Logic programming with solution preferences

Preference logic programming (PLP) is an extension of logic programming for declaratively specifying problems requiring optimization or comparison and selection among alternative solutions to a query. PLP essentially separates the programming of a problem itself from the criteria specification of its solution selection. In this paper we present a declarative method for specifying preference logic programs. The method introduces a precise formalization for the syntax and semantics of PLP. The syntax of a preference logic program contains two disjoint sets of definite clauses, separating a core program specifying a general computational problem from its preference rules for optimization; the semantics of PLP is given based on the Herbrand model and fixed point theory, where how preferences affects the least Herbrand model of a logic program is interpreted as a sequence of meta-level mapping operations. In addition, we present an operational semantics based on a new resolution strategy and a memoized recursive algorithm for computing strictly stratified logic programs with well-formed preferences, and further show that the operational semantics of such a preference logic program is consistent to its declarative semantics.     

A new framework for declarative programming

We propose a new framework for the syntax and semantics of Weak Hereditarily Harrop logic programming with constraints, based on resolution over τ-categories: finite product categories with canonical structure.

Constraint information is directly built-in to the notion of signature via categorical syntax. Many-sorted equational are a special case of the formalism which combines features of uniform logic programming languages (moduels and hypothetical implication) with those of constraint logic programming. Using the cannoical structure supplied by τ-categories, we define a diagrammatic generalization of formulas, goals, programs and resolution proofs up to equality (rather than just up to isomorphism).

We extend the Kowalski-van Emden fixed point interpretation, a cornerstone of declarative semantics, to an operational, non-ground, categorical semantics based on indexing over sorts and programs.

We also introduce a topos-theoretic declarative semantics and show soundness and completeness of resolution proofs and of a sequent calculus over the categorical signature. We conclude with a discussion of semantic perspectives on uniform logic programming.     

Towards a Declarative Semantics of Inheritance with Exceptions

This paper presents a declarative semantics of compositional inheritance in an object-oriented logic programming framework with explicit exceptions,based on the iterated least fixpoint semantics to normal logic programs.Taking logic objects with exceptions as a kind of non-monotonic theory,the nonmonotonicity of inheritance is achieved,which is of importance for modeling incoplete knowledge and requirement specifications in both artificial intelligence and software engineering.     

真稳定类与辩论语义

稳定类语义和辨论语义是逻辑程序语义学研究方面突出的两种语义统一框架理论,它们统一了一些很重要的语义,因为它们有着不同的直觉和概念,它们的相互关系并不清楚。我们在它们最基础的概念层次上证明了,常规逻辑程序句法下二者是等价的,它们有着同样的语义统一能力。基于这个结果,我们为有前途的辨论语义指明一种迭代构造的方法。     

A residualizing semantics for the partial evaluation of functional logic programs

Recent proposals for multi-paradigm declarative programming combine the most important features of functional, logic and concurrent programming into a single framework. The operational semantics of these languages is usually based on a combination of narrowing and residuation. In this paper, we introduce a non-standard, residualizing semantics for multi-paradigm declarative programs and prove its equivalence with a standard operational semantics. Our residualizing semantics is particularly relevant within the area of program transformation where it is useful, e.g., to perform computations during partial evaluation. Thus, the proof of equivalence is a crucial result to demonstrate the correctness of (existing) partial evaluation schemes.     

Confluence and Semantics of Constraint Simplification Rules

Constraint Simplification Rules (CSR) is a subset of the Constraint Handling Rules (CHR) language. CHR is a powerful special-purpose declarative programming language for writing constraint solvers. The CSR subset of CHR forms essentially a committed-choice language consisting of guarded rules with multiple heads that replace constraints by simpler ones until they are solved. This paper gives declarative and operational semantics as well as soundness and completeness results for CSR programs.We also introduce a notion of confluence for CSR programs. Confluence is an essential syntactical property of any constraint solver. It ensures that the solver will always compute the same result for a given set of constraints independent of which rules are applied. It also means that it does not matter for the result in which order the constraints arrive at the constraint solver.We give a decidable, sufficient and necessary syntactic condition for confluence of terminating CSR programs. Moreover, as shown in this paper, confluence of a program implies consistency of its logical meaning (under a mild restriction).     

