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When I took an AI course in 2009 it was largely about game playing algorithms that use graph traversal (possibly guided by heuristics). Stuff like this: https:

When I took an AI course in 2009 it was largely about game playing algorithms that use graph traversal (possibly guided by heuristics). Stuff like this: https://en.m.wikipedia.org/wiki/A*_search_algorithm https://en.m.wikipedia.org/wiki/A*_search_algorithm Such algorithms are still a viable approach in many situations (and I'm sure A* is used in production somewhere today).

moffkalast 4y ago
In practice these "traditional AI" algorithms are far more common because they're well defined, low cost to implement and run (no gpu instances required lol), and give useful results faster. Proper neural nets are still more buzzwords than workhorses these days, especially when you stray away from image processing where they really shine. Random forest can predict tabular data extremely well without overfitting, A* is the mathematical best approach to finding the shortest path through a graph, alfa-beta pruning with heuristics consistently beats any known neural net in chess, quicksort will be better than a neural sort because you don't have to first offload data onto a damn gpu, etc. Even Tesla doesn't give the car's accelerator and steering reigns to the detection neural net outputs (that would be actually insane), instead it lets them extract data from the world into its vector space and then uses deterministic motion planning to determine where the car should drive based on A* GPS routing. That way the system won't do anything undefined and stupid, like neural nets are usually prone to.
YeGoblynQueenne 4y ago
Search-based algorithms continue to dominate traditional board games. Stockfish, AlphaGo and friends are hybrid systems combining a game-tree search algorithm with a neural net. The neural net is trained (by self-play) to learn an evaluation function for the game-tree search algorithm (more precisely, the neural nets learn a classifier for board positions as leading to a win, loss or draw). The game-tree search algorithms are alpha-beta minimax in Stockfish and Monte Carlo Tree Search in AlphaGo and family. Far as I know anyway. DeepMind have downplayed the use of MCTS in their Alpha-x family, to the point of obfuscating the fact that is part of their system at all and have sowed much confusion about this, but their systems ain't going nowhere without good, old-fashion game-tree search. Stockfish only recently adopted neural nets, btw.

moffkalast 4y ago

In practice these "traditional AI" algorithms are far more common because they're well defined, low cost to implement and run (no gpu instances required lol), and give useful results faster. Proper neural nets are still more buzzwords than workhorses these days, especially when you stray away from image processing where they really shine. Random forest can predict tabular data extremely well without overfitting, A* is the mathematical best approach to finding the shortest path through a graph, alfa-beta pruning with heuristics consistently beats any known neural net in chess, quicksort will be better than a neural sort because you don't have to first offload data onto a damn gpu, etc. Even Tesla doesn't give the car's accelerator and steering reigns to the detection neural net outputs (that would be actually insane), instead it lets them extract data from the world into its vector space and then uses deterministic motion planning to determine where the car should drive based on A* GPS routing. That way the system won't do anything undefined and stupid, like neural nets are usually prone to.

YeGoblynQueenne 4y ago

Search-based algorithms continue to dominate traditional board games. Stockfish, AlphaGo and friends are hybrid systems combining a game-tree search algorithm with a neural net. The neural net is trained (by self-play) to learn an evaluation function for the game-tree search algorithm (more precisely, the neural nets learn a classifier for board positions as leading to a win, loss or draw). The game-tree search algorithms are alpha-beta minimax in Stockfish and Monte Carlo Tree Search in AlphaGo and family. Far as I know anyway. DeepMind have downplayed the use of MCTS in their Alpha-x family, to the point of obfuscating the fact that is part of their system at all and have sowed much confusion about this, but their systems ain't going nowhere without good, old-fashion game-tree search. Stockfish only recently adopted neural nets, btw.