Problem 1244. PACMAT - G03 Ghosts use minimum path to PACMAT; 3 Lives

Created by Richard Zapor

Solve Later

{"relationships":[{"relationshipType":"http://schemas.mathworks.com/matlab/code/2013/relationships/document","targetMode":"","relationshipId":"rId1","target":"/matlab/document.xml"},{"relationshipType":"http://schemas.mathworks.com/matlab/code/2013/relationships/output","targetMode":"","relationshipId":"rId2","target":"/matlab/output.xml"}],"parts":[{"partUri":"/matlab/document.xml","relationship":[{"relationshipType":"http://schemas.mathworks.com/matlab/code/2013/relationships/image","targetMode":"","relationshipId":"rId1","target":"/media/image1.JPEG"}],"contentType":"application/vnd.mathworks.matlab.code.document+xml","content":"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<w:document xmlns:w=\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\"><w:body><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>The Classic PACMAN game brought to Cody.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>PACMAT requires clearing the board of Yellow Dots while not bumping into the wandering ghosts in 3 lives. Adjacent Ghosts will capture PACMAT. Ghosts do not use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path to PACMAT. This is similar to the actual game.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId1\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>To aid in development of your routine, a PACMAT_Ghosts_003.m file that creates a video has been posted at</w:t></w:r><w:r><w:t> </w:t></w:r><w:hyperlink w:docLocation=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Ghosts_003.m\"><w:r><w:t>PACMAT_Ghosts_003.m</w:t></w:r></w:hyperlink><w:r><w:t>. (Right click, 'save link as'). Using patches thus enable/figure, disable/video for best results.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:hyperlink w:docLocation=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G003_video_raz.mp4\"><w:r><w:t>Nearest Dot Algorithm</w:t></w:r></w:hyperlink><w:r><w:t> (MP4) The ghosts snake. This is actually easier than pseudo-random.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Inputs:</w:t></w:r><w:r><w:t> Map Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, &gt;2=Ghost</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Output:</w:t></w:r><w:r><w:t> Direction Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Scoring:</w:t></w:r><w:r><w:t> Total # of Moves to Clear the Yellow Dots +(LivesRemaining-3)*100</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Near Future:</w:t></w:r><w:r><w:t> Ghosts that find minimum path to PACMAT assuming other ghosts are walls. I believe this board is unclearable for equal Ghost and PACMAT speeds. Passing criteria and scoring will be adjusted.</w:t></w:r></w:p></w:body></w:document>"},{"partUri":"/matlab/output.xml","contentType":"text/xml","content":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\" ?><embeddedOutputs><metaData><evaluationState>manual</evaluationState><layoutState>code</layoutState><outputStatus>ready</outputStatus></metaData><outputArray type=\"array\"/><regionArray 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"}]}

The Classic PACMAN game brought to Cody.PACMAT requires clearing the board of Yellow Dots while not bumping into the wandering ghosts in 3 lives. Adjacent Ghosts will capture PACMAT. Ghosts do not use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path to PACMAT. This is similar to the actual game.<img src="https://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg">To aid in development of your routine, a PACMAT_Ghosts_003.m file that creates a video has been posted at <a href="https://sites.google.com/site/razapor/matlab_cody/PACMAT_Ghosts_003.m">PACMAT_Ghosts_003.m</a>. (Right click, 'save link as'). Using patches thus enable/figure, disable/video for best results.<a href="https://sites.google.com/site/razapor/matlab_cody/PACMAT_G003_video_raz.mp4">Nearest Dot Algorithm</a> (MP4) The ghosts snake. This is actually easier than pseudo-random.Inputs: Map Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, >2=GhostOutput: Direction Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No moveScoring: Total # of Moves to Clear the Yellow Dots +(LivesRemaining-3)*100Near Future: Ghosts that find minimum path to PACMAT assuming other ghosts are walls. I believe this board is unclearable for equal Ghost and PACMAT speeds. Passing criteria and scoring will be adjusted.

The Classic PACMAN game brought to Cody.

PACMAT requires clearing the board of Yellow Dots while not bumping into the wandering ghosts in 3 lives. Adjacent Ghosts will capture PACMAT.  Ghosts do not use the tunnel. On Ghost capture everyone gets reset. These trained ghosts take the minimum path to PACMAT.  This is similar to the actual game.

<<https://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg>>

To aid in development of your routine, a PACMAT_Ghosts_003.m file that creates a video has been posted at <https://sites.google.com/site/razapor/matlab_cody/PACMAT_Ghosts_003.m PACMAT_Ghosts_003.m>. (Right click, 'save link as'). Using patches thus enable/figure, disable/video for best results.

<https://sites.google.com/site/razapor/matlab_cody/PACMAT_G003_video_raz.mp4 Nearest Dot Algorithm> (MP4) The ghosts snake. This is actually easier than pseudo-random.

*Inputs:* Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, >2=Ghost

*Output:* Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move

*Scoring:* Total # of Moves to Clear the Yellow Dots +(LivesRemaining-3)*100

*Near Future:* Ghosts that find minimum path to PACMAT assuming other ghosts are walls. I believe this board is unclearable for equal Ghost and PACMAT speeds. Passing criteria and scoring will be adjusted.

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Problem 1244. PACMAT - G03 Ghosts use minimum path to PACMAT; 3 Lives

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Problem 1244. PACMAT - G03 Ghosts use minimum path to PACMAT; 3 Lives

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