About: Predicting Opponent’s Production in Real-Time Strategy Games with Answer Set Programming     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
rdfs:seeAlso
Description
  • The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent’s actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player’s partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1-3 minutes seems to be surprisingly high, making the method useful in practice. Rootmean- square error grows only slowly with increasing prediction intervals – almost in a linear fashion.
  • The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent’s actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player’s partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1-3 minutes seems to be surprisingly high, making the method useful in practice. Rootmean- square error grows only slowly with increasing prediction intervals – almost in a linear fashion. (en)
Title
  • Predicting Opponent’s Production in Real-Time Strategy Games with Answer Set Programming
  • Predicting Opponent’s Production in Real-Time Strategy Games with Answer Set Programming (en)
skos:prefLabel
  • Predicting Opponent’s Production in Real-Time Strategy Games with Answer Set Programming
  • Predicting Opponent’s Production in Real-Time Strategy Games with Answer Set Programming (en)
skos:notation
  • RIV/68407700:21230/14:00227811!RIV15-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • I
http://linked.open...iv/cisloPeriodika
  • 99
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 38736
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/14:00227811
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Real-Time Strategy; Answer Set Programming; Prediction; StarCraft; WarCraft; Opponent Modelling; RTS (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • FR - Francouzská republika
http://linked.open...ontrolniKodProRIV
  • [541F16C54E43]
http://linked.open...i/riv/nazevZdroje
  • IEEE Transactions on Computational Intelligence and AI in Games
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • PP
http://linked.open...iv/tvurceVysledku
  • Čertický, Michal
  • Stanescu, M.
issn
  • 1943-068X
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/TCIAIG.2014.2365414
http://localhost/t...ganizacniJednotka
  • 21230
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software