Auto Topic: members
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Coverage Score
1
Mentioned Chunks
24
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1
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| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: member | CO_OCCURS | Auto Topic: members | 7 |
| Auto Topic: members | CO_OCCURS | Propositional Logic | 6 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... aller set and an element: ¬∃x,s Add (x,s) ={ }. 3. Adding an element already in the set has no effect: ∀x,s x ∈s ⇔ s =Add(x,s). 4. The only members of a set are the elements that were added into it. We express this recursively, saying that x is a member of s if and only if s is e ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... cts. First-order logic makes it easy to state facts about categories, either by relating objects to categories or by quantifying over their members. Here are some example facts: • An object is a member of a category. BB9∈Basketballs • A category is a subclass of another category. ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... member of s2: ∀x,s x ∈s ⇔ ∃ y,s2 (s =Add(y,s2) ∧ (x =y ∨ x ∈s2)). 5. A set is a subset of another set if and only if all of the first set’s members are members of the second set: ∀s1,s2 s1 ⊆ s2 ⇔ (∀x x ∈s1 ⇒ x ∈s2). 6. Two sets are equal if and only if each is a subset of the oth ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... e 20.5 shows 746 Chapter 20 Knowledge in Learning + + + + + + ++ + + – – – – – – – – – – – –– – S1 G1 G2 Figure 20.5 The extensions of the members of G and S. No known examples lie in between the two sets of boundaries. the situation: there are no known examples outside S but ins ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... in Greek mythology, asked that everything he touched should turn to gold, but then regretted it after touching his food, drink, and family members.16 We touched on this issue in Section 1.1.5, where we pointed out the need for a significant modification to the standard model of pu ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... mbolsRichard and John. Section 8.2 Syntax and Semantics of First-Order Logic 277 . . . . . . . . . R J R R R R R J J J J J Figure 8.4 Some members of the set of all models for a language with two constant symbols, R and J, and one binary relation symbol. The interpretation of eac ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... the others being William and Henry. Section 8.3 Using First-Order Logic 283 . . . R J J R R J R J R J R J R J R J R J R J Figure 8.5 Some members of the set of all models for a language with two constant symbols, R and J, and one binary relation symbol, under database semantics. ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | some properties. (x∈Basketballs)⇒ Spherical(x) • Members of a category can be recognized by some properties. Orange(x)∧ Round(x)∧ Diameter(x) =9.5′′∧ x∈Balls⇒ x∈Basketballs • A category as a whole has some properties. Dogs∈DomesticatedSpecies 1 Turning a proposition into an objec ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... n we have not said that an undergraduate cannot also be a graduate student. We say that two or more categories are disjoint if they have no members in common. WeDisjoint may also want to say that the classes undergrad and graduate student form an exhaustive decomposition of unive ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... iding exact definitions for most natural categories was explained in depth by Wittgenstein (1953). He used the example of games to show that members of a category shared “family resemblances” rather than necessary and sufficient characteristics: what strict definition encompasses ch ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... cialized categories of butter such asUnsaltedButter, which is also a kind of stuff. Note that the category PoundOfButter, which includes as members all butter-objects weighing one pound, is not a kind of stuff. If we cut a pound of butter in half, we do not, alas, get two pounds ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ws the MemberOf link from Mary to the category she belongs to, and then 5 Several early systems failed to distinguish between properties of members of a category and properties of the category as a whole. This can lead directly to inconsistencies, as pointed out by Drew McDermott ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... late themonotonicity property of Monotonicity logic that was proved in Chapter 7. 8 In this chapter we saw that a property inherited by all members of a category in a semantic network could be overridden by more specific informa- tion for a subcategory. In Section 9.4.4, we saw th ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... figure prominently in Chapters 14 and 16; the simulated annealing algo- rithm in Chapter 4 and the WALK SAT algorithm in Chapter 7 are also members of the MCMC family.) We begin by describing a particular form of MCMC called Gibbs sampling, whichGibbs sampling is especially well ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... e model is that when a group of agents cooperate, the group as a whole obtains some utility value, which can then be split among the group members. The model does not say what actions the agents will take, nor does the game structure itself specify how the value obtained will be ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... e Theory 617 that goes to player i. The payoff must satisfy the constraint that each coalition C splits up all of its value ν(C) among its members: ∑ i∈C xi = ν(C) for all C∈ CS For example, given the game ({1,2,3}, ν) where ν({1}) =4 and ν({2,3}) =10, a possible outcome is: ({{1 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... to the set of players preceding i in the ordering. We letP denote all possible permutations (e.g., orderings) of the players N, and denote members ofP by p, p′,... etc. Where p∈P and i∈ N, we denote by pi the set of players that precede i in the ordering p. Then the Shapley valu ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... 18 Probabilistic Programming . . . . . . . . . R J R R R R R J J J J J . . . R J J R R J R J R J R J R J R J R J R J Figure 18.1 Top: Some members of the set of all possible worlds for a language with two constant symbols, R and J, and one binary relation symbol, under the standa ... |