| 词汇 | example_english_literal |
| 释义 | Examples of literalThese examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. Within this framework, positive relevance literals denote addition of complexity and detail whilst negative relevance literals denote removal of complexity and detail. We assume we have a set of items of structured text, and that we will represent these by a set of positive literals. We represent each news report as a conjunction of classical logic literals. Our extended definition and the classical one coincide either for definite programs or for general programs whose negative literals have only input positions. The two definitions coincide either for definite programs or for general programs whose negative literals have only input positions. Sets of default negated literals are considered as extensions of the program, and a notion of "attack" between these sets is defined. Intuitively the truth of literals in these sequences must be determined in order to prove or fail the abductive subgoal. Elementary formulas are literals and the symbols ("false") and ("true"). Defining tightness relative to a set of literals extends the applicability of this method to some programs that are not absolutely tight. Their bodies usually contain special fluent literals of the form ab(c). Elementary formulas are literals and the 0-place connectives ("false") and ("true"). The body of rules is composed of negative literals only. Nevertheless, for the cases in which there are no conflicting literals in the first of the programs, the operators in question would be equivalent. The first four rules define literals and contrary literals. One of the main reasons is that, for each predicate mode declaration, the compiler is required to appropriately re-order literals in the predicate's definition. Obviously, the answer set also includes many other ground literals that we are not interested in listing here. Furthermore, two literals that are not complementary can also disagree. In the following example we show an argument and a counter-argument supporting literals that are not complementary. Each node contains a conjunction of literals including equality atoms. Nodes containing only a satisfiable set of equality atoms and non-grounded negative literals are said to be floundered and have no children. Finally, we consistently view clauses as disjunctions of their literals. We will say that head() depends on, or is defined in terms of, the literals in body(). If all the literals in the body are positive, the clause is a definite clause. Storing literals in a separate sorted table allows for indexed search for prefixes and numeric values. Concerning efficient algorithms, it is essentially known that random formulas with n nk/2 clauses with k literals can be efficiently certified as unsatisfiable. Numeric literals are defined in this indirect way so that they may be interpreted as values of any appropriate numeric type. We label the internal nodes by and with equal probability, and the leaves by the 2n literals, again with equal probability. Thus, when the dialectical analysis is carried out, default negated literals could be defeated by arguments. Clauses that contain only positive literals (negative literals, respectively) are called all-positive clauses (all-negative clauses, respectively). A clause is satis®ed if and only if any one of its literals evaluates to true. Each clause is a disjunction of literals, where each of which is a variable or its negation. To this end we need to consider as basic data types also literals. Intuitively, the distinction between goal literals and delay literals is that goal literals are currently selected within a node or are yet to be selected. As the program, except the constraint, is positive we can use classical negation () and dispense the inconsistent literals and rules. Atoms, literals, rules, and programs that do not contain variables are ground. In the rest of this paper, we rely on the assumption that the order of literals in the body of rules is irrelevant. Occasionally, we use the same term to denote the set of all literals in the body of a clause. As discussed in the beginning of this section, they involve a reduction of logic programs which is based on a pre-interpretation of negative literals. In particular, let us point out that negative default literals are treated classically at this point. As we can see, treating aggregates like negation-as-failure literals yields non-minimal answer sets. Thus, the third rule above states that for each clause, at least one of its literals must be satisfied. We emphasize that while the body of a rule consists of literals, positive and negative bodies are defined to consist of atoms. With this new definition of defeater, default negated literals become new points of attack. Therefore, accepting 1 for p and 2 for p would render both literals warranted. In other words, strictly derived literals can not be rebutted. Facts are literals that are treated as known knowledge (given or observed facts of a case). To introduce specifications for normal programs let us first consider definite programs with queries which may contain negative literals. Either operation will union the abductive context of a parent node with new objective literals to produce a new child node. A world view of is consistent if it does not contain the belief set of all literals. Further optimization can be obtained by immediately removing obviously true body literals from the conditional facts already during the fixpoint computation. The following two transformations reduce negative body literals if their truth value is obvious. The four a parts of this implementation, that reduce body literals, correspond directly to the four basic transformations defined above. Finally, statements 24-28 add a constraint for each adorned predicate, which prevents the generation of inconsistent sets of literals. In their approach, the revised programs contain newly introduced literals. Using decreasing order of indices, favors the retention of literals whose corresponding variable has been selected first. Numerical literals such as integers, floats, and decimals are also supported. The length of a formula is the number of input literals. Both the preconditions and eects can be positive or negative literals. Any data type in which one can only pass around compile-time literals, is hardly 'first-class'. The literals are called incompleteness-assumption literals, abbreviated to assumption literals. Of course, all those literals could be combined to gain more complex rules. Other literals represent changes in assumptions, other than relevance claims, between model fragments of the same assumption class. In turn, each clause is a disjunction of literals, which are instances of possibly negated variables. The symbols " -< " and " " denote meta-relations between sets of literals, and have no interaction with language symbols. The following proposition shows that strictly derived literals are 'equi-specific'. Thus, f lies(tina) and f lies(tina) are contradictory literals. Atoms and literals are either base or derived, according to their predicate symbols. The sets of all ground terms, atoms and literals over will be denoted by terms(), atoms() and l it() respectively. The first assumption allows us to treat clauses interchangingly as disjunctions of their literals or as sets of their literals. Nodes containing a satisfiable set of equality atoms and no other literals are said to be successful and have no children. If contains no ordinary literals, r is said to be prerequisite-free. Only positive body literals can be removed which are known as facts, and the set of facts can only increase during this process. Variables can appear in the annotations, and the annotation of the head predicate is usually a function of body literals annotations. Depending on its syntax, it may be necessary to unfold the formula into additional program rules, but will usually be a conjunction of literals. The repair programs we presented materialize the closed-world assumption by explicitly producing the negative primed literals. In the bodies, literals may be affected by weak negation, not (negation as failure). Thus, it is sufficient for the set of abducibles to contain only objective literals. Analogous to a co-unfounded set of answers are the non-supported objective literals. When a program undergoes the dual transformation, negative literals involved in infinite recursion must be made to succeed. In a dual program, there is also the dual notion of a co-unfounded set of literals. A free clause is built from a disjunction of literals in the head and a conjunction of literals in the body. Effectively, this definition treats aggregates in the same fashion as negation-as-failure literals. Since the running times of smodels may also depend on the order of rules in programs and literals in rules, we shuffle them randomly. The tags + and -, as well as the corresponding sets of literals, depend on each other, so we will carry out the proofs simultaneously. In addition, we have constants to denote fluents, literals and actions in the domain. Based on the failure or success of these queries, only the ones with the 'best' results are kept and are extended (by adding literals). A set of literals is interpreted as the conjunction of its members. A normal aggregate program is an aggregate program in which the bodies of all rules are conjunctions of literals and aggregate atoms. Recall that we assume that each sensing action senses at least two literals. Negative literals corresponding to the first argument are not considered, because weak negation will be applied. Only literals with the recursive predicate p appear in conditions. Similarly, those literals with no (strict) rules for them are unprovable (strictly). As a consequence, the head of rules may contain also such negative literals and rules can be conflicting on some literals. Also, we consider definite logic programs, as opposed to logic programs that also contain negated literals in clause bodies. We have also shown that semantics which reduce the set of unknown literals have high complexity (and also high expressive power). The application of the well-founded semantics gives a model containing as defined literals only new (a) and all other derived atoms are undefined. Two further conditions qualify the (disjunctive) head and the negative body literals of the rule r depending on their truth values. These examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. |
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