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.

This paper describes a fast hand strength estimation mod-el for the game of Gin Rummy. The algorithm is computationally inexpensive, and it incorporates not only cards in the player’s hand but also cards known to be in the opponent’s hand, cards in the discard pile, and the current game stage. This algorithm is used in conjunction with counterfactual regret (CFR) minimization to develop a gin rummy bot. CFR strategies were developed for the knocking strategies. The hand strength estimation algorithm was used to select a discard that balances the goals of maximizing the utility of the player’s hand and minimizing the likelihood that a card will be useful to the opponent. A study of the parameterization of this estimation algorithm demonstrates the soundness of approach as well as good performance under a wide range of parameter values.

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.

We describe a new, quadruped robot platform, Aracna, which requires non-intuitive motor commands in order to locomote and thus provides an interesting challenge for gait learning algorithms, such as those frequently developed in the Evolu-tionary Computation and Artificial Life communities. Aracna is an open-source hardware project composed of off-the-shelf and 3D-printed parts, enabling other research teams to mod-ify its design according to their scientific needs. Aracna was designed to overcome the shortcomings of a previous quadruped robot platform, whose legs were so heavy that the motors could not reliably execute the commands sent to them. We avoid this problem by locating all motors in the body core instead of on the legs and through a design which enables the servos to have a greater mechanical advantage. Specifically, each of the four legs has two joints controlled by separate four-bar linkage mechanisms that drive the pitch of the hip joint and knee joint. This novel design causes unconventional kinematics, creating an opportunity for gait-learning algorithms, which excel in counter-intuitive design spaces where human engineers tend to underperform. Be-cause it is low-cost, flexible, kinematically interesting, and and improvement over a previous design, Aracna provides a useful new hardware platform for testing algorithms that au-tomatically generate robotic behaviors.