Auto Topic: member
auto_member | topic
Coverage Score
1
Mentioned Chunks
32
Mentioned Docs
1
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
member
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: member | CO_OCCURS | Auto Topic: members | 7 |
| Auto Topic: member | CO_OCCURS | Propositional Logic | 5 |
| Auto Topic: member | CO_OCCURS | Bidirectional Search | 3 |
| Auto Topic: member | CO_OCCURS | State-Space Search | 3 |
| Auto Topic: member | CO_OCCURS | Logical Agents | 3 |
| Auto Topic: basketballs | CO_OCCURS | Auto Topic: member | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.67 | 7 | ... ent, we need the following two properties: 1. Every consistent hypothesis (other than those in the boundary sets) is more specific than some member of the G-set, and more general than some member of the S-set. (That is, there are no “stragglers” left outside.) This follows directl ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | is the same as x or x is a 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 e ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... he empty set is a constant written as { }. There is one unary predicate, Set, which is true of sets. The binary predicates are x ∈s (x is a member of set s) and s1 ⊆ s2 (set s1 is a subset of s2, possibly equal to s2). The binary functions are s1 ∩s2 (intersection), s1 ∪s2 (union ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... the empty set. We need a way to build up sets from elements or from operations on other sets. We will want to know whether an element is a member of a set and we will want to distinguish sets from objects that are not sets. We will use the normal vocabulary of set theory as synta ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... cts. That is, we can use the predicate Basketball(b), or we can reify1 the category as Reification an object, Basketballs. We could then say Member(b,Basketballs), which we will abbre- viate as b∈Basketballs, to say that b is a member of the category of basketballs. We say Subset( ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... Haldane said “An inordinate fondness for beetles.” 336 Chapter 10 Knowledge Representation Notice that because Dogs is a category and is a member of DomesticatedSpecies, the latter must be a category of categories. Of course there are exceptions to many of the above rules (punctu ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... potheses consistent with all the examples so far. It is represented by the S-set and G-set, each of which is a set of hypotheses. • Every member of the S-set is consistent with all observations so far, and there are no consistent hypotheses that are more specific. • Every member o ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ween the boundaries.) Any h between S and G must reject all the negative examples rejected by each member of G (because it is more specific), and must accept all the pos- itive examples accepted by any member of S (because it is more general). Thus, h must agree with all the examp ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... ative for Si: This means Si is too specific, so we replace it by all its immediate generalizations, provided they are more specific than some member ofG. 3. False positive for Gi: This means Gi is too general, so we replace it by all its immediate specializations, provided they are ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... wn University and a Ph.D. in computer science from Berkeley. He has been a professor at the University of Southern California and a faculty member at Berkeley and Stanford. He is a Fellow of the American Association for Artificial Intelligence, the Association for |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | has been a professor at the University of Southern California and a faculty member at Berkeley and Stanford. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, the American Academy of Arts and Sciences, and the Californ ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... either frontier. When the evaluation 9 In our implementation, the reached data structure supports a query asking whether a given state is a member, and the frontier data structure (a priority queue) does not, so we check for a collision using reached; but concep- tually we are as ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... an be guaranteed to be optimally efficient—any algorithm might expand up to twice the minimum number of nodes if it always chooses the wrong member of a pair to expand first. Some bidirectional heuristic search algorithms explicitly manage a queue of (m,n) pairs, but we will stick ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... epresented as an ex- plicit set of all tuples of values that satisfy the constraint, or as a function that can compute whether a tuple is a member of the relation. For example, if X1 and X2 both have the do- main {1,2,3}, then the constraint saying that X1 must be greater than X2 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ut 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. Basketballs⊂ Balls • All members of a category have so ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... e, we need to be careful not to assert that a category has legs; the single-boxed link in Figure 10.4 is used to assert properties of every member of a category. The semantic network notation makes it convenient to perform inheritance reasoning of the kind introduced in Section 1 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ert properties of every member of a category. The semantic network notation makes it convenient to perform inheritance reasoning of the kind introduced in Section 10.2. For example, by virtue of being a person, Mary inherits the property of having two legs. Thus, to find out how m ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... e that we could also override the default number of legs by creating a category of OneLeggedPersons, a subset of Persons of which John is a member. We can retain a strictly logical semantics for the network if we say that theLegs assertion for Persons includes an exception for Jo ... |