Source metadata
- Type: Introductory algorithms textbook
- Author: Aditya Y. Bhargava
- Published: 2016, Manning
Key takeaways
- Builds algorithmic thinking through small, readable examples rather than formal proofs.
- Gives a strong beginner-friendly route into Big O reasoning, which is essential for understanding why some AI/pathfinding approaches scale and others do not.
- Covers breadth-first search and Dijkstra’s algorithm clearly enough to support later, more game-specific pathfinding material.
- Introduces greedy algorithms and dynamic programming as design patterns for solving optimisation problems with trade-offs.
- Ends with k-nearest neighbours as a gentle entry point to machine learning and classification.
Notable claims
- The book frames algorithm choice as a performance trade-off problem: the same task can be solved correctly by several algorithms, but with radically different running times.
- Graph search is presented as a reusable mental model: once a problem is represented as nodes plus connections, search algorithms become general tools rather than one-off tricks.
- The KNN chapter is presented as an accessible bridge from classic algorithms into practical machine learning.
Relevance
Directly informs:
Useful support material for:
Open questions raised
- How much of this introductory presentation should be mirrored in game-specific pages versus kept as background support?
- Would a dedicated page on graph search be useful, or do the existing AI/pathfinding pages already cover that space well enough?
Links
pathfinding-algorithms · machine-learning-games · overview-artificial-intelligence-in-games