PokerKit is an open-source Python library designed to overcome the restrictions of existing poker game simulation and hand evaluation tools, which typically support only a handful of poker variants and lack flexibility in game state control. In contrast, PokerKit significantly expands this scope by supporting an extensive array of poker variants and it provides a flexible architecture for users to define their custom games. This paper details the design and implementation of PokerKit, including its intuitive programmatic API, multi-variant game support, and a unified hand evaluation suite across different hand types. The flexibility of PokerKit allows for applications in diverse areas, such as poker AI development, tool creation, and online poker casino implementation. PokerKit's reliability has been established through static type checking, extensive doctests, and unit tests, achieving 99% code coverage. The introduction of PokerKit represents a significant contribution to the field of computer poker, fostering future research and advanced AI development for a wide variety of poker games. The source code is available at https://github.com/uoftcprg/pokerkit

Hand gestures constitute another important mode of human communication that could be adopted for human-computer interactions. Recent progress in camera systems, image analysis, and machine learning have made optical-based gesture recognition a more attractive option in most contexts than approaches relying on wearable sensors or data gloves, as used by Anderton in Minority Report. However, current methods are hindered by a variety of limitations, including high computational complexity, low speed, poor accuracy, or a low number of recognizable gestures. To tackle these issues, a team led by Zhiyi Yu of Sun Yat-sen University, China, recently developed a new hand gesture recognition algorithm that strikes a good balance between complexity, accuracy, and applicability. As detailed in their paper, which was published in the Journal of Electronic Imaging, the team adopted innovative strategies to overcome key challenges and realize an algorithm that can be easily applied in consumer-level devices.

Poker Squares is a single-player card game played on a 5 x 5 grid, in which a player attempts to create as many high-scoring Poker hands as possible. As a stochastic single-player game with an extremely large state space, this game offers an interesting area of application for Monte-Carlo Tree Search (MCTS). This paper describes enhancements made to the MCTS algorithm to improve computer play, including pruning in the selection stage and a greedy simulation algorithm. These enhancements make extensive use of domain knowledge in the form of a state evaluation heuristic. Experimental results demonstrate both the general efficacy of these enhancements and their ideal parameter settings.

Poker is ideal for testing automated reasoning under uncertainty. It introduces uncertainty both by physical randomization and by incomplete information about opponents hands.Another source OF uncertainty IS the limited information available TO construct psychological models OF opponents, their tendencies TO bluff, play conservatively, reveal weakness, etc. AND the relation BETWEEN their hand strengths AND betting behaviour. ALL OF these uncertainties must be assessed accurately AND combined effectively FOR ANY reasonable LEVEL OF skill IN the game TO be achieved, since good decision making IS highly sensitive TO those tasks.We describe our Bayesian Poker Program(BPP), which uses a Bayesian network TO model the programs poker hand, the opponents hand AND the opponents playing behaviour conditioned upon the hand, and betting curves which govern play given a probability of winning. The history of play with opponents is used to improve BPPs understanding OF their behaviour.We compare BPP experimentally WITH : a simple RULE - based system; a program which depends exclusively ON hand probabilities(i.e., without opponent modeling); AND WITH human players.BPP has shown itself TO be an effective player against ALL these opponents, barring the better humans.We also sketch out SOME likely ways OF improving play.