1 resultado para learning machine
em Abertay Research Collections - Abertay University’s repository
Filtro por publicador
- Aberdeen University (8)
- Abertay Research Collections - Abertay University’s repository (1)
- Aberystwyth University Repository - Reino Unido (7)
- Academic Archive On-line (Karlstad University; Sweden) (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (3)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (4)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (14)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (3)
- Aston University Research Archive (15)
- Biblioteca de Teses e Dissertações da USP (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (6)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (4)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (7)
- Boston University Digital Common (6)
- Brock University, Canada (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (6)
- CaltechTHESIS (3)
- Cambridge University Engineering Department Publications Database (38)
- CentAUR: Central Archive University of Reading - UK (4)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (5)
- CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal (1)
- Cochin University of Science & Technology (CUSAT), India (4)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- CUNY Academic Works (2)
- Dalarna University College Electronic Archive (7)
- Deakin Research Online - Australia (91)
- Department of Computer Science E-Repository - King's College London, Strand, London (3)
- Digital Archives@Colby (1)
- Digital Commons at Florida International University (2)
- DigitalCommons@The Texas Medical Center (1)
- DigitalCommons@University of Nebraska - Lincoln (4)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- DRUM (Digital Repository at the University of Maryland) (1)
- Duke University (6)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (1)
- Glasgow Theses Service (1)
- Greenwich Academic Literature Archive - UK (1)
- Helda - Digital Repository of University of Helsinki (2)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (1)
- Indian Institute of Science - Bangalore - Índia (24)
- Instituto Gulbenkian de Ciência (1)
- Instituto Politécnico de Castelo Branco - Portugal (1)
- Instituto Politécnico do Porto, Portugal (3)
- Massachusetts Institute of Technology (7)
- National Center for Biotechnology Information - NCBI (1)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (32)
- Queensland University of Technology - ePrints Archive (522)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório Institucional da Universidade de Aveiro - Portugal (1)
- Repositorio Institucional de la Universidad de La Laguna (1)
- Repositorio Institucional de la Universidad de Málaga (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (18)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- Universidad de Alicante (3)
- Universidad Politécnica de Madrid (14)
- Universidade de Lisboa - Repositório Aberto (2)
- Universidade Federal do Rio Grande do Norte (UFRN) (1)
- Universitat de Girona, Spain (2)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (1)
- Université de Montréal, Canada (8)
- Université Laval Mémoires et thèses électroniques (1)
- University of Queensland eSpace - Australia (14)
- University of Southampton, United Kingdom (1)
- University of Washington (12)
Resumo:
Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.