BibSLEIGH — Noor

Collaborated with:

Julian Togelius Mohamed Abou-Zleikha Georgios N. Yannakakis Mohammad Shaker ∅ Miguel Nicolau Michael O'Neill 0001 Sergey Karakovskiy S.Asteriadis K.Karpouzis Mhd Hasan Sarhan Ola Al Naameh Britton Horn Steve Dahlskog G.Smith Likith Poovanna Vinay Sudha Ethiraj Stefan J. Johansson R.G.Reynolds Leonard Kinnaird-Heether Tom Schumann Marcus Gallagher

Talks about:

content (6) generat (5) player (5) mario (5) game (5) model (4) level (4) use (4) toward (3) learn (3)

Person: Noor Shaker

DBLP: Shaker:Noor

Contributed to:

2010

2011

2012

2013

2014

2015

2016

Wrote 18 papers:

AIIDE-2010-ShakerYT #automation #framework #game studies #generative #personalisation #platform #towards: Towards Automatic Personalized Content Generation for Platform Games (NS, GNY, JT).
CIG-2010-KarakovskiySTY: The Mario AI Championship (SK, NS, JT, GNY), pp. 1–3.
CIG-2011-ShakerYT #analysis #game studies #modelling #quality: Feature analysis for modeling game content quality (NS, GNY, JT), pp. 126–133.
AIIDE-2012-ShakerYTNO #evolution #personalisation #using: Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution (NS, GNY, JT, MN, MO0).
CIG-2012-ShakerNYTO #evolution #using: Evolving levels for Super Mario Bros using grammatical evolution (NS, MN, GNY, JT, MO0), pp. 304–311.
VS-Games-2012-AsteriadisKSY #behaviour #clustering #detection #towards #using #visual notation: Towards Detecting Clusters of Players using Visual and Gameplay Behavioral Cues (SA, KK, NS, GNY), pp. 140–147.
AIIDE-2013-ShakerST #approach #evolution: Evolving Playable Content for Cut the Rope through a Simulation-Based Approach (NS, MS, JT).
AIIDE-2013-ShakerST13a #authoring #design #named #optimisation: Ropossum: An Authoring Tool for Designing, Optimizing and Solving Cut the Rope Levels (NS, MS, JT).
CIG-2013-ShakerSNST #analysis #automation #game studies #generative: Automatic generation and analysis of physics-based puzzle games (MS, MHS, OAN, NS, JT), pp. 1–8.
CIG-2013-ShakerTYPEJRKSG #evaluation: The turing test track of the 2012 Mario AI Championship: Entries and evaluation (NS, JT, GNY, LP, VSE, SJJ, RGR, LKH, TS, MG), pp. 1–8.
AIIDE-2014-Abou-ZleikhaS #authoring #design #matrix #named #using: PaTux: An Authoring Tool for Level Design through Pattern Customisation Using Non-Negative Matrix Factorization (MAZ, NS).
AIIDE-2014-ShakerA #approach #combinator #game studies #generative: Alone We Can Do So Little, Together We Can Do So Much: A Combinatorial Approach for Generating Game Content (NS, MAZ).
FDG-2014-HornDSST #comparative #evaluation #framework #generative: A comparative evaluation of procedural level generators in the Mario AI framework (BH, SD, NS, GS, JT).
FDG-2014-TogeliusSY #modelling: Active player modelling (JT, NS, GNY).
AIIDE-2015-ShakerSA #experience #modelling #towards: Towards Generic Models of Player Experience (NS, MS, MAZ), pp. 191–197.
FDG-2015-ShakerAS #learning #modelling: Active Learning for Player Modeling (NS, MAZ, MS).
CIG-2016-Shaker #framework #generative #learning #motivation: Intrinsically motivated reinforcement learning: A promising framework for procedural content generation (NS), pp. 1–8.
CIG-2016-ShakerA #experience #learning #predict: Transfer learning for cross-game prediction of player experience (NS, MAZ), pp. 1–8.