Monte Carlo Approaches to Parameterized Poker Squares

The paper summarized a variety of Monte Carlo approaches employed in the top three performing entries to the Parameterized Poker Squares NSG Challenge competition. In all cases AI players benefited from real-time machine learning and various Monte Carlo game-tree search techniques

The Faculty Notebook, September 2016

The Faculty Notebook is published periodically by the Office of the Provost at Gettysburg College to bring to the attention of the campus community accomplishments and activities of academic interest. Faculty are encouraged to submit materials for consideration for publication to the Associate Provost for Faculty Development. Copies of this publication are available at the Office of the Provost

Metagame Autobalancing for Competitive Multiplayer Games

Automated game balancing has often focused on single-agent scenarios. In this paper we present a tool for balancing multi-player games during game design. Our approach requires a designer to construct an intuitive graphical representation of their meta-game target, representing the relative scores that high-level strategies (or decks, or character types) should experience. This permits more sophisticated balance targets to be defined beyond a simple requirement of equal win chances. We then find a parameterization of the game that meets this target using simulation-based optimization to minimize the distance to the target graph. We show the capabilities of this tool on examples inheriting from Rock-Paper-Scissors, and on a more complex asymmetric fighting game

Deep Counterfactual Regret Minimization in Continuous Action Space

Counterfactual regret minimization based algorithms are used as the state-of-the-art solutions for various problems within imperfect-information games. Deep learning has seen a multitude of uses in recent years. Recently deep learning has been combined with counterfactual regret minimization to increase the generality of the counterfactual regret minimization algorithms. This thesis proposes a new way of increasing the generality of the counterfactual regret minimization algorithms even further by increasing the role of neural networks. In addition, to combat the variance caused by the use of neural networks, a new way of sampling is introduced to reduce the variance. These proposed modifications were compared against baseline algorithms. The proposed way of reducing variance improved the performance of counterfactual regret minimization, while the method for increasing generality was found to be lacking especially when scaling the baseline model. Possible reasons for this are discussed and future research ideas are offered

Extensible graphical game generator

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.Vita.Includes bibliographical references (leaves 162-167).An ontology of games was developed, and the similarities between games were analyzed and codified into reusable software components in a system called EGGG, the Extensible Graphical Game Generator. By exploiting the similarities between games, EGGG makes it possible for someone to create a fully functional computer game with a minimum of programming effort. The thesis behind the dissertation is that there exist sufficient commonalities between games that such a software system can be constructed. In plain English, the thesis is that games are really a lot more alike than most people imagine, and that these similarities can be used to create a generic game engine: you tell it the rules of your game, and the engine renders it into an actual computer game that everyone can play.by Jon Orwant.Ph.D