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I understand your question to be: If some moves are compulsory, and my agent has no choice about which move to make next, do I need to perform a search, or can I just return the compulsory move? The answer depends on what your goal is. If your goal is to make an interactive agent that will play the game against you, then you are correct: there's no need ...

To build on Neil's answer a bit, you're right that the better your evaluation function gets, the less work your optimization function will need to perform. If your evaluation function gets good enough, you won't need to search at all. This is not just an academic idea though! It's actually fairly widely used, and has been key to solving several games. The ...

A perfect evaluation function would mean that you only had to do a local search - i.e. maximise over next set of decisions - in order for an agent to behave optimally in an environment. As such if you could somehow create that function, it would make a search with alpha-beta pruning redundant. In practice, evaluation functions for complex environments are ...

Your logic is flawed because you negated "stand-pat" (i.e. do nothing) and alpha-beta. Let's take a look at the pseudocode (https://www.chessprogramming.org/Quiescence_Search#Pseudo_Code): int Quiesce( int alpha, int beta ) { int stand_pat = Evaluate(); if( stand_pat >= beta ) return beta; if( alpha < stand_pat ) alpha = ...

Are these algorithms an extension of the alpha-beta algorithm, or Are they completely new algorithms, in that they have got nothing to do the alpha-beta algorithm? Most of them are extensions of the Alpha-Beta pruning algorithm. For example, Iterative Deepening is almost the same as Alpha-Beta pruning, but automatically keeps repeating the algorithm ...