Auto Topic: loginid
auto_loginid | topic
Coverage Score
1
Mentioned Chunks
4
Mentioned Docs
1
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
loginid
Relations
| Source | Type | Target | W |
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Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.67 | 11 | ... 0.01 Kindness⟨Customer,,1⟩ 4 0 .3 Kindness⟨Customer,,2⟩ 1 0 .1 Quality⟨Book,,1⟩ 1 0 .05 Quality⟨Book,,2⟩ 3 0 .4 Quality⟨Book,,3⟩ 5 0 .15 #LoginID⟨Owner,⟨Customer,,1⟩⟩ 1 1 .0 #LoginID⟨Owner,⟨Customer,,2⟩⟩ 2 0 .25 Recommendation⟨LoginID,⟨Owner,⟨Customer,,1⟩⟩,1⟩,⟨Book,,1⟩ 2 0 .5 Re ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ⟩ 1 0 .4 Recommendation⟨LoginID,⟨Owner,⟨Customer,,2⟩⟩,2⟩,⟨Book,,1⟩ 5 0 .4 Recommendation⟨LoginID,⟨Owner,⟨Customer,,2⟩⟩,2⟩,⟨Book,,2⟩ 5 0 .4 Recommendation⟨LoginID,⟨Owner,⟨Customer,,2⟩⟩,2⟩,⟨Book,,3⟩ 1 0 .4 Figure 18.4 One particular world for the book recommendation OUPM. The numbe ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... umber statement. For number statements with origin functions—e.g., Equation (18.3)—each object records its origin; for example, the object ⟨LoginID, ⟨Owner, ⟨Customer,, 2⟩⟩,3⟩ is the third login be- longing to the second customer. The number variablesof an OUPM specify how many o ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... iformInt(2,4). (18.2) We expect honest customers to have just one ID, whereas dishonest customers might have anywhere between 2 and 5 IDs: #LoginID(Owner =c) ∼ if Honest(c) then Exactly(1) else UniformInt(2,5). (18.3) This number statement specifies the distribution over the numbe ... |