Cruz et al., 2021 - Google Patents
MarioMix: Creating Aligned Playstyles for Bots with Interactive Reinforcement LearningCruz et al., 2021
View PDF- Document ID
- 1532147282152621194
- Author
- Cruz C
- Igarashi T
- Publication year
- Publication venue
- arXiv preprint arXiv:2105.12944
External Links
Snippet
In this paper, we propose a generic framework that enables game developers without knowledge of machine learning to create bot behaviors with playstyles that align with their preferences. Our framework is based on interactive reinforcement learning (RL), and we …
- 230000002452 interceptive 0 title abstract description 25
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/004—Artificial life, i.e. computers simulating life
- G06N3/006—Artificial life, i.e. computers simulating life based on simulated virtual individual or collective life forms, e.g. single "avatar", social simulations, virtual worlds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0202—Market predictions or demand forecasting
- G06Q30/0203—Market surveys or market polls
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/60—Methods for processing data by generating or executing the game program
- A63F2300/63—Methods for processing data by generating or executing the game program for controlling the execution of the game in time
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/50—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers
- A63F2300/55—Details of game data or player data management
- A63F2300/5546—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yannakakis et al. | Artificial intelligence and games | |
| Arzate Cruz et al. | A survey on interactive reinforcement learning: Design principles and open challenges | |
| Cook et al. | The angelina videogame design system—part i | |
| Yannakakis et al. | A panorama of artificial and computational intelligence in games | |
| Yannakakis | Game AI revisited | |
| Stahlke et al. | Artificial players in the design process: Developing an automated testing tool for game level and world design | |
| Takala et al. | Empowering students to create better virtual reality applications: A longitudinal study of a VR capstone course. | |
| Guss et al. | The minerl 2019 competition on sample efficient reinforcement learning using human priors | |
| Roohi et al. | Predicting game difficulty and engagement using AI players | |
| Machado et al. | Ai-assisted game debugging with cicero | |
| Lee et al. | Learning a Super Mario controller from examples of human play | |
| Pfau et al. | Player-driven game analytics: The case of guild wars 2 | |
| Sestini et al. | Towards informed design and validation assistance in computer games using imitation learning | |
| Zhang et al. | Crew: Facilitating human-ai teaming research | |
| Arzate Cruz et al. | Mariomix: Creating aligned playstyles for bots with interactive reinforcement learning | |
| Butt et al. | The Development of Intelligent Agents: A Case-Based Reasoning Approach to Achieve Human-Like Peculiarities via Playback of Human Traces | |
| Camilleri et al. | Platformer level design for player believability | |
| Kruse et al. | An interactive multi-agent system for game design | |
| Prada et al. | Agent-based testing of extended reality systems | |
| Cruz et al. | MarioMix: Creating Aligned Playstyles for Bots with Interactive Reinforcement Learning | |
| Kruse et al. | Evaluation of a Multi-agent “Human-in-the-loop” Game Design System | |
| Norling | Modelling human behaviour with BDI agents | |
| Walther-Franks et al. | Robots, pancakes, and computer games: designing serious games for robot imitation learning | |
| Bauckhage et al. | Learning human behavior from analyzing activities in virtual environments | |
| Walton et al. | Does mapping elites illuminate search spaces? A large-scale user study of MAP--Elites applied to human--AI collaborative design |