AI · Theory

Uninformed Search

Theory

21/215

Uninformed Search

Uninformed search has no heuristic. BFS uses a queue — shortest path if every step costs 1. DFS uses a stack — can dive deep, may miss a nearby goal. UCS (uniform cost) expands cheapest path first — like Dijkstra. IDS repeats DFS with a rising depth limit.

Trap — DFS is not “always faster”. On a huge tree it can get lost. BFS eats memory. Say those two costs out loud.

Uninformed Search — output — three lines. No heuristic.

Diagram

start
    ├── BFS  queue
    ├── DFS  stack
    └── A*   g+h
    │
    ▼
   goal

Exam tip

BFS vs DFS: data structure + when shortest.

Example

# Uninformed search
print("BFS: queue  → shortest hops (cost=1)")
print("DFS: stack  → may miss a nearby goal")
print("UCS: cheapest g(n) first")

Uninformed Search — output: three lines. No heuristic.

Short notes

DefBFS queue. DFS stack. UCS cheapest path.
RuleNo h(n).

AI · Theory

Uninformed Search

← All stacks

Theory

21/215

Uninformed Search

Trap — DFS is not “always faster”. On a huge tree it can get lost. BFS eats memory. Say those two costs out loud.

Uninformed Search — output — three lines. No heuristic.

Diagram

start
    ├── BFS  queue
    ├── DFS  stack
    └── A*   g+h
    │
    ▼
   goal

Exam tip

BFS vs DFS: data structure + when shortest.

Example

# Uninformed search
print("BFS: queue  → shortest hops (cost=1)")
print("DFS: stack  → may miss a nearby goal")
print("UCS: cheapest g(n) first")

Uninformed Search — output: three lines. No heuristic.

Short notes

DefBFS queue. DFS stack. UCS cheapest path.
RuleNo h(n).

Uninformed Search

Example

Short notes

Questions

Uninformed Search

Example

Short notes

Questions