Operational semantics of Framed Tempura

This paper investigates the operational semantics of temporal logic programs. To this end, a temporal logic programming language called Framed Tempura is employed. The evaluation rules for both the arithmetic and boolean expressions are defined. The semantic equivalence rules for the reduction of a program within a state is formalized. Furthermore, the transition rules within a state and transition rules over an interval between configurations are also specified. Moreover, some examples are given to illustrate how these rules work. Thus, the executable behavior of framed programs can be captured in an operational way. In addition, the consistency between the operational semantics and the minimal model semantics based on model theory is proved in detail.     

Abductive logic programming agents with destructive databases

In this paper we present an agent language that combines agent functionality with a state transition theory and model-theoretic semantics. The language is based on abductive logic programming (ALP), but employs a simplified state-free syntax, with an operational semantics that uses destructive updates to manipulate a database, which represents the current state of the environment. The language builds upon the ALP combination of logic programs, to represent an agent??s beliefs, and integrity constraints, to represent the agent??s goals. Logic programs are used to define macro-actions, intensional predicates, and plans to reduce goals to sub-goals including actions. Integrity constraints are used to represent reactive rules, which are triggered by the current state of the database and recent agent actions and external events. The execution of actions and the assimilation of observations generate a sequence of database states. In the case of the successful solution of all goals, this sequence, taken as a whole, determines a model that makes the agent??s goals and beliefs all true.     

Paraconsistent Declarative Semantics for Extended Logic Programs

We introduce a fixpoint semantics for logic programs with two kinds of negation: an explicit negation and a negation-by-failure. The programs may also be prioritized, that is, their clauses may be arranged in a partial order that reflects preferences among the corresponding rules. This yields a robust framework for representing knowledge in logic programs with a considerable expressive power. The declarative semantics for such programs is particularly suitable for reasoning with uncertainty, in the sense that it pinpoints the incomplete and inconsistent parts of the data, and regards the remaining information as classically consistent. As such, this semantics allows to draw conclusions in a non-trivial way, even in cases that the logic programs under consideration are not consistent. Finally, we show that this formalism may be regarded as a simple and flexible process for belief revision.     

Graph theoretical structures in logic programs and default theories

In this paper we present a graph representation of logic programs and default theories. We show that many of the semantics proposed for logic programs with negation can be expressed in terms of notions emerging from graph theory, establishing in this way a link between the fields. Namely the stable models, the partial stable models, and the well-founded semantics correspond respectively to the kernels, semikernels and the initial acyclic part of an associated graph. This link allows us to consider both theoretical (existence, uniqueness) and computational problems (tractability, algorithms, approximations) from a more abstract and rather combinatorial point of view. It also provides a clear and intuitive understanding about how conflicts between rules are resolved within the different semantics. Furthermore, we extend the basic framework developed for logic programs to the case of Default Logic by introducing the notions of partial, deterministic and well-founded extensions for default theories. These semantics capture different ways of reasoning with a default theory.     

一种新的辩论推理模式及其应用

文中定义了一个新的辩论推理模式,建立了一个形式化的知识表示框架,并把它应用于研究扩展逻辑程序类的说明语义,结果表明,新语义克服了择优语义的不足。作者还根据上述研究结果实现了逻辑程序设计风格下的知识框架。     

A semantics for a class of non-deterministic and causal production system programs

We define a class of function-free rule-based production system (PS) programs that exhibit non-deterministic and/or causal behavior. We develop a fixpoint semantics and an equivalent declarative semantics for these programs. The criterion to recognize the class of non-deterministic causal (NDC) PS programs is based upon extending and relaxing the concept of stratification, to partition the rules of the program. Unlike strict stratification, this relaxed stratification criterion allows a more flexible partitioning of the rules and admits programs whose execution is non-deterministic or causal or both. The fixpoint semantics is based upon a monotonic fixpoint operator which guarantees that the execution of the program will terminate. Each fixpoint corresponds to a minimal database of answers for the NDC PS program. Since the execution of the program is non-deterministic, several fixpoints may be obtained. To obtain a declarative meaning for the PS program, we associate a normal logic program with each NDC PS program. We use the generalized disjunctive well-founded semantics to provide a meaning to the normal logic program Through these semantics, a well-founded state is associated with and a set of possible extensions, each of which are minimal models for the well-founded state, are obtained. We show that the fixpoint semantics for the NDC PS programs is sound and complete with respect to the declarative semantics for the corresponding normal logic program .This research is partially sponsored by the National Science Foundation under grant IRI-9008208 and by the Institute for Advanced Computer Studies.     

