| EPSRC Reference: |
EP/T008962/1 |
| Title: |
Abstract Forward Models for Modern Games |
| Principal Investigator: |
Perez Liebana, Dr D |
| Other Investigators: |
|
| Researcher Co-Investigators: |
|
| Project Partners: |
|
| Department: |
Sch of Electronic Eng & Computer Science |
| Organisation: |
Queen Mary University of London |
| Scheme: |
New Investigator Award |
| Starts: |
01 April 2020 |
Ends: |
30 September 2022 |
Value (£): |
305,207
|
| EPSRC Research Topic Classifications: |
|
| EPSRC Industrial Sector Classifications: |
| Creative Industries |
Information Technologies |
|
| Related Grants: |
|
| Panel History: |
| Panel Date | Panel Name | Outcome |
|
03 Sep 2019
|
EPSRC ICT Prioritisation Panel September 2019
|
Announced
|
|
|
Summary on Grant Application Form |
The games industry is one of the fastest-growing industries in the world, with yearly revenues expected to increase from US$ 138bn in 2018 to US$ 180bn in 2021. The UK games industry is a worldwide leader that contributes significantly to wealth creation and export, with a clear growing tendency: 62% of the 2261 companies in the UK were founded in the last 8 years. Employing 12,000 people with sales valued in £4.3bn for 2017, this industry is the second largest market in Europe and the fifth in the world.
Games have also been excellent benchmarks for the advancement of AI. One of the most clear and recent examples of this is the progress on search methods in the game of Go. Go is a thousand years old board game of simple rules but complex strategy, where humans had dominated computer AIs since the beginning of the field. Monte Carlo Tree Search (MCTS), an AI technique that explores the different branches of actions that both players can take, became in 2016 the standard algorithm for creating Go AI players, giving birth to substantial research on variations and applications of this algorithm. Since then, MCTS has been used in thousands of other works in and outside games. This progress reached another milestone when Google Deepmind's Alpha Go mastered this game with a combination of MCTS and Deep Learning (DL).
MCTS uses a forward model (FM), which is a representation of the game state that allows to roll the state forward after applying any action in the game. This "simulator" is also used by other Statistical Forward Planning (SFP) methods that are also showing similar promise to MCTS in some domains, such as Rolling Horizon Evolutionary Algorithms (RHEA). It is however striking that despite the popularity and progress on SFP methods, they have barely reached the games industry. The most known uses of MCTS for Opponent AI in the games industry are in the Total War series by Creative Assembly, AI Factory on card games and Lionhead's tactical planning for Fable Legends. Given that the games industry is one of the fastest growing industries in the world and UK one may wonder why one of the top algorithms on AI in Games barely reaches far less than 0.01% of this industry.
The aim of this project is to incorporate an FM library into a modern games engine in order to facilitate research on the use of SFP techniques in large, complex, video-games. On the one hand, the project will address the technical and design problems of integrating a customisable FM that determines which elements of the real game state form part of the FM and how abstractions can be made. On the other hand, the project will aim to understand how SFP methods perform under these conditions in complex and large commercial-like games, investigating how these can be improved. The resultant framework will allow to test these methods in a wide range of games, with a special emphasis on proposing a Game AI competition for industry and researchers. Dissemination of the project's research outcomes will be guaranteed via open source libraries, frameworks, documentation and scientific papers.
This project builds naturally on the PI's recent work on GVGAI (for which he is main developer, organiser and coordinator of the competition, tracks and team - www.gvgai.net), and it proposes a step change on General Game AI research and its relevance to the games industry, adapting it to modern games. This project addresses directly the applicability of well-established methods such as MCTS/RHEA to large and complicated games and also the industry needs for fast, reliable and state of the art AI techniques. Our strong group of game industry partners (Microsoft Research, AI Factory, Bossa Studios, Creative Assembly and Gwaredd Mountain) will help steer the project into the interests of the game research and industry communities. Applications beyond games will also be explored with the help of our non-game industry partner (the Defence Science and Technology Laboratory).
|
|
Key Findings |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Potential use in non-academic contexts |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Impacts |
|
| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
|
| Date Materialised |
|
|
|
|
Sectors submitted by the Researcher |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
| Project URL: |
|
| Further Information: |
|
| Organisation Website: |
|