p>They can be too deep to fully explore or may have infinite depth (A)</p> Signup and view all the answers

<p>States, Actions, and Rewards (C)</p> Signup and view all the answers

A search algorithm that uses additional knowledge, called heuristics, to guide its search, making it more efficient.

Expands the node with the smallest heuristic value at each step.

Uses a formula f(n) = g(n) + h(n) to estimate the total cost of a path, combining cost to reach a node with the estimated cost to the goal.

Combines the efficient space exploration of Depth-First Search with the heuristic guidance of A*. It explores the search space in gradually increasing depth.

Algorithms that explore the space of possible solutions rather than the space of states, often used for optimization problems.

Moves to the neighboring solution with the highest improvement in the heuristic value at each step.

A probabilistic approach that allows worse moves early in the search to escape local maxima and find better solutions.

Evolves a population of candidate solutions by applying selection, crossover, and mutation.

A valid sentence in FOL is true in all possible situations or interpretations of the world.

In FOL, the model represents the world or environment being modeled. It includes objects, functions, and relationships between them.

A term in FOL is a logical expression that refers to a specific object. It can be a constant, a function, or a variable.

Constant symbols in FOL directly represent specific objects in the world. For example, 'John' is a constant representing the object John.

Variables in FOL are placeholders for objects. They can represent any object in the world.

Functions in FOL represent relationships between objects. They take one or more objects as input and return another object as output.

Planning involves determining a sequence of actions to achieve a desired goal state while considering potential outcomes.

AI planning involves designing intelligent systems that can autonomously make decisions and choose actions to achieve their goals. It's about decision-making in various circumstances.

A type of AI planning where the goal is to find a solution that meets the basic requirements of the problem, but not necessarily the best or most efficient solution. This approach prioritizes speed and resource efficiency over finding the optimal solution.

A type of AI planning where the goal is to find the best possible solution based on specific criteria, such as minimizing steps or maximizing efficiency. This approach may take longer to compute but ensures the solution is the most optimal.

The representation of the current world state in AI planning. It includes a set of true facts about the world and follows the closed-world assumption, where everything not explicitly stated as true is considered false.

A common type of AI planning where the goal is to find a valid plan by searching through a graph of partial plans. Each node in the graph represents a possible plan, and the planner explores the graph to find a valid path from the initial state to the goal state.

A common type of AI planning where the goal is to find a valid state by searching through a space of possible world states. The planner explores the state space to find a path from the initial state to a state that satisfies the goal condition.

A type of planning in AI that addresses the issue of uncertainty in the environment or action outcomes. It involves creating plans that account for different possible outcomes and incorporating conditional statements to react to real-time observations.

A situation where the planner does not have complete knowledge of the world state. This can be caused by limitations in the information available or by the inherent complexity of the environment.

A situation where the outcomes of actions are not deterministic and can vary depending on external factors. This introduces uncertainty and makes planning more challenging.

A mathematical framework for making decisions in situations where outcomes are uncertain, especially when aiming to maximize rewards over time.

The current state of the system. It represents where the agent is in the environment at a given moment.

The actions an agent can take within a given state.

The probability of transitioning to a new state after taking an action.

A numerical value assigned to a state that represents its desirability. The agent aims to maximize rewards.

A type of reinforcement learning where the agent follows a fixed policy and learns to evaluate its performance. It learns how good the existing policy is, without changing it.

A type of reinforcement learning where the agent learns both the policy and the value function. It explores the environment to find the best actions to maximize rewards.

A technique used in active reinforcement learning where the agent always chooses the action that it believes will lead to the highest immediate reward, based on its current knowledge.

A strategy that is the best choice for a player, no matter what others do.

A situation where no player can improve their payoff by changing their strategy alone.

An outcome where no player can be better off without making someone else worse off.

A strategy assignment for each player, where each player cannot do better by switching strategy unilaterally.

When every player would suffer a loss by changing their strategy, assuming the other players' strategies remain unchanged.

When a player has an alternative strategy that gives the same payoff, but might have a better outcome in some scenarios.

A player chooses based on probability distribution over pure strategies, rather than always selecting the same strategy.

Designing the rules of a game to ensure fairness and incentivize players to behave in a desired way.

An agent that explores the environment to learn about potential rewards and improve its strategy.

An agent that focuses on using its knowledge to maximize rewards based on its current understanding of the environment.

When multiple intelligent agents interact in a shared environment, each with its own goals and actions.

Agents with conflicting objectives, where one agent's gain equals another's loss.

A strategy for finding the best move in a game by considering all possible future moves and countermoves.

A technique that estimates the value of a game state, used when exploring the entire game tree is impossible.

A method that optimizes the Minimax Algorithm by pruning branches of the game tree that are guaranteed not to affect the final decision.

A game where outcomes are uncertain due to random events, making the game tree more complex.