this paper introduces techniques for updating knowledge bases represented in extended logic programs. three different types of updates, view updates, theory updates, and inconsistency removal, are considered. We formu...
详细信息
ISBN:
(纸本)3540667490
this paper introduces techniques for updating knowledge bases represented in extended logic programs. three different types of updates, view updates, theory updates, and inconsistency removal, are considered. We formulate these updates through abduction, and provide methods for computing them with update programs. An update program is an extended logic program which specifies changes on abductive hypotheses, then updates are computed by the U-minimal answer sets of an update program. the proposed technique provides a uniform framework for these different types of updates, and each update is computed using existing procedures of logicprogramming.
dlv is a knowledge representation system, based on disjunctive logicprogramming, which offers front-ends to several advanced KR formalisms. this paper describes new techniques for the computation of answer sets of di...
详细信息
ISBN:
(纸本)3540667490
dlv is a knowledge representation system, based on disjunctive logicprogramming, which offers front-ends to several advanced KR formalisms. this paper describes new techniques for the computation of answer sets of disjunctive logic programs, that have been developed and implemented in the dlv system. these techniques try to "push" the query goals in the process of model generation (query goals are often present either explicitly, like in planning and diagnosis, or implicitly in the form of integrity constraints). this way, a lot of useless models are discarded "a priori" and the computation converges rapidly toward the generation of the "right" answer set. A few preliminary benchmarks show dramatic efficiency gains due to the new techniques.
Przymusinski9;s Autoepistemic logic of Knowledge and Belief (AELKB) is a unifying framework for various non-monotonic formalisms. In this paper we present a semantic characterization of AELKB in terms of Dynamic Kr...
详细信息
ISBN:
(纸本)3540667490
Przymusinski's Autoepistemic logic of Knowledge and Belief (AELKB) is a unifying framework for various non-monotonic formalisms. In this paper we present a semantic characterization of AELKB in terms of Dynamic Kripke Structures (DKS). A DKS is composed of two components - a static one (a Kripke structure) and a dynamic one (a set of transformations). Transformations between possible worlds correspond to hypotheses generation and to revisions. therefore they enable to define a semantics of insertions to and revisions of AELKB-theories. A computation of the transformations (between possible worlds) is based on (an enhanced) model-checking. the transformations may be used as a method of computing static autoepistemic expansions.
this paper reports on systematic research which aims to classify non-monotonic logics by their expressive power. the classification is based on translation functions that satisfy three important criteria: polynomialit...
详细信息
ISBN:
(纸本)3540667490
this paper reports on systematic research which aims to classify non-monotonic logics by their expressive power. the classification is based on translation functions that satisfy three important criteria: polynomiality, faithfulness and modularity (PFM for short). the basic method for classification is to prove that PFM translation functions exist (or do not exist) between certain logics. As a result, non-monotonic logics can be arranged to form a hierarchy. this paper gives an overview of the current expressive power hierarchy (EPH) and investigates semi-normal default logic as well as prerequisite-free and semi-normal default logic in order to locate their exact positions in the hierarchy.
We present many-valued disjunctive logic programs in which classical disjunctive logic program clauses are extended by a truth value that respects the material implication. Interestingly, these many-valued disjunctive...
详细信息
ISBN:
(纸本)3540667490
We present many-valued disjunctive logic programs in which classical disjunctive logic program clauses are extended by a truth value that respects the material implication. Interestingly, these many-valued disjunctive logic programs have both a probabilistic semantics in probabilities over possible worlds and a truth-functional semantics. We then define minimal, perfect, and stable models and show that they have the same properties like their classical counterparts. In particular, perfect and stable models are always minimal models. Under local stratification, the perfect model semantics coincides withthe stable model semantics. Finally, we show that some special cases of propositional many-valued disjunctive logicprogramming under minimal, perfect, and stable model semantics have the same complexity like their classical counterparts.
In this paper, we propose a new semantics for disjunctive logicprogramming and deductive databases. the semantics, called minimal founded, generalizes stable model semantics for normal (i.e. non disjunctive) programs...
详细信息
ISBN:
(纸本)3540667490
In this paper, we propose a new semantics for disjunctive logicprogramming and deductive databases. the semantics, called minimal founded, generalizes stable model semantics for normal (i.e. non disjunctive) programs but differs from disjunctive stable model semantics (the extension of stable model semantics for disjunctive programs). Compared with disjunctive stable model semantics, the minimal founded semantics seems to be, in some case, more intuitive, it gives meaning to programs which are meaningless under stable model semantics and it is not harder to compute. We study the expressive power of the semantics and show that for general disjunctive datalog programs it has the same power of disjunctive stable model semantics. We also present a variation of the minimal founded semantics, called strongly founded which on stratified programs coincide withthe perfect model semantics.
