Nama : Qolbinisa
Nim : 2681494393
Matkul :Artificial Intelligence 26/27 – BD107
Module Question 2
1. Uninformed Search vs. Informed Search
Question:
Explain the primary difference between an uninformed and an informed search algorithm.
Status:
Completed
Description:
The primary difference between uninformed and informed search algorithms is the information they use to find a solution. Uninformed search algorithms, such as Breadth-First Search (BFS) and Depth-First Search (DFS), do not have additional information about which direction is closer to the goal. They explore the search space based only on the problem definition and available paths. In contrast, informed search algorithms use additional information, called a heuristic, to estimate which path is more likely to lead to the goal. Therefore, informed search can often find a solution more efficiently because it focuses on more promising paths.
Evidence:
University of California, Berkeley – Introduction to Artificial Intelligence: Informed Search
Full link:
https://inst.eecs.berkeley.edu/~cs188/textbook/search/informed.html
University of California, Berkeley – Uninformed Search
Full link:
https://inst.eecs.berkeley.edu/~cs188/textbook/search/uninformed.html
2. Depth-First Search vs. Breadth-First Search
Question:
In what scenario would Depth-First Search be more efficient than Breadth-First Search?
Status:
Completed
Description:
Depth-First Search (DFS) can be more efficient than Breadth-First Search (BFS) when the solution is likely to be located deep in the search tree and the available memory is limited. DFS explores one path as deeply as possible before backtracking, so it generally requires less memory than BFS. In comparison, BFS explores all nodes at one level before moving to the next level, which can require a large amount of memory. Therefore, DFS is useful for problems with deep search spaces where finding any solution is more important than finding the shortest solution.
Evidence:
Oregon State University – AI Search: Breadth-First Search and Depth-First Search
Full link:
https://web.engr.oregonstate.edu/~huanlian/teaching/ai100/2026winter/unit2-symbolic/ai-search.html
University of California, Berkeley – Uninformed Search
Full link:
https://inst.eecs.berkeley.edu/~cs188/textbook/search/uninformed.html
3. Heuristic Function and A* Algorithm
Question:
What is a heuristic function, and what role does it play in the A* algorithm?
Status:
Completed
Description:
A heuristic function is a method used to estimate the remaining cost or distance from a current state to the goal. It helps an AI search algorithm determine which path appears to be more promising. In the A* algorithm, the heuristic function is represented by h(n) and is combined with the actual cost of reaching the current node, represented by g(n). A* uses these values to determine which path should be explored next. This allows A* to search more efficiently while still being able to find an optimal path when an appropriate admissible heuristic is used.
Evidence:
University of British Columbia – Artificial Intelligence: Foundations of Computational Agents, A* Search
Full link:
https://www.cs.ubc.ca/~poole/aibook/3e/html/ArtInt3e.Ch3.S6.html
University of California, Berkeley – Informed Search and Heuristics
Full link:
https://inst.eecs.berkeley.edu/~cs188/textbook/search/informed.html
4. Real-World Application of Search Algorithms
Question:
Describe a real-world problem (e.g., GPS navigation) that can be solved using a search algorithm.
Status:
Completed
Description:
One real-world problem that can be solved using a search algorithm is GPS navigation. When a user enters a destination, the system needs to find a suitable route from the current location to the destination. The locations can be represented as nodes, while roads can be represented as connections between the nodes. A search algorithm such as A* can evaluate different routes by considering the actual distance or travel cost and estimating the remaining distance to the destination. This allows the system to find an efficient route instead of checking every possible road combination. GPS navigation is therefore a practical example of how search algorithms can be applied to solve route-finding problems.
Evidence:
Cornell University – A* Search
Full link:
https://www.cs.cornell.edu/courses/cs312/2007sp/recitations/rec26.html
University of British Columbia – A* Search
Full link:
https://www.cs.ubc.ca/~poole/aibook/3e/html/ArtInt3e.Ch3.S6.html