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Marc Denecker

Publications and source records attributed to Marc Denecker.

53 records · Page 3Linked to original sources

Reiter's Default Logic Is a Logic of Autoepistemic Reasoning And a Good One, Too

A fact apparently not observed earlier in the literature of nonmonotonic reasoning is that Reiter, in his default logic paper, did not directly formalize informal defaults. Instead, he translated a default into a certain natural language proposition and provided a formalization of the latter. A few years later, Moore noted that propositions like the one used by Reiter are fundamentally different than defaults and exhibit a certain autoepistemic nature. Thus, Reiter had developed his default logic as a formalization of autoepistemic propositions rather than of defaults. The first goal of this paper is to show that some problems of Reiter's default logic as a formal way to reason about informal defaults are directly attributable to the autoepistemic nature of default logic and to the mismatch between informal defaults and the Reiter's formal defaults, the latter being a formal expression of the autoepistemic propositions Reiter used as a representation of informal defaults. The second goal of our paper is to compare the work of Reiter and Moore. While each of them attempted to formalize autoepistemic propositions, the modes of reasoning in their respective logics were different. We revisit Moore's and Reiter's intuitions and present them from the perspective of autotheoremhood, where theories can include propositions referring to the theory's own theorems. We then discuss the formalization of this perspective in the logics of Moore and Reiter, respectively, using the unifying semantic framework for default and autoepistemic logics that we developed earlier. We argue that Reiter's default logic is a better formalization of Moore's intuitions about autoepistemic propositions than Moore's own autoepistemic logic.

cs.AI↗

Constraint Propagation for First-Order Logic and Inductive Definitions

Constraint propagation is one of the basic forms of inference in many logic-based reasoning systems. In this paper, we investigate constraint propagation for first-order logic (FO), a suitable language to express a wide variety of constraints. We present an algorithm with polynomial-time data complexity for constraint propagation in the context of an FO theory and a finite structure. We show that constraint propagation in this manner can be represented by a datalog program and that the algorithm can be executed symbolically, i.e., independently of a structure. Next, we extend the algorithm to FO(ID), the extension of FO with inductive definitions. Finally, we discuss several applications.

cs.LO↗

FO(FD): Extending classical logic with rule-based fixpoint definitions

We introduce fixpoint definitions, a rule-based reformulation of fixpoint constructs. The logic FO(FD), an extension of classical logic with fixpoint definitions, is defined. We illustrate the relation between FO(FD) and FO(ID), which is developed as an integration of two knowledge representation paradigms. The satisfiability problem for FO(FD) is investigated by first reducing FO(FD) to difference logic and then using solvers for difference logic. These reductions are evaluated in the computation of models for FO(FD) theories representing fairness conditions and we provide potential applications of FO(FD).

cs.LO↗

CP-logic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming

This papers develops a logical language for representing probabilistic causal laws. Our interest in such a language is twofold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, our work extends Shafer's by offering a convenient logical representation for his semantical objects. Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages such as ICL or PRISM. Because we can motivate and explain our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they can express probabilistic causal laws.

cs.AI↗

Splitting an operator: Algebraic modularity results for logics with fixpoint semantics

It is well known that, under certain conditions, it is possible to split logic programs under stable model semantics, i.e. to divide such a program into a number of different "levels", such that the models of the entire program can be constructed by incrementally constructing models for each level. Similar results exist for other non-monotonic formalisms, such as auto-epistemic logic and default logic. In this work, we present a general, algebraicsplitting theory for logics with a fixpoint semantics. Together with the framework of approximation theory, a general fixpoint theory for arbitrary operators, this gives us a uniform and powerful way of deriving splitting results for each logic with a fixpoint semantics. We demonstrate the usefulness of these results, by generalizing existing results for logic programming, auto-epistemic logic and default logic.

