TDS+: Improving Temperature Discovery Search

Abstract

Temperature Discovery Search (TDS) is a forward search method for computing or approximating the temperature of a combinatorial game. Temperature and mean are important concepts in combinatorial game theory, which can be used to develop efficient algorithms for playing well in a sum of subgames. A new algorithm TDS+ with five enhancements of TDS is developed, which greatly speeds up both exact and approximate versions of TDS. Means and temperatures can be computed faster, and fixed-time approximations which are important for practical play can be computed with higher accuracy than before.

Overview of Amazon chess’ game playing algorithms

“…Zhang proposed an improved Temperature Discovery Search (TDS), which is a forward search method used to calculate or approximate the temperature of a combined game. It can calculate the average value and temperature faster and achieve higher calculation accuracy [55]. Many researchers regard MiniMax algorithm and MCTS search as two methods of competition and incompatibility [56].…”