scholarly journals New Polynomial Classes for Logic-Based Abduction

2003 ◽  
Vol 19 ◽  
pp. 1-10 ◽  
Author(s):  
B. Zanuttini
Keyword(s):  
Knowledge Base ◽  
Propositional Logic ◽  
Propositional Knowledge ◽  
General Algorithm ◽  
Propositional Knowledge Base

We address the problem of propositional logic-based abduction, i.e., the problem of searching for a best explanation for a given propositional observation according to a given propositional knowledge base. We give a general algorithm, based on the notion of projection; then we study restrictions over the representations of the knowledge base and of the query, and find new polynomial classes of abduction problems.

A practical propositional knowledge base revision algorithm

1997 ◽  
Vol 12 (2) ◽  
pp. 154-159 ◽  
Author(s):  
Xuehong Tao ◽  
Wei Sun ◽  
Shaohan Ma
Keyword(s):  
Knowledge Base ◽  
Propositional Knowledge ◽  
Base Revision ◽  
Propositional Knowledge Base

Propositional knowledge base revision and minimal change

1991 ◽  
Vol 52 (3) ◽  
pp. 263-294 ◽  
Author(s):  
Hirofumi Katsuno ◽  
Alberto O. Mendelzon
Keyword(s):  
Knowledge Base ◽  
Minimal Change ◽  
Propositional Knowledge ◽  
Base Revision ◽  
Propositional Knowledge Base

scholarly journals Logical derivation search with assumption traceability

2021 ◽  
Vol 62 ◽  
pp. 16-22
Author(s):  
Adomas Birštunas ◽  
Elena Reivytytė
Keyword(s):  
Knowledge Base ◽  
Propositional Logic ◽  
Logical Derivation ◽  
Formula Derivation ◽  
Derivation Procedure

In this paper authors research the problem of traceability of assumptions in logical derivation. The essence of this task is to trace which assumptions from the available knowledge base of assumptions are necessary to derive a certain conclusion. The paper presents a new derivation procedure for propositional logic, which ensures traceability feature. For the derivable conclusion formula derivation procedure also returns the smallest set of assumptions those are enough to get derivation of the conclusion formula. Verification of the procedure were performed using authors implementation.

KNOWLEDGE BASE REFORMATION: PREPARING FIRST-ORDER THEORIES FOR EFFICIENT PROPOSITIONAL REASONING

2000 ◽  
Vol 14 (01) ◽  
pp. 35-57 ◽  
Author(s):  
HELMUT PRENDINGER ◽  
MITSURU ISHIZUKA ◽  
GERHARD SCHURZ
Keyword(s):  
Knowledge Base ◽  
Propositional Logic ◽  
Knowledge Compilation ◽  
First Order ◽  
Order Language

We present an approach to knowledge compilation that transforms a function-free first-order Horn knowledge base to propositional logic. This form of compilation is important since the most efficient reasoning methods are defined for propositional logic, while knowledge is most conveniently expressed within a first-order language. To obtain compact propositional representations, we employ techniques from (ir)relevance reasoning as well as theory transformation via unfold/fold transformations. Application areas include diagnosis, planning, and vision. Preliminary experiments with a hypothetical reasoner indicate that our method may yield significant speed-ups.

scholarly journals General information spaces: measuring inconsistency, rationality postulates, and complexity

2021 ◽  
Author(s):  
John Grant ◽  
Francesco Parisi
Keyword(s):  
Knowledge Base ◽  
Knowledge Bases ◽  
General Information ◽  
Propositional Knowledge ◽  
Information Space ◽  
Temporal Database ◽  
Information Spaces ◽  
Spatio Temporal ◽  
World Information ◽  
Rationality Postulates

AbstractAI systems often need to deal with inconsistent information. For this reason, since the early 2000s, some AI researchers have developed ways to measure the amount of inconsistency in a knowledge base. By now there is a substantial amount of research about various aspects of inconsistency measuring. The problem is that most of this work applies only to knowledge bases formulated as sets of formulas in propositional logic. Hence this work is not really applicable to the way that information is actually stored. The purpose of this paper is to extend inconsistency measuring to real world information. We first define the concept ofgeneral information spacewhich encompasses various types of databases and scenarios in AI systems. Then, we show how to transform any general information space to aninconsistency equivalentpropositional knowledge base, and finally apply propositional inconsistency measures to find the inconsistency of the general information space. Our method allows for the direct comparison of the inconsistency of different information spaces, even though the data is presented in different ways. We demonstrate the transformation on four general information spaces: a relational database, a graph database, a spatio-temporal database, and a Blocks world scenario, where we apply several inconsistency measures after performing the transformation. Then we review so-called rationality postulates that have been developed for propositional knowledge bases as a way to judge the intuitive properties of these measures. We show that although general information spaces may be nonmonotonic, there is a way to transform the postulates so they can be applied to general information spaces and we show which of the measures satisfy which of the postulates. Finally, we discuss the complexity of inconsistency measures for general information spaces.

Knowledge-Based Description Logic ALCO Knowledge Compiled Algorithm to Determine Consistency

2012 ◽  
Vol 487 ◽  
pp. 347-351
Author(s):  
Ya Qiong Jiang ◽  
Jun Wang
Keyword(s):  
Normal Form ◽  
Knowledge Base ◽  
Propositional Logic ◽  
Description Logic ◽  
Knowledge Bases ◽  
Knowledge Compilation ◽  
Knowledge Based ◽  
Common Technique ◽  
Compilation Techniques

Knowledge compilation is a common technique for propositional logic knowledge bases. A given knowledge base is transformed into a normal form, for which reasoning can be answered efficiently. The precompilation of description logic knowledge base is important for reasoning and services of description logic. This paper gives precompilation about the description logic ALCO TBox based on knowledge compilation techniques, for which the consistency of TBox can be determined.

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