Emulating Human Play in a Leading Mobile Card Game

Monte Carlo Tree Search (MCTS) has become a popular solution for game AI, capable of creating strong game playing opponents. However, the emergent playstyle of agents using MCTS is not neces- sarily human-like, believable or enjoyable. AI Factory Spades, currently the top rated Spades game in the Google Play store, uses a variant of MCTS to control AI allies and opponents. In collaboration with the developers, we showed in a previous study that the playstyle of human players significantly differed from that of the AI players [1]. This article presents a method for player modelling using gameplay data and neural networks that does not require domain knowledge, and a method of biasing MCTS with such a player model to create Spades playing agents that emulate human play whilst maintaining strong, competitive performance. The methods of player modelling and biasing MCTS presented in this study are applied to the commercial codebase of AI Factory Spades, and are transferable to MCTS implementations for discrete-action games where relevant gameplay data is available

Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone

We present the design of a competitive artificial intelligence for Scopone, a popular Italian card game. We compare rule-based players using the most established strategies (one for beginners and two for advanced players) against players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS) with different reward functions and simulation strategies. MCTS requires complete information about the game state and thus implements a cheating player while ISMCTS can deal with incomplete information and thus implements a fair player. Our results show that, as expected, the cheating MCTS outperforms all the other strategies; ISMCTS is stronger than all the rule-based players implementing well-known and most advanced strategies and it also turns out to be a challenging opponent for human players.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game

Monte Carlo Tree Search for games with Hidden Information and Uncertainty

Monte Carlo Tree Search (MCTS) is an AI technique that has been successfully applied to many deterministic games of perfect information, leading to large advances in a number of domains, such as Go and General Game Playing. Imperfect information games are less well studied in the field of AI despite being popular and of significant commercial interest, for example in the case of computer and mobile adaptations of turn based board and card games. This is largely because hidden information and uncertainty leads to a large increase in complexity compared to perfect information games. In this thesis MCTS is extended to games with hidden information and uncertainty through the introduction of the Information Set MCTS (ISMCTS) family of algorithms. It is demonstrated that ISMCTS can handle hidden information and uncertainty in a variety of complex board and card games. This is achieved whilst preserving the general applicability of MCTS and using computational budgets appropriate for use in a commercial game. The ISMCTS algorithm is shown to outperform the existing approach of Perfect Information Monte Carlo (PIMC) search. Additionally it is shown that ISMCTS can be used to solve two known issues with PIMC search, namely strategy fusion and non-locality. ISMCTS has been integrated into a commercial game, Spades by AI Factory, with over 2.5 million downloads. The Information Capture And ReUSe (ICARUS) framework is also introduced in this thesis. The ICARUS framework generalises MCTS enhancements in terms of information capture (from MCTS simulations) and reuse (to improve MCTS tree and simulation policies). The ICARUS framework is used to express existing enhancements, to provide a tool to design new ones, and to rigorously define how MCTS enhancements can be combined. The ICARUS framework is tested across a wide variety of games

Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games

ABSTRACT Online search in games has been a core interest of artificial intelligence. Search in imperfect information games (e.g., Poker, Bridge, Skat) is particularly challenging due to the complexities introduced by hidden information. In this paper, we present Online Outcome Sampling, an online search variant of Monte Carlo Counterfactual Regret Minimization, which preserves its convergence to Nash equilibrium. We show that OOS can overcome the problem of non-locality encountered by previous search algorithms and perform well against its worst-case opponents. We show that exploitability of the strategies played by OOS decreases as the amount of search time increases, and that preexisting Information Set Monte Carlo tree search (ISMCTS) can get more exploitable over time. In head-to-head play, OOS outperforms ISMCTS in games where non-locality plays a significant role, given a sufficient computation time per move

A GRAPH-BASED APPROACH FOR ADAPTIVE SERIOUS GAMES

Traditional education systems are based on the one-size-fits-all approach, which lacks personalization, engagement, and flexibility necessary to meet the diverse needs and learning styles of students. This encouraged researchers to focus on exploring automated, personalized instructional systems to enhance students’ learning experiences. Motivated by this remark, this thesis proposes a personalized instructional system using a graph method to enhance a player’s learning process by preventing frustration and avoiding a monotonous experience. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated with our system would both detect player errors and provide personalized assistance to direct a player in the direction of a correct solution. To verify system performance, this research presents comparison testing on a group of students engaging in the game both with and without AI. Students who played the AI-assisted game showed an average 20% decrease in time needed and an average 58% decrease in actions taken to complete the game

Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction

This paper investigates the use of game theoretic representations to represent and learn how to play interactive games such as Connect Four. We combine aspects of learning by demonstration, active learning, and game theory allowing a robot to leverage its developing representation of the game to conduct question/answer sessions with a person, thus filling in gaps in its knowledge. The paper demonstrates a method for teaching a robot the win conditions of the game Connect Four and its variants using a single demonstration and a few trial examples with a question and answer session led by the robot. Our results show that the robot can learn arbitrary win conditions for the game with little prior knowledge of the win conditions and then play the game with a human utilizing the learned win conditions. Our experiments also show that some questions are more important for learning the game's win conditions. We believe that this method could be broadly applied to a variety of interactive learning scenarios.Comment: The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-62056-1_4

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone