A Declarative Approach to Business Rules in Contracts: Courteous Logic Programs in XML

We argue for an XML encoding of FIPA Agent Communication Language (ACL), and give an alpha version of it, called Agent Communication Markup Language (ACML), which we have implemented. The XML approach facilitates: (a) developing/maintaining parsers, integrating with WWW-world software engineering, and (b) the enriching capability to (hyper-)link to ontologies and other extra information. The XML approach applies similarly to KQML as well. Motivated by the importance of the content language aspect of agent communication, we focus in particular on business rules as a form of content that is important in e-commerce applications such as bidding negotiations. A leading candidate content language for business rules is Knowledge Interchange Format (KIF), which is currently in the ANSI standards committee process. We observe several major practical shortcomings of KIF as a content language for business rules in e-commerce. We argue instead for a knowledge representation (KR) approach based on Courteous Logic Programs (CLP) that overcomes several of KIF’s representational limitations, and argue for this CLP approach, e.g., for its logical non-monotonicity and its computational practicality. CLP is a previous KR that expressively extends declarative ordinary logic programs cf. Prolog; it includes negation-as-failure plus prioritized conflict handling. We argue for an XML encoding of business rules content, and give an alpha version of it, called Business Rules Markup Language (BRML), which we have implemented. BRML can express both CLP and a subset of KIF (i.e., of first-order logic) that overlaps with CLP. BRML expressively both extends and complements KIF. The overall advantages of an XML approach to content language are similar to those for the XML approach to ACL, and indeed complements the latter since content is carried within ACL messages. We have implemented both ACML and BRML/CLP; a free alpha prototype of BRML/CLP, called IBM CommonRules, was released on the Web in July of 1999.

The declarative approach specifies what is to be done rather than how to do it. When adopted in information systems development, this implies that the system should be seen as a collection of business rules that can be enacted using a business rules engine. Business rules should be expressed in a form that is as close to the one in which business people perceive the rules. A business rules management system is needed to acquire, store, and allow modification of a business rules database. The rules are then handed over to a rules engine for enactment. The BRMS considered in this chapter uses an antecedent-consequent form for representing rules. These are based in a first order logic. Rules are formed with courses of actions and conditions in rules antecedents and courses of actions in rule consequents. It also introduces notions of state change in the business rule and temporal relation within rule and between different rules. Business rules are structured into atomic, complex, and abstract rules. The business rules are translated into enactment rules and converted to Java.

A formal model of analogy is introduced in the logic programming setting, and an analogical reasoning program (called DIANA, i.e. Declarative Inference by ANAlogy) is developed in accordance with precise procedural and declarative semantics. Given the source and target domains of analogy as two logic programsPs andPt, together with a specificationS of the analogical correspondence between predicate symbols, atoms involving these symbols are analogically derived fromP=Ps ∪Pt givenS, which are not derivable fromPs orPt orPs ∪Pt alone. In this paper, the requirements of the analogical process are first stated. The declarative semantics of analogy is then given, by defining the least analogical model ofP as an extension of the classical semantics of Horn clauses. A procedural semantics is also described, in terms of an extension of SLD resolution. Both semantics rely on implicit analogical axioms defining the kind of analogical reasoning envisaged. The implementation of DIANA has been done in Reflective Prolog, a metalogic programming language previously developed by the first two authors. It is shown that analogical axioms can be viewed as an instance of reflection axioms used in Reflective Prolog. By exploiting this feature, the implementation of DIANA is argued to be sound w.r.t. the defined semantics. Examples of analogical reasoning in DIANA are also described. By comparison with the AI literature on analogy, it is claimed that this is the first approach which gives a declarative semantics to analogical reasoning, thanks to the possibility of carrying over in this field the basic logic programming concepts.

Binary logic programs can be obtained from ordinary logic programs by a binarizing transformation. In most cases, binary programs obtained by this way are less efficient than the original programs. Demoen [2] showed an interesting example of a logic program whose computational behavior was improved if it was transformed to a binary program and then specialized by partial deduction.The class of so called B-stratifiable logic programs is defined. It is shown that for every B-stratifiable logic program, binarization and subsequent partial deduction produce a binary program which usually has a better computational behavior than the original one. Both binarization and partial deduction can be automated.KeywordsLogic ProgramOriginal ProgramPredicate SymbolUnit ClausePartial EvaluatorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Management of the whole product lifecycle is a major stake that most companies have now integrated into their strategies. Research works have been focused on this asset but mainly for mechanical products. In previous work proposes a knowledge modelling approach in a mechanical context, centred on expert users’ collaboration. In this paper, we worked with an aeronautical company in charge of electrical maintenance. Its objective was to improve its business processes, from design phase to production phase, by developing a new PLM system, integrated to its electrical CAD software. Our research aim is to evaluate the genericity of previous knowledge modelling approach in this electrical context. We propose then an integrated knowledge model based on three dimensions: (electric) product modelling, (aeronautics retrofitting) process/project modelling and organization/collaboration modelling. We show how this model identifies generic vs expert knowledge and how ‘business rules’ can be defined to support collaboration and to reduce time of development process. Finally, we propose a global PLM system and we detail the impact on the collaborations and on the whole ‘product’ lifecycle. This PLM system is actually developed by an electrical CAD editor, ensuring the integration between CAD data and PLM database. We conclude on future work extending this first PLM system: a generic problem-solving approach for capitalizing and reusing knowledge then an augmented reality environment for using PLM product data during installation phase.

Active Logic is a conceptual system, the principles of which are satisfied with formalisms of reasoning, which allow correlating the results of reasoning with moments in time and ensuring tolerance to contradictions. At present, tolerance to contradictions (paraconsistency) as applied to Active Logic systems has received theoretical justification in the works of the authors of this report and refers to the so-called formalisms of step theories, combining the principles of Active Logic and logical programming. In particular, it was proved that the argumentative semantics of the formalism of extended step theories, which have two types of negation and have more developed expressive capabilities, are paranormal. The formalism introduced in this paper has even greater expressive capabilities. Keeping all the controls for the process of reasoning in time, this formalism allows one to more adequately represent facts about the subject area by using the language of first-order logic instead of the language of propositional logic. For the formalism of extended step theories of the first order, a declarative semantics is proposed, its paraneosistance is shown, and the expressive possibilities of this formalism in relation to the tasks of action planning are demonstrated using the example of the monkey and the banana.KeywordsActive logicTime reasoningExtended first-order step theoriesDeclarative semanticsParaconsistencyArgumentative semantics

Most of the theoretical work on the semantics of logic programs assumes an interpreter that provides a complete resolution procedure. In contrast, for reasons of efficiency, most logic programming languages are built around incomplete procedures. This difference is rooted in Prolog, which evaluates resolvent trees in a depth-first rather than a breadth-first order. The gap is widened by some equational logic languages, which combine the incompleteness of depth-first evaluation with incomplete approximations to equational unification. Because of this gap, it is unsound to reason about logic programs using their declarative semantics. This in turn makes it difficult to develop abstraction mechanisms that can be used to partition a logic program into independently specifiable modules.

The PPL framework is proposed as a simple extension to logic programming aiming at handling resources. It is argued that the separation between logical treatments and resource handling is desirable and, to that end, resources are proposed to be manipulated by means of pre- and post-conditions associated with usual Horn clauses. The expressiveness of the resulting framework is evidenced through the coding of several applications involving objects, databases, actions and changes. Operational and declarative semantics are presented as well. The operational semantics rests on a derivation relation stating how goals and conditions are evaluated. The declarative semantics extends the classical model and fixed-point theories to take into account the evaluation of pre- and post-conditions, and in particular the non-monotonic behavior of the world of resources they induce in general. As suggested, an effort has been made to keep the work close to the classical logic programming setting. In particular, the semantics are in the main streams of logic programming semantics. However, the PPL framework raises new problems for which fresh solutions are proposed.

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.

The problem of determining the correct declarative semantics for generalized logic programs is still open. In generalized logic programs the body of a rule may contain negated goals. Using a logical equivalence these programs may be viewed as disjunctive logic programs where the head of a rule may contain a disjunction of goals. We hope that a careful study of the declarative semantics of disjunctive logic programs will produce criteria for evaluating the different candidates for the semantics of generalized logic programs.A conceptual analysis leads to a semantical definition of the notion of a disjunctive deductive database as a generalization of the notion of a deductive database. It will be shown that the notion of a disjunctive deductive database is equivalent to the syntactical definition of a disjunctive logic program. We have characterized disjunctive deductive databases, i. e. theories which admit in each irreducible component a minimal Herbrand model and for which this property is preserved under the addition of new facts, as disjunctive logic programs. As a special case the result yields the known characterization of deductive databases, i. e. theories which admit a minimal Herbrand model and for which this property is preserved under the addition of new facts, as logic programs. In the presence of equations term structures i. e. extended Herbrand structures replace the Herbrand structures and h-core models replace the minimal models. Actually, the results could be proved in a more general context where pseudo term structures replace the term structures. In addition, there is an intermediate case where the irreducible components coincide with the connected components. Moreover, there are characterization results for the cases where the uniformity condition is not present.KeywordsLogic ProgramIrreducible ComponentTerm StructureCanonical ModelDeductive DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A Practically Implementable and Tractable Delegation Logic

Business Rules Management: Implementation and Evaluation

We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We define a fixpoint theory, declarative semantics, and proof procedure for the new class of probabilistic logic programs. Compared to other approaches to quantitative logic programming, we provide a true probabilistic framework with potential applications in probabilistic expert systems and decision support systems. We also discuss the relationship between such programs and Bayesian networks, thus moving toward a unification of two major approaches to automated reasoning.

On the semantics of μ Log

In this article, we propose to adopt the SCIFF abductive logic language to specify business contracts, and show how its proof procedures are useful to verify contract execution and fulfilment. SCIFF is a declarative language based on abductive logic programming, which accommodates forward rules, predicate definitions, and constraints over finite domain variables. Its declarative semantics is abductive, and can be related to that of deontic operators; its operational specification is the sound and complete SCIFF proof procedure, defined as a set of transition rules, which has been implemented and integrated into a reasoning and verification tool. A variation of the SCIFF proof-procedure (g-SCIFF) can be used for static verification of contract properties. We demonstrate the use of the SCIFF language for business contract specification and verification, in a concrete scenario. In order to accommodate integration of SCIFF with architectures for business contract, we also propose an encoding of SCIFF contract rules in RuleML.