Knowledge-Based Agents and Knowledge Representation

In local search algorithms, what is the criterion for a solution in the traveling salesman problem?

What does a local search algorithm start with?

What does hill climbing search continually move towards?

What is the potential problem with hill climbing search?

What type of landscape does hill climbing's success depend on?

What type of games are most commonly studied within AI, such as chess and Go?

What is the synonym for 'fully observable' in the context of games?

What is the optimal play strategy for deterministic, perfect-information games according to the text?

What characterizes a 'zero-sum' game?

What type of game allows for infinitely repeating positions according to the text?

What do knowledge-based agents use to decide what actions to take?

What type of logic is developed as a general class of representations to support knowledge-based agents?

What is the main purpose of a route-finding agent's knowledge in AI?

In AI, what type of landscape does hill climbing's success depend on?

What does a local search algorithm start with in the traveling salesman problem according to the text?

What is the role of a knowledge-based agent's knowledge base (KB)?

In the wumpus world, what does 'Grabbing' refer to?

Which approach to system building involves encoding desired behaviors directly as program code?

What type of game is characterized as being fully observable, deterministic, episodic, static, discrete, and single-agent?

What percentage of environments in the wumpus world are considered utterly unfair?

The criterion for a solution in the traveling salesman problem is a tour that visits every city exactly once and returns to the starting city.
Local search algorithms start with a randomly generated tour.

Hill climbing continually moves towards states with higher values, seeking to find the best possible solution.
One potential problem with hill climbing search is that it can get stuck in local optima, which are states that are better than their neighbors but not the global optimum.
The success of hill climbing search depends on the smoothness of the landscape.

The most commonly studied games within AI are two-player, deterministic, perfect-information games, such as chess and Go.
The synonym for 'fully observable' in the context of games is 'perfect information'.
The optimal play strategy for deterministic, perfect-information games is to play perfectly, which means making the best possible move at every turn.
A 'zero-sum' game is characterized by the fact that one player's gain is the other player's loss.
A repeated game is a game that allows for infinitely repeating positions.

Knowledge-based agents use knowledge to decide what actions to take.
Logic is developed as a general class of representations to support knowledge-based agents.

The main purpose of a route-finding agent's knowledge is to find the shortest path from one location to another.

Grabbing in the wumpus world refers to the action of picking up an object such as gold.
25% of environments in the wumpus world are considered utterly unfair.

The approach to system building that involves encoding desired behaviors directly as program code is called 'hand-coding'.
A single-player, deterministic, perfect-information game is characterized as being fully observable, episodic, static, discrete, and single-agent.