Dial locks are commonly used to secure a person’s items. Commercially available dial locks often use four or five wheels of letters, allowing a user to select a word as a combination. In order to evaluate the security of these locks, we create a game, with an instance created by the lock designer, and played by a lock owner and a thief. In the game, the lock owner chooses a word as a combination, and the thief creates a brute force strategy to try all possible combinations that yield words until the combination is found. To accomplish the task, the thief will solve a version of the Probabilistic Travelling Salesman Problem (PTSP) by creating an a priori tour through all the words a lock can create. The goal for the game designer, then, is to create a lock configuration that maximizes the expected length of the best possible PTSP tour. This paper describes a Genetic Algorithm (GA) approach to design a near-optimal game, i.e. a lock configuration that makes it as difficult for the thief to crack. An analysis of the output of the GA shows that the locks that the system creates are significantly more secure than both commercial locks, in the context of this game.

Various Gin Rummy strategy guides provide heuristics for human players to improve their gameplay. Often these heuristics are either conflicting or contain ambiguity that limits their applicability, especially for discard and end-of-game decisions. This paper describes an approach to analyzing the machine learning capabilities of a Gin Rummy agent to help resolve these conflicts and ambiguities. There are three main decision points in the game: when to draw from the discard pile, which card to discard from the player's hand, and when to knock. The agent us-es a learning approach to estimate the expected utility for discards. An analysis of these utility values provides in-sight into resolving ambiguities in tips for discard decisions in human play. The agent’s end-of-game, or knocking, strategy was derived using Monte Carlo Counterfactual regret minimization (MCCFR). This approach was applied to estimate Nash equilibrium knocking strategies under different rules of the game. The analysis suggests that conflicts in the end-of-game playing tips are due in part to different rules used in common Gin Rummy variants.