cs.AI↗

Well-founded and Stable Semantics of Logic Programs with Aggregates

In this paper, we present a framework for the semantics and the computation of aggregates in the context of logic programming. In our study, an aggregate can be an arbitrary interpreted second order predicate or function. We define extensions of the Kripke-Kleene, the well-founded and the stable semantics for aggregate programs. The semantics is based on the concept of a three-valued immediate consequence operator of an aggregate program. Such an operator approximates the standard two-valued immediate consequence operator of the program, and induces a unique Kripke-Kleene model, a unique well-founded model and a collection of stable models. We study different ways of defining such operators and thus obtain a framework of semantics, offering different trade-offs between precision and tractability. In particular, we investigate conditions on the operator that guarantee that the computation of the three types of semantics remains on the same level as for logic programs without aggregates. Other results show that, in practice, even efficient three-valued immediate consequence operators which are very low in the precision hierarchy, still provide optimal precision.

cs.LO↗

A Logic for Non-Monotone Inductive Definitions

Well-known principles of induction include monotone induction and different sorts of non-monotone induction such as inflationary induction, induction over well-founded sets and iterated induction. In this work, we define a logic formalizing induction over well-founded sets and monotone and iterated induction. Just as the principle of positive induction has been formalized in FO(LFP), and the principle of inflationary induction has been formalized in FO(IFP), this paper formalizes the principle of iterated induction in a new logic for Non-Monotone Inductive Definitions (ID-logic). The semantics of the logic is strongly influenced by the well-founded semantics of logic programming. Our main result concerns the modularity properties of inductive definitions in ID-logic. Specifically, we formulate conditions under which a simultaneous definition $\D$ of several relations is logically equivalent to a conjunction of smaller definitions $\D_1 \land ... \land \D_n$ with disjoint sets of defined predicates. The difficulty of the result comes from the fact that predicates $P_i$ and $P_j$ defined in $\D_i$ and $\D_j$, respectively, may be mutually connected by simultaneous induction. Since logic programming and abductive logic programming under well-founded semantics are proper fragments of our logic, our modularity results are applicable there as well.

cs.AI↗

Repairing Inconsistent Databases: A Model-Theoretic Approach and Abductive Reasoning

In this paper we consider two points of views to the problem of coherent integration of distributed data. First we give a pure model-theoretic analysis of the possible ways to `repair' a database. We do so by characterizing the possibilities to `recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. Then we introduce an abductive application to restore the consistency of a given database. This application is based on an abductive solver (A-system) that implements an SLDNFA-resolution procedure, and computes a list of data-facts that should be inserted to the database or retracted from it in order to keep the database consistent. The two approaches for coherent data integration are related by soundness and completeness results.

cs.LO↗

Ultimate approximations in nonmonotonic knowledge representation systems

We study fixpoints of operators on lattices. To this end we introduce the notion of an approximation of an operator. We order approximations by means of a precision ordering. We show that each lattice operator O has a unique most precise or ultimate approximation. We demonstrate that fixpoints of this ultimate approximation provide useful insights into fixpoints of the operator O. We apply our theory to logic programming and introduce the ultimate Kripke-Kleene, well-founded and stable semantics. We show that the ultimate Kripke-Kleene and well-founded semantics are more precise then their standard counterparts We argue that ultimate semantics for logic programming have attractive epistemological properties and that, while in general they are computationally more complex than the standard semantics, for many classes of theories, their complexity is no worse.

cs.AI↗

Abductive reasoning with temporal information

Texts in natural language contain a lot of temporal information, both explicit and implicit. Verbs and temporal adjuncts carry most of the explicit information, but for a full understanding general world knowledge and default assumptions have to be taken into account. We will present a theory for describing the relation between, on the one hand, verbs, their tenses and adjuncts and, on the other, the eventualities and periods of time they represent and their relative temporal locations, while allowing interaction with general world knowledge. The theory is formulated in an extension of first order logic and is a practical implementation of the concepts described in Van Eynde 2001 and Schelkens et al. 2000. We will show how an abductive resolution procedure can be used on this representation to extract temporal information from texts. The theory presented here is an extension of that in Verdoolaege et al. 2000, adapted to VanEynde 2001, with a simplified and extended analysis of adjuncts and with more emphasis on how a model can be constructed.

