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Ai Search

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction and philosophy. The Turing Test. The Winograd Schema Challenge. Placing search in the landscape of AI.

Incomplete

02W02

Search spaces. Examples. State space search. Depth First, Breadth First, Iterative Deepening. Analysis.

Incomplete

03W03

Heuristic search. Heuristic functions. Solution space search. Escaping local optima. Stochastic local search.

Incomplete

04W04

Population based methods. Genetic Algorithms, emergent systems, Ant Colony Optimization.

Incomplete

05W05

Finding optimal paths. Algorithm A. Admissibility of A.

Incomplete

06W06

The monotone condition. Space saving versions of A*. Sequence alignment.

Incomplete

07W07

Game playing. Board games. Algorithms Minimax, Alpha-Beta, and SSS*.

Incomplete

08W08

Automated domain independent planning. Goal Stack Planning, Partial Order Planning.

Incomplete

09W09

Problem decomposition with goal trees. Algorithm AO*.

Incomplete

010W10

Pattern directed inference systems. Forward chaining inference engine. The Rete algorithm.

Incomplete

011W11

Constraint processing. Algorithm Backtracking. Arc consistency. Combining search and reasoning. Waltz algorithm. Model based diagnosis.

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

⚙️ Configuration vs. Planning Problems

Open this first for the core logic and traps.

Dive in

1 min

✅ Admissibility & Consistency in A*

Open this first for the core logic and traps.

Dive in

1 min

🌐 Implicit vs. Explicit State Spaces

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // Playlist Connected

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSCS3003

BSc Degree

4 Credits

AI: Search Methods for Problem Solving

We look at how an intelligent agent solves new problems. Starting with blind search we quickly move on to heuristic search, and look at several var...

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Introduction and philosophy. The Turing Test. The Winograd Schema Challenge. Placing search in the landscape of AI.

Module 2

Search spaces. Examples. State space search. Depth First, Breadth First, Iterative Deepening. Analysis.

Module 3

Heuristic search. Heuristic functions. Solution space search. Escaping local optima. Stochastic local search.

Module 4

Population based methods. Genetic Algorithms, emergent systems, Ant Colony Optimization.

Module 5

Finding optimal paths. Algorithm A. Admissibility of A.

Module 6

The monotone condition. Space saving versions of A*. Sequence alignment.

Module 7

Game playing. Board games. Algorithms Minimax, Alpha-Beta, and SSS*.

Module 8

Automated domain independent planning. Goal Stack Planning, Partial Order Planning.

Module 9

Problem decomposition with goal trees. Algorithm AO*.

Module 10

Pattern directed inference systems. Forward chaining inference engine. The Rete algorithm.

Module 11

Syllabus

Topic

Introduction and philosophy. The Turing Test. The Winograd Schema Challenge. Placing search in the landscape of AI.

Search spaces. Examples. State space search. Depth First, Breadth First, Iterative Deepening. Analysis.

Heuristic search. Heuristic functions. Solution space search. Escaping local optima. Stochastic local search.

Population based methods. Genetic Algorithms, emergent systems, Ant Colony Optimization.

Finding optimal paths. Algorithm A*. Admissibility of A*.

The monotone condition. Space saving versions of A*. Sequence alignment.

Game playing. Board games. Algorithms Minimax, Alpha-Beta, and SSS*.

Automated domain independent planning. Goal Stack Planning, Partial Order Planning.

Problem decomposition with goal trees. Algorithm AO*.

Pattern directed inference systems. Forward chaining inference engine. The Rete algorithm.

Constraint processing. Algorithm Backtracking. Arc consistency. Combining search and reasoning. Waltz algorithm. Model based diagnosis.

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

AI: Search Methods for Problem Solving

Execution Protocol

Topic

Introduction and philosophy. The Turing Test. The Winograd Schema Challenge. Placing search in the landscape of AI.

Search spaces. Examples. State space search. Depth First, Breadth First, Iterative Deepening. Analysis.

Heuristic search. Heuristic functions. Solution space search. Escaping local optima. Stochastic local search.

Population based methods. Genetic Algorithms, emergent systems, Ant Colony Optimization.

Finding optimal paths. Algorithm A*. Admissibility of A*.

The monotone condition. Space saving versions of A*. Sequence alignment.

Game playing. Board games. Algorithms Minimax, Alpha-Beta, and SSS*.

Automated domain independent planning. Goal Stack Planning, Partial Order Planning.

Problem decomposition with goal trees. Algorithm AO*.

Pattern directed inference systems. Forward chaining inference engine. The Rete algorithm.

Constraint processing. Algorithm Backtracking. Arc consistency. Combining search and reasoning. Waltz algorithm. Model based diagnosis.

Video Archive

Intelligence Hub

Finding optimal paths. Algorithm A. Admissibility of A.

Finding optimal paths. Algorithm A. Admissibility of A.