A framework for linguistic logic programming

Lawry's label semantics for modeling and computing with linguistic information in natural language provides a clear interpretation of linguistic expressions and thus a transparent model for real‐world applications. Meanwhile, annotated logic programs (ALPs) and its fuzzy extension AFLPs have been developed as an extension of classical logic programs offering a powerful computational framework for handling uncertain and imprecise data within logic programs. This paper proposes annotated linguistic logic programs (ALLPs) that embed Lawry's label semantics into the ALP/AFLP syntax, providing a linguistic logic programming formalism for development of automated reasoning systems involving soft data as vague and imprecise concepts occurring frequently in natural language. The syntax of ALLPs is introduced, and their declarative semantics is studied. The ALLP SLD‐style proof procedure is then defined and proved to be sound and complete with respect to the declarative semantics of ALLPs. © 2010 Wiley Periodicals, Inc.     

对象式逻辑程序设计语言LKO的说明性语义

本文基于逻辑程序设计语言的良基模型语义，探讨了对象逻辑程序设计语言ＬＫＯ的说明性语义，该语义由组合迭代的极小不动点定义，具有构造性和组合性，迷在ＬＫＯ中进一步引入非单调继承和逻辑奠定了基础。     

Semantics of EqL

The formal semantics of a novel language, called EqL, are presented for first-order functional and Horn logic programming. An EqL program is a set of conditional pattern-directed rules, where the conditions are expressed as a conjunction of equations. The programming paradigm provided by this language may be called equational programming. The declarative semantics of equations is given in terms of their complete set of solutions, and the operational semantics for solving equations is an extension of reduction, called object refinement. The correctness of the operational semantics is established through the soundness and completeness theorems. Examples are given to illustrate the language and its semantics.<>     

Semantic forgetting in answer set programming

The notion of forgetting, also known as variable elimination, has been investigated extensively in the context of classical logic, but less so in (nonmonotonic) logic programming and nonmonotonic reasoning. The few approaches that exist are based on syntactic modifications of a program at hand. In this paper, we establish a declarative theory of forgetting for disjunctive logic programs under answer set semantics that is fully based on semantic grounds. The suitability of this theory is justified by a number of desirable properties. In particular, one of our results shows that our notion of forgetting can be entirely captured by classical forgetting. We present several algorithms for computing a representation of the result of forgetting, and provide a characterization of the computational complexity of reasoning from a logic program under forgetting. As applications of our approach, we present a fairly general framework for resolving conflicts in inconsistent knowledge bases that are represented by disjunctive logic programs, and we show how the semantics of inheritance logic programs and update logic programs from the literature can be characterized through forgetting. The basic idea of the conflict resolution framework is to weaken the preferences of each agent by forgetting certain knowledge that causes inconsistency. In particular, we show how to use the notion of forgetting to provide an elegant solution for preference elicitation in disjunctive logic programming.     

Revision programming

In this paper we introduce revision programming — a logic-based framework for describing constraints on databases and providing a computational mechanism to enforce them. Revision programming captures those constraints that can be stated in terms of the membership (presence or absence) of items (records) in a database. Each such constraint is represented by a revision rule ← ₁,…,_k, where and all ga_i are of the form in(a) and out(b). Collections of revision rules form revision programs. Similarly as logic programs, revision programs admit both declarative and imperative (procedural) interpretations. In our paper, we introduce a semantics that reflects both interpretations. Given a revision program, this semantics assigns to any database B a collection (possibly empty) of P-justified revisions of B. The paper contains a thorough study of revision programming. We exhibit several fundamental properties of revision programming. We study the relationship of revision programming to logic programming. We investigate complexity of reasoning with revision programs as well as algorithms to compute P-justified revisions. Most importantly from the practical database perspective, we identify two classes of revision programs, safe and stratified, with a desirable property that they determine for each initial database a unique revision.