cs.CL↗

Semantic interpretation of temporal information by abductive inference

Besides temporal information explicitly available in verbs and adjuncts, the temporal interpretation of a text also depends on general world knowledge and default assumptions. We will present a theory for describing the relation between, on the one hand, verbs, their tenses and adjuncts and, on the other, the eventualities and periods of time they represent and their relative temporal locations. The theory is formulated in logic and is a practical implementation of the concepts described in Ness Schelkens et al. We will show how an abductive resolution procedure can be used on this representation to extract temporal information from texts.

cs.CL↗

Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison

Many logic programming based approaches can be used to describe and solve combinatorial search problems. On the one hand there is constraint logic programming which computes a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are systems based on stable model semantics, abductive systems, and first order logic model generators which compute solutions as models of some theory. This paper compares these different approaches from the point of view of knowledge representation (how declarative are the programs) and from the point of view of performance (how good are they at solving typical problems).

cs.AI↗

Detecting Unsolvable Queries for Definite Logic Programs

In solving a query, the SLD proof procedure for definite programs sometimes searches an infinite space for a non existing solution. For example, querying a planner for an unreachable goal state. Such programs motivate the development of methods to prove the absence of a solution. Considering the definite program and the query ``<- Q'' as clauses of a first order theory, one can apply model generators which search for a finite interpretation in which the program clauses as well as the clause ``false <- Q'' are true. This paper develops a new approach which exploits the fact that all clauses are definite. It is based on a goal directed abductive search in the space of finite pre-interpretations for a pre-interpretation such that ``Q'' is false in the least model of the program based on it. Several methods for efficiently searching the space of pre-interpretations are presented. Experimental results confirm that our approach find solutions with less search than with the use of a first order model generator.

cs.LO↗

A note on the Declarative reading(s) of Logic Programming

This paper analyses the declarative readings of logic programming. Logic programming - and negation as failure - has no unique declarative reading. One common view is that logic programming is a logic for default reasoning, a sub-formalism of default logic or autoepistemic logic. In this view, negation as failure is a modal operator. In an alternative view, a logic program is interpreted as a definition. In this view, negation as failure is classical objective negation. From a commonsense point of view, there is definitely a difference between these views. Surprisingly though, both types of declarative readings lead to grosso modo the same model semantics. This note investigates the causes for this.

cs.LO↗

Problem solving in ID-logic with aggregates: some experiments

The goal of the LP+ project at the K.U.Leuven is to design an expressive logic, suitable for declarative knowledge representation, and to develop intelligent systems based on Logic Programming technology for solving computational problems using the declarative specifications. The ID-logic is an integration of typed classical logic and a definition logic. Different abductive solvers for this language are being developed. This paper is a report of the integration of high order aggregates into ID-logic and the consequences on the solver SLDNFA.

cs.AI↗

Extending Classical Logic with Inductive Definitions

The goal of this paper is to extend classical logic with a generalized notion of inductive definition supporting positive and negative induction, to investigate the properties of this logic, its relationships to other logics in the area of non-monotonic reasoning, logic programming and deductive databases, and to show its application for knowledge representation by giving a typology of definitional knowledge.

cs.LO↗

Uniform semantic treatment of default and autoepistemic logics

We revisit the issue of connections between two leading formalisms in nonmonotonic reasoning: autoepistemic logic and default logic. For each logic we develop a comprehensive semantic framework based on the notion of a belief pair. The set of all belief pairs together with the so called knowledge ordering forms a complete lattice. For each logic, we introduce several semantics by means of fixpoints of operators on the lattice of belief pairs. Our results elucidate an underlying isomorphism of the respective semantic constructions. In particular, we show that the interpretation of defaults as modal formulas proposed by Konolige allows us to represent all semantics for default logic in terms of the corresponding semantics for autoepistemic logic. Thus, our results conclusively establish that default logic can indeed be viewed as a fragment of autoepistemic logic. However, as we also demonstrate, the semantics of Moore and Reiter are given by different operators and occupy different locations in their corresponding families of semantics. This result explains the source of the longstanding difficulty to formally relate these two semantics. In the paper, we also discuss approximating skeptical reasoning with autoepistemic and default logics and establish constructive principles behind such approximations.

cs.AI↗