Artificial Intelligence unit 2 part 2 easy explanation for btech students in detail #btech #ai#study

Got it — here is Unit 2

*1. Depth First Search DFS*

*What it does*: Goes deep into the tree before trying other branches. Like exploring a maze by picking one path and going till dead end, then backtracking.

*Steps in words*:
1. Put start node in a stack
2. Take the top node from stack. If it is goal, stop
3. Put all its children on top of stack
4. Repeat until stack empty

*Key points*:
Uses less memory because it stores only current path.
Not complete in infinite trees because it may go forever down one branch.
Not optimal because first solution found may not be shortest.
Used in Prolog, puzzle solving with less memory.

*Example*: In a maze, DFS will go to the farthest corner before checking side paths.

*2. Breadth First Search BFS*

*What it does*: Explores level by level. Checks all nodes at depth one, then depth two, and so on.

*Steps in words*:
1. Put start node in a queue
2. Remove first node from queue. If goal, stop
3. Add all its children to end of queue
4. Repeat

*Key points*:
Complete, so it will find solution if one exists.
Optimal when all steps cost the same, because it finds shallowest goal first.
Uses a lot of memory because it stores whole level.
Used for shortest path in unweighted graphs, like finding fewest bus stops.

*DFS vs BFS*: DFS uses stack and less memory. BFS uses queue and more memory but gives shortest path.

*3. A Star Algorithm*

*What it does*: Informed search. Uses both cost from start and guess to goal to pick best node.

*Idea*: For each node calculate two things.
First, actual cost from start to that node.
Second, estimated cost from that node to goal using heuristic.
Add both costs. Pick node with smallest total.

*Steps in words*:
1. Keep a list of nodes to explore. Start with first node
2. Always pick node with lowest total cost
3. If it is goal, return path
4. Otherwise expand it and add children to list with their costs
5. Repeat

*Key points*:
Complete and optimal if the guess never overestimates real cost.
Heuristic makes it faster than BFS.
Used in GPS, games for pathfinding.
Example heuristic for 8 puzzle: count total distance of all tiles from their goal place.

*4. AO Star Algorithm*

*What it does*: Works on AND OR graphs. Used when a problem can be broken into subproblems.

*AND node meaning*: To solve parent, you must solve all children.
*OR node meaning*: To solve parent, you can solve any one child.

*Steps in words*:
1. Start with main problem as OR node
2. Pick most promising node to expand
3. If node is solved, mark it and update parent cost
4. For AND node, cost is sum of all child costs. For OR node, cost is min of child costs
5. Keep expanding and updating until start node solved

*Key points*:
Different from A star because A star is for single path, AO star is for problem reduction.
Used in theorem proving, expert systems.
Example: To prove statement A and B, you must prove A AND prove B.

*5. Minimax Algorithm*

*What it does*: Used in two player games. One player tries to maximize score, other tries to minimize.

*Idea*:
MAX player turn: pick move with highest value.
MIN player turn: pick move with lowest value.
Look ahead till game ends or depth limit, then back up values.

*Steps in words*:
1. Generate game tree till some depth
2. At leaf nodes, give score using evaluation function. Win for MAX is high, win for MIN is low
3. If level is MAX, take maximum of child scores
4. If level is MIN, take minimum of child scores
5. Root gives best move for MAX

*Alpha Beta Pruning*: Improvement to Minimax. Cuts off branches that cannot affect final decision. Makes search twice as deep in same time.

*Key points*:
Complete and optimal against perfect opponent.
Used in Chess, Tic Tac Toe, Checkers.
Without pruning it checks too many nodes.

*Simple Comparison Without Symbols*
Algorithm Uses Good For Bad For
**DFS** Stack Low memory, solution anywhere deep May miss shallow solution
**BFS** Queue Shortest steps, guaranteed High memory
**A Star** Heuristic + cost Fast optimal path Needs good guess
**AO Star** AND OR graph Breaking problems Complex to implement
**Minimax** Game tree Two player games Slow without pruning
*For JNTUK exam*:
2 marks: Define each in 2 lines.
5 marks: Steps + 2 pros + 2 cons.
10 marks: Add example trace with diagram.

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