![]() Performance Testing and Constraintsįor engineers, designers and other creative problem-solvers, a formal definition of the constraints within which they must work is essential to channel energies and expand creativity. It's a great experience to walk in the footsteps of those pioneers of computer science. Today Machine Learning and Artificial Intelligence are increasingly powerful and ubiquitous. This is a symbolic moment in the advance of computing technology. This became the first defeat of a world chess champion by a computer under tournament conditions. Kasparov played against IBM's Deep Blue chess engine in 1997. Building a data structure with efficient insertion, deletion and search is another critical piece of a competitive chess engine. A chess position and its “children” form a tree of connected possibilities. Links Listsĭata structures are central to computer science. Hash tables are a useful way to save time and resources by saving the resulting evaluation for a position. ![]() It’s obviously inefficient to evaluate a given position multiple times, however, as the game progresses the engine will of course have large parts of the current and previous search trees overlapping. Writing a chess engine is a great opportunity to improve this skill. Understanding when and how to use hash tables is critical in software engineering. In the best case, Alpha-beta can reduce the search size to the square root of the original search. However, using an Alpha-beta pruning will reduce the number of nodes that need to be evaluated by ignoring branches that are worse than previously analyzed branches. Just as an example, the naive Minimax algorithm is effective in finding the optimal move given a search tree with each node evaluated. There are many clever search algorithms, pruning strategies, hashing methods, and move generation approaches to reduce the search space and increase efficiency. This is not possible to compute within the time bounds of a chess match. Searching every position to a depth of 24 requires evaluating 6.2e+10³⁵ (or 620 decillion) positions. AlgorithmsĪ given chess position has an average of 31 possible moves. This poses a fun, worthwhile challenge for developers of any experience level. How efficiently the chess engine can determine legal moves and evaluate positional advantage is critical to its strength. If Stockfish is only analyzing three moves per position that’s 3²⁴ position, i.e. ![]() Of course, the search tree is pruned so that not every move is analyzed, but to give you some idea of the scale here, let’s check the numbers. For example, Stockfish, a leading open-source chess engine, searches to a depth of 24 on average. Computing ConstraintsĪ chess engine's strength depends to some degree on its search depth. The following aspects of chess programming will improve your software engineering skills. The chess engine I built is named Glee, short for Golang Chess Engine. I want to share the challenges that make chess programming such a great way to level up as a software engineer. It also greatly improved my skill in applying important software engineering concepts. I ended up writing a chess engine and it turned out to be more challenging than I had anticipated. So, when I started to learn Golang I wanted to find a challenging project requiring a variety of software engineering concepts. I’ve always learned better working on projects rather than reading theory.
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