Collaborated with:Edward Jack Powley Daniel Whitehouse Hendrik Baier Nick Sephton Nicholas H. Slaven S.Devlin Roderick J. S. Baker Munir Hussain Naveed M. Alamgir Hossain ∅ Colin D. Ward Christian Gmeinwieser Jeff Rollason Daniel Kudenko Thomas W. G. Randall Ping Jiang 0001 Hanting Xie M.J.Nelson Paolo Burelli K.Karpouzis S.M.Lucas Anastasija Anspoka V.J.Hodge Myat Aung Valerio Bonometti Anders Drachen A. V. Kokkinakis C. Yoder Alex R. Wade Nikolaos Goumagias Alberto Nucciarelli Ignazio Cabras Kiran Jude Fernandes 0001 F.Li
Talks about:search (11) carlo (10) mont (10) tree (9) game (8) action (4) oppon (4) evolutionari (3) predict (3) heurist (3)
Person: Peter I. Cowling
DBLP: Cowling:Peter_I=
Contributed to:
20052006200720082009
201020112012201320132014201520162016201720182018Wrote 24 papers:
- CIG-2005-Cowling #evaluation #game studies
- Board Evaluation For The Virus Game (PIC).
- CIG-2006-CowlingNH #game studies
- A Coevolutionary Model for The Virus Game (PIC, MHN, MAH), pp. 45–51.
- CIG-2007-BakerC #modelling
- Bayesian Opponent Modeling in a Simple Poker Environment (RJSB, PIC), pp. 125–131.
- CIG-2007-NaveedCH #game studies #hybrid #learning
- Hybrid Evolutionary Learning Approaches for The Virus Game (MHN, PIC, MAH), pp. 196–202.
- CIG-2008-BakerCRJ #evolution #modelling #question
- Can opponent models aid poker player evolution? (RJSB, PIC, TWGR, PJ0), pp. 23–30.
- CIG-2009-WardC #monte carlo
- Monte Carlo search applied to card selection in Magic: The Gathering (CDW, PIC), pp. 9–16.
- AIIDE-2010-CowlingG
- AI for Herding Sheep (PIC, CG).
- CIG-2011-WhitehousePC #game studies #monte carlo #set
- Determinization and information set Monte Carlo Tree Search for the card game Dou Di Zhu (DW, EJP, PIC), pp. 87–94.
- CIG-2012-NelsonBKLC
- Tutorials (MJN, PB, KK, SML, PIC).
- CIG-2012-PowleyWC #heuristic #monte carlo #physics #problem
- Monte Carlo Tree Search with macro-actions and heuristic route planning for the Physical Travelling Salesman Problem (EJP, DW, PIC), pp. 234–241.
- AIIDE-2013-WhitehouseCPR #game studies #knowledge-based #mobile #monte carlo
- Integrating Monte Carlo Tree Search with Knowledge-Based Methods to Create Engaging Play in a Commercial Mobile Game (DW, PIC, EJP, JR).
- CIG-2013-PowleyWC #monte carlo #policy #simulation
- Bandits all the way down: UCB1 as a simulation policy in Monte Carlo Tree Search (EJP, DW, PIC), pp. 1–8.
- CIG-2013-PowleyWC13a #heuristic #monte carlo #multi #physics #problem
- Monte Carlo Tree Search with macro-actions and heuristic route planning for the Multiobjective Physical Travelling Salesman Problem (EJP, DW, PIC), pp. 1–8.
- CIG-2014-DevlinCKGNCFL #game studies
- Game intelligence (SD, PIC, DK, NG, AN, IC, KJF0, FL), pp. 1–8.
- CIG-2014-SephtonCPS #game studies #heuristic #monte carlo
- Heuristic move pruning in Monte Carlo Tree Search for the strategic card game Lords of War (NS, PIC, EJP, NHS), pp. 1–7.
- CIG-2015-CowlingWP #monte carlo
- Emergent bluffing and inference with Monte Carlo Tree Search (PIC, DW, EJP), pp. 114–121.
- CIG-2015-SephtonCS #case study
- An experimental study of action selection mechanisms to create an entertaining opponent (NS, PIC, NHS), pp. 122–129.
- CIG-2015-XieDKC #data transformation #predict #representation
- Predicting player disengagement and first purchase with event-frequency based data representation (HX, SD, DK, PIC), pp. 230–237.
- AIIDE-2016-DevlinASCR #game studies #monte carlo
- Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play (SD, AA, NS, PIC, JR), pp. 16–22.
- CIG-2016-SephtonCDHS #android #mining #predict #using
- Using association rule mining to predict opponent deck content in android: Netrunner (NS, PIC, SD, VJH, NHS), pp. 1–8.
- AIIDE-2017-PowleyCW #bound #memory management #monte carlo
- Memory Bounded Monte Carlo Tree Search (EJP, PIC, DW), pp. 94–100.
- AIIDE-2018-BaierC #flexibility
- Evolutionary MCTS with Flexible Search Horizon (HB, PIC), pp. 2–8.
- CIG-2018-AungBDCKYW #dataset #learning #predict #scalability
- Predicting Skill Learning in a Large, Longitudinal MOBA Dataset (MA, VB, AD, PIC, AVK, CY, ARW), pp. 1–7.
- CIG-2018-BaierC #game studies #multi
- Evolutionary MCTS for Multi-Action Adversarial Games (HB, PIC), pp. 1–8.