Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Comparison of A*, Euclidean and Manhattan distance using Influence map in MS. Pac-Man
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Context An influence map and potential fields are used for finding path in domain of Robotics and Gaming in AI. Various distance measures can be used to find influence maps and potential fields. However, these distance measures have not been compared yet.

ObjectivesIn this paper, we have proposed a new algorithm suitable to find an optimal point in parameters space from random parameter spaces. Finally, comparisons are made among three popular distance measures to find the most efficient.

Methodology For our RQ1 and RQ2, we have implemented a mix of qualitative and quantitative approach and for RQ3, we have used quantitative approach. Results A* distance measure in influence maps is more efficient compared to Euclidean and Manhattan in potential fields.

Conclusions Our proposed algorithm is suitable to find optimal point and explores huge parameter space. A* distance in influence maps is highly efficient compared to Euclidean and Manhattan distance in potentials fields. Euclidean and Manhattan distance performed relatively similar whereas A* distance performed better than them in terms of score in Ms. Pac-Man (See Appendix A).

Place, publisher, year, edition, pages
2016.
Keyword [en]
Ms. Pac-Man, algorithm, influence maps, potential fields, distance measure, A*, Euclidean, Manhattan, optimal parameter space
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-11800OAI: oai:DiVA.org:bth-11800DiVA: diva2:918778
Subject / course
DV2566 Master's Thesis (120 credits) in Computer Science
Educational program
DVACS Master of Science Programme in Computer Science
Supervisors
Examiners
Available from: 2016-05-13 Created: 2016-04-11 Last updated: 2016-05-13Bibliographically approved

Open Access in DiVA

fulltext(1827 kB)147 downloads
File information
File name FULLTEXT02.pdfFile size 1827 kBChecksum SHA-512
461ca4e8b4bd781dc1b08d31ca690cad0e37060fabb7d837e60c203b69914c2523272428f7436b4c8ae69b92fd4108aa4e82325b2bf64275273baa2ed58c3677
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Ranjitkar, Hari SagarKarki, Sudip
By organisation
Department of Computer Science and Engineering
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 147 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: 78 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf