Change search
ReferencesLink to record
Permanent link

Direct link
Performance Comparison of AI Algorithms: Anytime Algorithms
Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering.
2008 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesisAlternative title
Utförande Jämförelse av AI Algoritmer : Anytime Algoritmer (Swedish)
Abstract [en]

Commercial computer gaming is a large growing industry, that already has its major contributions in the entertainment industry of the world. One of the most important among different types of computer games are Real Time Strategy (RTS) based games. RTS games are considered being the major research subject for Artificial Intelligence (AI). But still the performance of AI in these games is poor by human standards because of some broad sets of problems. Some of these problems have been solved with the advent of an open real time research platform, named as ORTS. However there still exist some fundamental AI problems that require more research to be better solved for the RTS games. There also exist some AI algorithms that can help us solve these AI problems. Anytime- Algorithms (AA) are algorithms those can optimize their memory and time resources and are considered best for the RTS games. We believe that by making AI algorithms anytime we can optimize their behavior to better solve the AI problems for the RTS games. Although many anytime algorithms are available to solve various kinds of AI problems, but according to our research no such study is been done to compare the performances of different anytime algorithms for each AI problem in RTS games. This study will take care of that by building our own research platform specifically design for comparing performances of selected anytime algorithms for an AI problem

Place, publisher, year, edition, pages
2008. , 75 p.
Keyword [en]
Artificial Intelligence (AI), Real Time Strategy (RTS) Games, AI Algorithms, AI Problems, Anytime Algorithms, A – Star, RBFS, Potential Fields, Path Finding, ORTS platform, PFPC platform
National Category
Computer Science Human Computer Interaction
URN: urn:nbn:se:bth-5846Local ID: diva2:833252
Available from: 2015-04-22 Created: 2008-12-08 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(712 kB)70 downloads
File information
File name FULLTEXT01.pdfFile size 712 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Department of Systems and Software Engineering
Computer ScienceHuman Computer Interaction

Search outside of DiVA

GoogleGoogle Scholar
Total: 70 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 43 hits
ReferencesLink to record
Permanent link

Direct link