| Procedural Content Generation via Machine Learning (PCGML) |
12 |
| Learning to Navigate Through Complex Dynamic Environment With Modular Deep Reinforcement Learning |
9 |
| An Enhanced Particle Swarm Optimization Method Integrated With Evolutionary Game Theory |
9 |
| Automated Playtesting With Procedural Personas Through MCTS With Evolved Heuristics |
6 |
| Compressing Chinese Dark Chess Endgame Databases by Deep Learning |
5 |
| Playtime Measurement With Survival Analysis |
5 |
| General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms |
4 |
| Multiagent Inverse Reinforcement Learning for Two-Person Zero-Sum Games |
3 |
| Learning Constructive Primitives for Real-Time Dynamic Difficulty Adjustment in Super Mario Bros |
3 |
| Game Data Mining Competition on Churn Prediction and Survival Analysis Using Commercial Game Log Data |
3 |
| Orchestrating Game Generation |
3 |
| Learning of Behavior Trees for Autonomous Agents |
3 |
| Analysis of Agent Expertise in Ms. Pac-Man Using Value-of-Information-Based Policies |
3 |
| Trained Behavior Trees: Programming by Demonstration to Support AI Game Designers |
2 |
| StarCraft AI Competitions, Bots, and Tournament Manager Software |
2 |
| Hierarchical Reinforcement Learning With Monte Carlo Tree Search in Computer Fighting Game |
2 |
| Pac-Man Conquers Academia: Two Decades of Research Using a Classic Arcade Game |
2 |
| Multimodal Student Engagement Recognition in Prosocial Games |
2 |
| Mastering 2048 With Delayed Temporal Coherence Learning, Multistage Weight Promotion, Redundant Encoding, and Carousel Shaping |
2 |
| Move Prediction Using Deep Convolutional Neural Networks in Hex |
2 |
| Game Tree Search Based on Nondeterministic Action Scripts in Real-Time Strategy Games |
2 |
| Integrating Skills and Simulation to Solve Complex Navigation Tasks in Infinite Mario |
2 |
| Residual Networks for Computer Go |
2 |
| Symbolic Reasoning for Hearthstone |
2 |
| HearthBot: An Autonomous Agent Based on Fuzzy ART Adaptive Neural Networks for the Digital Collectible Card Game HearthStone |
2 |
| Discovering Agent Behaviors Through Code Reuse: Examples From Half-Field Offense and Ms. Pac-Man |
1 |
| Multilabeled Value Networks for Computer Go |
1 |
| Exploration in Continuous Control Tasks via Continually Parameterized Skills |
1 |
| Learning to Play Othello With Deep Neural Networks |
1 |
| Combat Models for RTS Games |
1 |
| Implementing Adaptive Game Difficulty Balancing in Serious Games |
1 |
| The ASC-Inclusion Perceptual Serious Gaming Platform for Autistic Children |
1 |
| Serious Games for Training Social Skills in Job Interviews |
1 |
| How the Business Model of Customizable Card Games Influences Player Engagement |
1 |
| Emulating Human Play in a Leading Mobile Card Game |
1 |
| Recommender System for Items in Dota 2 |
1 |
| Machine Learning Approaches to Competing in Fantasy Leagues for the NFL |
1 |
| Multiobjective Evolutionary Map Design for Cube 2: Sauerbraten |
1 |
| ViZDoom Competitions: Playing Doom From Pixels |
1 |
| Dota 2 Bot Competition |
1 |
| Playing Multiaction Adversarial Games: Online Evolutionary Planning Versus Tree Search |
1 |
| Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone |
1 |
| Spread-It: A Strategic Game of Competitive Diffusion Through Social Networks |
1 |
| Machine Learning Techniques for Analyzing Training Behavior in Serious Gaming |
1 |
| Building a Planner: A Survey of Planning Systems Used in Commercial Video Games |
1 |
| Modeling Users' Collaborative Behavior With a Serious Game |
0 |
| Tanager: A Generator of Feasible and Engaging Levels for Angry Birds |
0 |
| Procedural Generation of Game Maps With Human-in-the-Loop Algorithms |
0 |
| Pacman Capture the Flag in AI Courses |
0 |
| Deep Learning Competition Framework on Othello for Education |
0 |
