Search Algorithms for Mastermind
This work addresses the challenge of optimizing search strategies in combinatorial games like Mastermind, which is an incremental contribution to algorithm design in game theory and AI.
The paper tackles the problem of solving the classic board game Mastermind by introducing two novel search algorithms, a variant of simulated annealing and maximum expected reduction in consistency, and compares their performance to baseline methods like random search and established techniques by Knuth and Norvig, with results showing competitive or improved search efficiency in terms of query counts or solution times.
his paper presents two novel approaches to solving the classic board game mastermind, including a variant of simulated annealing (SA) and a technique we term maximum expected reduction in consistency (MERC). In addition, we compare search results for these algorithms to two baseline search methods: a random, uninformed search and the method of minimizing maximum query partition sets as originally developed by both Donald Knuth and Peter Norvig.