Nonmonotonic reasoning is a logical inference system which attempts to approximate human commonsense reasoning and is characterized as defeasible: having reasonably drawn a conclusion from some premises we may be forced to retract that conclusion upon learning new facts. This paper introduces a Petri net model for nonmonotonic reasoning with nonmonotonic rules generated by annotated logic programs and the unless operator. In the Petri net model, a fixpoint of a nonmonotonic theory can be represented as a maximal and consistent support of a firing sequence. We propose a structural method for finding extensions (coherent consequences) for a given set of nonmonotonic logic rules. It is based on the T-invariant technique for testing fireability of a goal transition in the Petri net model of Horn clause logic programs.

We present outside-in conditional narrowing for orthogonal conditional term rewriting systems, and show the completeness of leftmost-outside-in conditional narrowing with respect to normalizable solutions. We consider orthogonal conditional term rewriting systems whose conditions consist of strict equality only. Completeness results are obtained for systems both with and without extra variables. The result bears practical significance since orthogonal conditional term rewriting systems can be viewed as a computation model for functional-logic programming languages and leftmost-outside-in conditional narrowing is the computing mechanism for the model.

This paper introduces a constraint logic programming (CLP) language cu-Prolog as an implementation framework for constraint-based natural language processing. Compared to other CLP languages, cu-Prolog has several unique features. Most CLP languages take algebraic equations or inequations as constraints. cu-Prolog, on the other hand, takes Prolog atomic formulas in terms of user-defined predicates. cu-Prolog, thus, can describe symbolic and combinatorial constraints occurring in the constraint-based grammar formalisms. As a constraint solver, cu-Prolog uses the unfold/fold transformation, which is well known as a program transformation technique, dynamically with some heuristics. To treat the information partiality described with feature structures, cu-Prolog uses PST (Partially Specified Term) as its data structure. Sections 1 and 2 give an introduction to the constraint-based grammar formalisms on which this paper is based and the outline of cu-Prolog is explained in Sect. 3 with implementation issues described in Sect. 4. Section 5 illustrates its linguistic application to disjunctive feature structure (DFS) and parsing constraint-based grammar formalisms such as Japanese Phrase Structure Grammar (JPSG). In either application, a disambiguation process is realized by transforming constraints, which gives a picture of constraint-based NLP.

This paper describes the derivation of a parallel program from a nondeterministic sequential program using a bottom-up parser as an example. The derivation procedure consists of two stages: exploitation of AND-parallelism and exploitation of OR-parallelism. An interpreter of the sequential parser BUP is first transformed so that processes for the nodes in a parsing tree can run in parallel. Then, the resultant program is transformed so that a nondeterministic search of a parsing tree can be done in parallel. The former stage is performed by hand-simulation, and the latter is accomplished by the compiler of ANDOR-, which is an AND/OR parallel logic programming language. The program finally derived, written in KL1 (Kernel Language of the FGCS Project), achieves an all-solution search without side effects. The program generated corresponds to an interpreter of PAX, a revised parallel version of BUP. This correspondence shows that the derivation method proposed in this paper is effective for creating efficient parallel programs.

This paper presents a system that automatically refines the theory expressed in the function-free first-order logic. Our system can efficiently correct multiple faults in both the concept and subconcepts of the theory, given only the classified examples of the concept. It can refine larger classes of theory than existing systems can since it has overcome many of their limitations. Our system is based on a new combination of an inductive and an explanation-based learning algorithms, which we call the biggest-first multiple-example EBL (BM-EBL). From a learning perspective, our system is an improvement over the FOIL learning system in that our system can accept a theory as well as examples. An experiment shows that when our system is given a theory that has the classification error rate as high as 50%, it can still learn faster and with more accuracy than when it is not given any theory.

The meaning of analogical reasoning in locally stratified logic programs are described by generalized stable model (GSM) semantics. Although studies on the theoretical aspects of analogical reasoning have recently been on the increase, there have been few attempts to give declarative semantics for analogical reasoning. This paper takes notice of the fact that GSM semantics gives meaning to the effect that the negated predicates represent exceptional cases. We define predicates that denote unusual cases regarding analogical reasoning; for example, ab(x)p(x)g(x), where p(s), q(s), p(t) are given. We also add rules with negated occurrences of such predicates into the original program. In this way, analogical models for original programs are given in the form of GSMs of extended programs. A proof procedure for this semantics is presented. The main objective of this paper is not to construct a practical analogical reasoning system, but rather to present a framework for analyzing characteristics of analogical reasoning.