AI Basics: 63 flashcards
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1. What is adversarial search?
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A search technique in AI where two or more agents compete, each trying to achieve their own goal, often in zero-sum games.
2. In the context of adversarial search, what does 'zero-sum' mean?
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A situation where one agent's gain is exactly the other agent's loss, so the total utility remains constant.
3. What is the Minimax algorithm?
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A recursive algorithm for choosing the optimal move in a two-player zero-sum game, assuming both players play optimally. It minimizes the maximum possible loss.
4. In Minimax, what does the MAX player try to do?
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Maximize the utility score.
5. In Minimax, what does the MIN player try to do?
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Minimize the utility score.
6. What is alpha-beta pruning?
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An optimization technique for Minimax that reduces the number of nodes evaluated by pruning branches that cannot influence the final decision.
7. In alpha-beta pruning, what does alpha represent?
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The best value found so far for MAX along the path.
8. In alpha-beta pruning, what does beta represent?
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The best value found so far for MIN along the path.
9. What is a k-ply look ahead in game playing?
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Searching ahead k moves (plies) from the current state to evaluate the desirability of a move.
10. What is a ply in game tree search?
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A single move by one player.
11. What is the basic definition of probability?
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A measure of the likelihood that an event will occur, quantified as a number between 0 and 1.
12. What is a random variable?
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A variable whose possible values are numerical outcomes of a random phenomenon.
13. What is a Bayesian network?
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A probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).
14. In a Bayesian network, what does each node represent?
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A random variable.
15. In a Bayesian network, what does a directed edge from X to Y signify?
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That X has a direct influence on Y (X is a parent of Y).
16. What is a logical agent?
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An agent that uses logical reasoning to deduce actions based on its knowledge base and percepts.
17. What is a knowledge base (KB) in logical agents?
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A set of sentences expressed in a formal language that the agent uses to reason and make decisions.
18. What is inference in the context of logical agents?
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The process of deriving new sentences from the knowledge base using logical rules.
19. What is model checking in propositional logic?
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A method to determine if a knowledge base entails a sentence by checking all possible truth assignments (models).
20. What does the PEAS framework stand for?
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Performance measure, Environment, Actuators, Sensors.
21. In PEAS, what does 'P' stand for?
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Performance measure: how well the agent is doing.
22. In PEAS, what does 'E' stand for?
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Environment: the surroundings in which the agent operates.
23. In PEAS, what does 'A' stand for?
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Actuators: mechanisms through which the agent acts on the environment.
24. In PEAS, what does 'S' stand for?
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Sensors: mechanisms through which the agent perceives the environment.
25. What is the minimax value of a terminal state in a game tree?
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The utility value of that state for the MAX player.
26. In alpha-beta pruning, when can a branch be pruned?
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When the value of the current node is worse than the best value found so far for the opponent (i.e., alpha >= beta).
27. What is the time complexity of Minimax for a game tree with branching factor b and depth m?
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$O(b^m)$
28. What is the main advantage of alpha-beta pruning over plain Minimax?
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It can reduce the number of nodes evaluated, allowing deeper search in the same time, but does not affect the final decision.
29. What is a conditional probability?
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The probability of an event given that another event has occurred, denoted $P(A|B)$.
30. What is Bayes' theorem?
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A formula that relates conditional probabilities: $P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}$.
31. In a Bayesian network, what is the joint probability distribution defined as?
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The product of conditional probabilities of each node given its parents: $P(X_1, \ldots, X_n) = \prod_{i=1}^n P(X_i | \text{Parents}(X_i))$.
32. What is a tautology in propositional logic?
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A sentence that is true in all possible models (truth assignments).
33. What is a contradiction in propositional logic?
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A sentence that is false in all possible models.
34. What is the entailment relation in logic?
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A sentence A entails B (written A ⊨ B) if in every model where A is true, B is also true.
35. What is the Modus Ponens inference rule?
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From $P$ and $P \Rightarrow Q$, infer $Q$.
36. What is the Modus Tollens inference rule?
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From $\neg Q$ and $P \Rightarrow Q$, infer $\neg P$.
37. What is the And-Elimination inference rule?
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From $P \land Q$, infer $P$ (or infer $Q$).
38. What is the And-Introduction inference rule?
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From $P$ and $Q$, infer $P \land Q$.
39. What is the Or-Introduction inference rule?
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From $P$, infer $P \lor Q$ for any Q.
40. What is the resolution inference rule?
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From $P \lor Q$ and $\neg P \lor R$, infer $Q \lor R$.
41. What is a Horn clause?
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A clause with at most one positive literal, often used in logic programming.
42. What is forward chaining?
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A data-driven inference method that starts from known facts and applies inference rules to derive new facts until the goal is reached.
43. What is backward chaining?
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A goal-driven inference method that starts from the goal and works backward to find supporting facts.
44. In the PEAS framework, what is the performance measure for a self-driving car?
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Safety, time, comfort, legality, etc.
45. In the PEAS framework, what are the actuators for a self-driving car?
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Steering, accelerator, brake, signals, etc.
46. In the PEAS framework, what are the sensors for a self-driving car?
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Cameras, lidar, speedometer, GPS, etc.
47. What is the environment type for tic-tac-toe in terms of observability?
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Fully observable.
48. What is the environment type for tic-tac-toe in terms of determinism?
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Deterministic.
49. What is the environment type for tic-tac-toe in terms of number of agents?
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Multi-agent (two players).
50. What is the environment type for tic-tac-toe in terms of dynamism?
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Static (state doesn't change while agent deliberates).
51. What is the environment type for tic-tac-toe in terms of discreteness?
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Discrete.
52. What is the environment type for tic-tac-toe in terms of episodicity?
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Sequential (current move affects future moves).
53. What is the environment type for tic-tac-toe in terms of known/unknown?
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Known (rules and outcomes are known).
54. What is the minimax value of a non-terminal state?
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For MAX: maximum of minimax values of children; for MIN: minimum of minimax values of children.
55. What is the purpose of the alpha parameter in alpha-beta pruning?
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To keep track of the best value that MAX can guarantee so far.
56. What is the purpose of the beta parameter in alpha-beta pruning?
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To keep track of the best value that MIN can guarantee so far.
57. What is a probabilistic model?
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A mathematical model that uses probability theory to represent uncertainty and make predictions.
58. What is the law of total probability?
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The total probability of an event A is the sum of its conditional probabilities given a partition of the sample space: $P(A) = \sum_i P(A|B_i)P(B_i)$.
59. What is a conditional probability table (CPT) in a Bayesian network?
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A table that specifies the probability of each value of a node given each combination of parent values.
60. What is the Markov property in Bayesian networks?
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Each variable is conditionally independent of its non-descendants given its parents.
61. What is a logical model in propositional logic?
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An assignment of truth values (true/false) to each propositional symbol.
62. What is satisfiability in propositional logic?
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A sentence is satisfiable if it is true in at least one model.
63. What is validity in propositional logic?
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A sentence is valid if it is true in all models (tautology).
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