Unit 2 Artificial Intelligence

What are the main types of problem solving agents in AI?

• Data-driven agents

• Predictive agents

• Goal-based agents, Utility-based agents, Model-based agents, Reactive agents, Learning agents.

• Static agents

Define informed search strategy and provide an example.

• Informed search strategies do not use heuristics.

• An example of an informed search strategy is the A* search algorithm, which uses a heuristic to estimate the cost from the current node to the goal.

• The A* search algorithm is an example of an uninformed search strategy.

• A breadth-first search is an example of an informed search strategy.

What is an uninformed search strategy?

• A strategy that guarantees finding the optimal solution.

• A search technique that relies on prior knowledge of the search space.

• A search method that uses heuristics to guide the search process.

• An uninformed search strategy is a search method that explores the search space without additional information or heuristics.

Explain the role of heuristic functions in AI.

• Heuristic functions are used to store data in databases.

• Heuristic functions help estimate costs and guide search algorithms in AI.

• Heuristic functions are primarily for hardware optimization.

• Heuristic functions replace the need for algorithms in AI.

Describe the A* search algorithm and its components.

• A* search algorithm is a sorting algorithm that organizes data in ascending order.

• A* search algorithm is a pathfinding algorithm that uses a heuristic to find the shortest path from a start node to a goal node.

• A* search algorithm is a brute-force method that explores all possible paths.

• A* search algorithm only works in unweighted graphs without heuristics.

Compare and contrast breadth-first search and depth-first search.

• BFS explores all nodes in a single path before moving to the next, while DFS explores all nodes at once.

• BFS uses a stack and explores depth first, while DFS uses a queue and explores level by level.

• BFS and DFS both use a queue to explore nodes in the same manner.

• BFS uses a queue and explores level by level, while DFS uses a stack and explores depth first.

What is depth-limited search and when is it used?

• Depth-limited search is used when the search space is too large or infinite, and a maximum depth is needed to avoid excessive resource consumption.

• Depth-limited search is primarily used for searching in sorted arrays.

• Depth-limited search is a method for sorting data efficiently.

• Depth-limited search is used to find the shortest path in a graph.

Explain the concept of hill climbing in problem solving.

• Hill climbing requires a complete knowledge of the problem space before starting the search.

• Hill climbing is a global search algorithm that explores all possible solutions at once.

• Hill climbing is a local search algorithm that continuously moves towards the direction of increasing value (or decreasing cost) to find the optimal solution.

• Hill climbing is a method that randomly selects solutions without any direction.

What are the advantages of using informed search strategies?

• Informed search strategies always guarantee the optimal solution.

• The advantages of using informed search strategies include improved efficiency, reduced search space, and faster solution finding.

• Informed search strategies are slower than random search methods.

• They require more memory than uninformed strategies.

List some common heuristic functions used in AI.

• Depth-first search strategy

• Manhattan distance, Euclidean distance, A* heuristic, Greedy best-first search heuristic.

• Binary search tree

• Random search algorithm