I tend to disagree that this represents an explosion of approaches. For example, AlphaGo and DeepBlue are more similar than they may at first seem. AlphaGo used

I tend to disagree that this represents an explosion of approaches. For example, AlphaGo and DeepBlue are more similar than they may at first seem. AlphaGo used MCTS (a search algorithm), and value and policy networks as heuristics to evaluate board positions and search order. AlphaGo scored a couple of big wins by showing that you don't need exhaustive search (in fact, AlphaGo evaluates orders of magnitude fewer nodes in the game tree than previous state-of-the art MCTS Go agents, and Go is notorious for such a high branching factor that minimax, et. al., are straight out), and that the heuristic can be learned through reinforcement learning.