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How do you use resolution to solve FOL and CNF sentences? Prompt: Use resolution to answer the question Is there a member of the Girls

How do you use resolution to solve FOL and CNF sentences?

Prompt: Use resolution to answer the question "Is there a member of the Girls Coding Club who is a Linux-user and not a Windows-user?"

Blossom, Bubbles and Buttercup are members of the Girls Coding Club.

Every member of the Girls Coding Club is either a Linux-user or a Windows-user or both.

No Linux-user likes Microsoft.

All Windows-users like MSWord.

Buttercup dislikes whatever Blossom likes and likes whatever Blossom dislikes.

Blossom likes Microsoft and MSWord.

1.

FOL: Blossom, Bubble and Buttercup are menbers of Girls Coding Club

CNF: member(Blossom) image text in transcribed member(Bubble) image text in transcribed member(Buttercup) --- (ANS)

2.

FOL: For all x (member(x) ---->Linux(x) or Windows(x) or (Linux(x) and Windows(x))

CNF:

member(x) image text in transcribed(Linux(x) image text in transcribed Windows(x) image text in transcribed (Linux(x) image text in transcribed Windows(x)))

image text in transcribed member(x) image text in transcribed( Linux(x) image text in transcribed Windows(x) image text in transcribed(Linux(x) image text in transcribed Windows(x) ))

image text in transcribed( member(x) image text in transcribedimage text in transcribed(Linux(x) image text in transcribed Windows(x) image text in transcribed(Linux(x) image text in transcribed Windows(x) )))

image text in transcribed ( member(x) image text in transcribedimage text in transcribedLinux(x) image text in transcribedimage text in transcribedWindows(x) image text in transcribed image text in transcribed(Linux(x) image text in transcribed Windows(x) ))) --- (ANS)

3.

FOL: For all x(Linux(x)--->not likes(x,Microsoft)

CNF:

Linux(x) image text in transcribedimage text in transcribed likes(x,Microsoft)

image text in transcribed Linux(x) image text in transcribedimage text in transcribed likes(x,Microsoft)

image text in transcribed (Linux(x) image text in transcribed likes(x,Microsoft)) --- (ANS)

4.

FOL: Far all x (Windows(x)-->likes(x,MSWord)

CNF:

Windows(x) image text in transcribed likes(x,MSWord)

image text in transcribed Windows(x) image text in transcribed likes(x,MSWord)

image text in transcribed (Windows(x) image text in transcribedimage text in transcribed likes(x,MSWord)) --- (ANS)

5.

FOL:

a) For all x (Likes(Buttercup,x)--->Not Likes(Blossom,x))

b) For all x (Not Likes(Buttercup,x)---->Likes(Blossom,x))

CNF:

a)

Likes(Buttercup,x) image text in transcribedimage text in transcribed Likes(Blossom,x)

image text in transcribed Likes(Buttercup,x) image text in transcribedimage text in transcribed Likes(Blossom,x)

image text in transcribed (Likes(Buttercup,x) image text in transcribed Likes(Blossom,x)) --- (ANS)

b)

image text in transcribed Likes(Buttercup,x) image text in transcribed Likes(Blossom,x)

Likes(Buttercup,x) image text in transcribed Likes(Blossom,x)

image text in transcribed (image text in transcribed Likes(Buttercup,x) image text in transcribedimage text in transcribed Likes(Blossom,x)) --- (ANS)

6.

FOL: Likes(Blossom,Microsoft) and Likes(Blossom, MSWord)

CNF: Likes(Blossom,Microsoft) image text in transcribed Likes(Blossom, MSWord)

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