Extended logic programs and annotated logic programs are two important extensions of normal logic programs that allow for a more concise and declarative representation of knowledge. Extended logic programs add explici...
详细信息
ISBN:
(纸本)3540667490
Extended logic programs and annotated logic programs are two important extensions of normal logic programs that allow for a more concise and declarative representation of knowledge. Extended logic programs add explicit negation to the default negation of normal programs in order to distinguish what can be shown to be false from what cannot be proven true. Annotated logic programs generalize the set of truth values over which a program is interpreted by explicitly annotating atoms with elements of a new domain of truth values. In this paper coherent well-founded annotated programs are defined, and shown to generalize both consistent and paraconsistent extended programs, along with several classes of annotated programs.
We introduce choice logic programs as negation-free datalog programs that allow rules to have exclusive-only (possibly empty) disjunctions in the head. Such programs naturally model decision problems where, depending ...
详细信息
ISBN:
(纸本)3540667490
We introduce choice logic programs as negation-free datalog programs that allow rules to have exclusive-only (possibly empty) disjunctions in the head. Such programs naturally model decision problems where, depending on a context, agents must make a decision, i.e. an exclusive choice out of several alternatives. It is shown that such a choice mechanism is in a sense equivalent with negation as supported in semi-negative ("normal") datalog programs. We also discuss an application where strategic games can be naturally formulated as choice programs: it turns out that the stable models of such programs capture exactly the set of Nash equilibria. We then consider the effect of choice on "negative information" that may be implicitly derived from a program. Based on an intuitive notion of unfounded set for choice programs. ive show that several results from (seminegative) disjunctive programs can be strengthened;characterizing the position of choice programs as an intermediate between simple positive programs and programs that allow for the explicit use of negation in the body of a rule.
this paper proposes a new knowledge representation language, called QDLP, which extends DLP to deal with uncertain values. A certainty degree interval (a subinterval of [0, 1]) is assigned to each (quantitative) rule....
详细信息
ISBN:
(纸本)3540667490
this paper proposes a new knowledge representation language, called QDLP, which extends DLP to deal with uncertain values. A certainty degree interval (a subinterval of [0, 1]) is assigned to each (quantitative) rule. Triangular norms (T-norms) are employed to define calculi for propagating uncertainty information from the premises to the conclusion of a quantitative rule. Negation is considered and the concept of stable model is extended to QDLP. Different T-norms induce different semantics for one given quantitative program. In this sense, QDLP is parameterized and each choice of a T-norm induces a different QDLP language. Each T-norm is eligible for events with determinate relationships (e.g., independence, exclusiveness) between them. Since there are infinitely many T-norms, it turns out that there is a family of infinitely many QDLP languages. this family is carefully studied and the set of QDLP languages which generalize traditional DLP is precisely singled out. Finally the complexity of the main decisional problems arising in the context of QDLP (i.e., Model Checking, Stable Model Checking, Consistency, and Brave reasoning) is analyzed. It is shown that the complexity of the relevant fragments of QDLP coincides exactly withthe complexity of DLP. that is, reasoning with uncertain values is more general and not harder than reasoning with boolean values.
Data standardization is the commercially important process of extracting useful information from poorly structured textual data. this process includes correcting misspellings and truncations, extraction of data via pa...
详细信息
ISBN:
(纸本)3540667490
Data standardization is the commercially important process of extracting useful information from poorly structured textual data. this process includes correcting misspellings and truncations, extraction of data via parsing, and correcting inconsistencies in extracted data. Prolog programming offers natural advantages for standardizing: definite clause grammars can be used to parse data;Prolog rules can be used to correct inconsistencies;and Prolog's simple syntax allows rules to be generated to correct misspellings and truncations of keywords. these advantages can be seen as rudimentary mechanisms for knowledge representation and at least one commercial standardizer has exploited these advantages. However advances in implementation and in knowledge representation - in particular the addition of preferences to logical formalisms - allow even more powerful and declarative standardizers to be constructed. In this paper a simple preference logic, that of [7] is considered. A fixed point semantics is defined for this logic and its tabled implementation within XSB is described. Development of a commercial standardizer using the preference logic of [7] is then documented. Finally, detailed comparisons are made between the preference logic standardizer and the previous Prolog standardizer illustrating how an advance in knowledge representation can lead to improved commercial software.
暂无评论