Analyzing the robustness of general video game playing agents

This paper presents a study on the robustness and variability of performance of general video game-playing agents. Agents analyzed includes those that won the different legs of the 2014 and 2015 General Video Game AI Competitions, and two sample agents distributed with its framework. Initially, these agents are run in four games and ranked according to the rules of the competition. Then, different modifications to the reward signal of the games are proposed and noise is introduced in either the actions executed by the controller, their forward model, or both. Results show that it is possible to produce a significant change in the rankings by introducing the modifications proposed here. This is an important result because it enables the set of human-authored games to be automatically expanded by adding parameter-varied versions that add information and insight into the relative strengths of the agents under test. Results also show that some controllers perform well under almost all conditions, a testament to the robustness of the GVGAI benchmark

MCTS/EA hybrid GVGAI players and game difficulty estimation

© 2016 IEEE. In the General Video Game Playing competitions of the last years, Monte-Carlo tree search as well as Evolutionary Algorithm based controllers have been successful. However, both approaches have certain weaknesses, suggesting that certain hybrids could outperform both. We envision and experimentally compare several types of hybrids of two basic approaches, as well as some possible extensions. In order to achieve a better understanding of the games in the competition and the strength and weaknesses of different controllers, we also propose and apply a novel game difficulty estimation scheme based on several observable game characteristics

Enhancements for Real-Time Monte-Carlo Tree Search in General Video Game Playing

General Video Game Playing (GVGP) is a field of Artificial Intelligence where agents play a variety of real-time video games that are unknown in advance. This limits the use of domain-specific heuristics. Monte-Carlo Tree Search (MCTS) is a search technique for game playing that does not rely on domain-specific knowledge. This paper discusses eight enhancements for MCTS in GVGP; Progressive History, N-Gram Selection Technique, Tree Reuse, Breadth-First Tree Initialization, Loss Avoidance, Novelty-Based Pruning, Knowledge-Based Evaluations, and Deterministic Game Detection. Some of these are known from existing literature, and are either extended or introduced in the context of GVGP, and some are novel enhancements for MCTS. Most enhancements are shown to provide statistically significant increases in win percentages when applied individually. When combined, they increase the average win percentage over sixty different games from 31.0% to 48.4% in comparison to a vanilla MCTS implementation, approaching a level that is competitive with the best agents of the GVG-AI competition in 2015

AtDelfi: Automatically Designing Legible, Full Instructions For Games

This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.Comment: 10 pages, 11 figures, published at Foundations of Digital Games Conference 201

Novelty and MCTS

Novelty search has become a popular technique in different fields such as evolutionary computing, classical AI planning, and deep reinforcement learning. Searching for novelty instead of, or in addition to, directly maximizing the search objective, aims at avoiding dead ends and local minima, and overall improving exploration. We propose and test the integration of novelty into Monte Carlo Tree Search (MCTS), a state-of-the-art framework for online RL planning, by linearly combining value estim

The 2016 Two-Player GVGAI Competition

This paper showcases the setting and results of the first Two-Player General Video Game AI competition, which ran in 2016 at the IEEE World Congress on Computational Intelligence and the IEEE Conference on Computational Intelligence and Games. The challenges for the general game AI agents are expanded in this track from the single-player version, looking at direct player interaction in both competitive and cooperative environments of various types and degrees of difficulty. The focus is on the agents not only handling multiple problems, but also having to account for another intelligent entity in the game, who is expected to work towards their own goals (winning the game). This other player will possibly interact with first agent in a more engaging way than the environment or any non-playing character may do. The top competition entries are analyzed in detail and the performance of all agents is compared across the four sets of games. The results validate the competition system in assessing generality, as well as showing Monte Carlo Tree Search continuing to dominate by winning the overall Championship. However, this approach is closely followed by Rolling Horizon Evolutionary Algorithms, employed by the winner of the second leg of the contest

General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

General Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demand of different research directions. The agents are required either to play multiple unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education and competitions five years after its birth. A future plan of framework improvements is also described.Comment: 20 pages, 1 figure, accepted by IEEE To