摘要:

A method for finding all deterministic and maximally general rules for a target classification is explained in detail and illustrated with examples. Maximally general rules are rules with minimal numbers of conditions. The method has been developed within the context of the rough sets model and is based on the concepts of a decision matrix and a decision function. The problem of finding all the rules is reduced to the problem of computing prime implicants of a group of associated Boolean expressions. The method is particularly applicable to identifying all potentially interesting deterministic rules in a knowledge discovery system but can also be used to produce possible rules or nondeterministic rules with decision probabilities, by adapting the method to the definitions of the variable precision rough sets model.

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00260.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY

摘要:

This article introduces a temporal deductive database system featuring a logic programming language and an algebraic front‐end. The language, called Temporal DATALOG, is an extension of DATALOG based on a linear‐time temporal logic in which the flow of time is modeled by the set of natural numbers. Programs of Temporal DATALOG are considered as temporal deductive databases, specifying temporal relationships among data and providing base relations to the algebraic front‐end. The minimum model of a given Temporal DATALOG program is regarded as the temporal database the program models intensionally. The algebraic front‐end, called TRA, is a point‐wise extension of the relational algebra upon the set of natural numbers. When needed during the evaluation of TRA expressions, slices of temporal relations over intervals can be retrieved from a given temporal deductive database by bottom‐up evaluation strategies.A modular extension of Temporal DATALOG is also proposed, through which temporal relations created during the evaluation of TRA expressions may be fed back to the deductive part for further manipulation. Modules therefore enable the algebra to have full access to the deductive capabilities of Temporal DATALOG and to extend it with nonstandard algebraic operators. This article also shows that the temporal operators of TRA can be simulated in Temporal DATALOG by prog

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00261.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY

摘要:

Monotonicity and concavity play important roles in human cognition, reasoning, and decision making. This paper shows that neural networks can learn monotonic‐concave interval concepts based on real‐world data, Traditionally, the training of neural networks has been based only on raw data. In cases where the training samples carry statistical fluctuations, the products of the training have often suffered. This paper suggests that global knowledge about monotonicity and concavity of a problem domain can be incorporated in neural network training. This paper proposes a learning scheme for the back‐propagation layered neural networks in learning monotonic‐concave interval concepts and provides an example to show its appl

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00262.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY

摘要:

Sentence syntax is the basis for organizing semantic relations in TANKA, a project that aims to acquire knowledge from technical text. Other hallmarks include an absence of precoded domain‐specific knowledge; significant use of public‐domain generic linguistic information sources; involvement of the user as a judge and source of expertise; and learning from the meaning representations produced during processing. These elements shape the realization of the TANKA project: implementing a trainable text processing system to propose correct semantic interpretations to the user. A three‐level model of sentence semantics, including a comprehensive Case system, provides the framework for TANKA's representations. Text is first processed by the DIPETT parser, which can handle a wide variety of unedited sentences. The semantic analysis module HAIKU then semi‐automatically extracts semantic patterns from the parse trees and composes them into domain knowledge representations. HAIKU's dictionaries and main algorithm are described with the aid of examples and traces of user interaction. Encouraging experimental results are described and ev

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00263.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY

摘要:

In this paper, we describe the implementation and evaluation of the AbTweakplanning system, a test bed for studying and teaching concepts in partial‐order planning, abstraction, and search control. We start by extending the hierarchical, precondition‐elimination abstraction of ABSTRIPS to partial‐order‐based, least‐commitment planners such as Tweak. The resulting system, AbTweak, illustrates the advantages of using abstraction to improve the efficiency of search. We show that by protecting a subset of abstract conditions achieved so far, and by imposing a bias on search toward deeper levels in a hierarchy, planning efficiency can be greatly improved. Finally, we relate AbTweakto other planning systems SNLP, ALPINE, and SIPE by exploring their similarities and di

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00264.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY

摘要:

Allen's interval algebra has been shown to be useful for representing plans. We present a strengthened algorithm for temporal reasoning about plans, which improves on straightforward applications of the existing reasoning algorithms for the algebra. This is made possible by viewing plans as both temporal networks and hierarchical structures. The temporal network view allows us to check for inconsistencies as well as propagate the effects of new temporal constraints, whereas the hierarchical view helps us to get the strengthened results by taking into account the dependency relationships between actions.We further apply our algorithm to the process of plan recognition through the analysis of natural language input. We show that such an application has two useful effects: the temporal relations derived from the natural language input can be used as constraints to reduce the number of candidate plans, and the derived constraints can be made more specific by combining them with the prestored constraints in the plans being recognized.

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1996.tb00265.x

出版商:Blackwell Publishing Ltd    

年代:1996

数据来源: WILEY