This article surveys three techniques for enhancing heuristic game-tree search pioneered in the author’s Othello program LOGISTELLO, which dominated the computer Othello scene for several years and won against the human World-champion 6–0 in 1997. First, a generalized linear evaluation model (GLEM) is described that combines conjunctions of Boolean features linearly. This approach allows an aut...

This paper studies the effect of varying the depth of look-ahead for heuristic search in temporal difference (TD) learning and game playing. The acquisition position evaluation functions for the game of Othello is studied. The paper provides important insights into the strengths and weaknesses of using different search depths during learning when 2-greedy exploration is applied. The main findin...

The article presents Shakespeare's Iago as Hell's master therapist. Illustrated with excerpts from the play, the reader is shown how with consummate psychological skills, Iago bends his victims to his will. Step by step he awakens Othello's jealousy, and makes use of the hero's basic insecurity on account of his blackness. Once Othello has determined to take revenge, Iago makes sure to prevent ...