A First Order Logic Benchmark for Defeasible Reasoning Tool Profiling

Hecham, Abdelraouf; Croitoru, Madalina; Bisquert, Pierre

doi:10.1007/978-3-319-99906-7_6

Cited by 2 publications

(9 citation statements)

References 24 publications

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“…Works [12][13][14] are also devoted to the problems of presentation and processing of Semantic Web knowledge. In these works, cases of logical inference based on rules that may be impossible (defeasible rules), in particular, in the presence of conflicting information.…”

Section: © S Shapovalova 2020mentioning

confidence: 99%

“…Such cases are presented in formal form and in the rules of logical languages used by software tools for processing information of Internet resources and ontologies (Semantic Web, Ontology Based Data). In [12], a method of generating knowledge bases for testing defeasible reasoning tools was proposed. The following tools were tested: ASPIC +, DEFT, DeLP, Flora-2, SPINdle.…”

Section: © S Shapovalova 2020mentioning

confidence: 99%

“…In the conditional part of the rules of each subsequent level in the functor is added one: f<1>3(Y,3,X)), (12) where f<1>1, f<1>2, f<1>3 are names of samples, X, Yunrelated variables.…”

Section: The Way To Present the Rulesmentioning

confidence: 99%

“…The LHS part of the rule contains only one connection (12). The link that corresponds to the colour obtained later than the other (that is, with more units in the sample names) is represented.…”

Section: The Way To Present the Rulesmentioning

confidence: 99%

“…For example, you can leave the colour representation unchanged. It is enough to replace the symbol "f" (according to the formulas (11)(12)) in the samples and facts of the rules with another symbol or their sequence and turn the previously generated rules into new ones. Thus, you can multiply the number of rules while maintaining the logic of inference by forming new parts of the knowledge base as a reflection of previously formed.…”

Section: Increasing the Knowledge Basementioning

confidence: 99%

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Generation of Test Bases of Rules for the Analysis of Productivity of Logical Inference Engine

Шаповалова

2020

Innovative Technologies and Scientific Solutions for Industries

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The subject of research in the article are test tasks to determine the performance of logical inference engines based on rules. The purpose of the work is to create a method of forming a database of rules and a set of data for analyzing the performance of logical inference mechanisms according to the given characteristics of rule activation and the complexity of finding a way to the target conclusion. The article solves the following tasks: determining the requirements for the knowledge base to be formed; creation of a knowledge base model; creating a way to form rules; identifying ways to increase the number of rules; providing testing of logical inference mechanisms for the proposed test problem. The following methods are used: methods of comparison with the sample, graph theory, logical programming. The following results were obtained: the method provides opportunities: creation of conditions of rules that complicate the data flow network of the Rete-algorithm as much as possible; formation of test bases of rules for derivation both on logic of the first order, and on offer logic; simply increase the number of knowledge base rules while maintaining the output logic. The formation of the knowledge base is based on a graph that represents the metarules of mixing paints to obtain a new colour. The vertices of the graph are colour classes. Each metarule is either an edge leading to the OR vertex or a set of edges in the case of the AND vertex. Each metarule specifies a scheme for creating several rules, because its structural components are classes of paints. The given structure of the graph significantly complicates the logical inference, because to prove the truth of the conclusion on ANDvertices it is necessary to have the conclusions obtained in the previous steps of different search directions. Examples of rule formation are given. Target vertices are defined, which determine the simplest and most complex cases of logical inference. Conclusions: it was proposed a semantic model of the knowledge base in the form of AND/OR-graph, which allows you to test the effectiveness of the implementation of conflict resolution strategies, as well as heuristic algorithms; a method of creating tests for inference mechanisms, which allows you to generate a database of rules and a set of data of certain sizes, as well as to model the complexity of finding the target output and activating the rules. Ways to increase the number of rules of the knowledge base to complicate the problem of logical inference have been presented; formulation of tests to determine the performance of logic output mechanisms for the proposed test problem has been done.

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Section: © S Shapovalova 2020mentioning

confidence: 99%

Section: © S Shapovalova 2020mentioning

confidence: 99%

“…In the conditional part of the rules of each subsequent level in the functor is added one: f<1>3(Y,3,X)), (12) where f<1>1, f<1>2, f<1>3 are names of samples, X, Yunrelated variables.…”

Section: The Way To Present the Rulesmentioning

confidence: 99%

Section: The Way To Present the Rulesmentioning

confidence: 99%

Section: Increasing the Knowledge Basementioning

confidence: 99%

See 3 more Smart Citations

Generation of Test Bases of Rules for the Analysis of Productivity of Logical Inference Engine

Шаповалова

2020

Innovative Technologies and Scientific Solutions for Industries

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Technologies of Inference in Software Systems

2022

mpom23

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The target of research is the implementation technology of inference. The purpose of the article is to determine the main areas of research on automatic inference, software obtained on their basis, and relevant areas of application.Inference is a common task that is implemented in application software. The efficiency of the software systems, in particular, is determined by the performance of the built-in inference engine. Means of implementing inference shall provide optimal execution time and interaction with other components, as well as meet the requirements of the application task. The article highlights the main areas of improving the software implementation of inference engine and relevant areas of research: (i) expanding the concept and combining several paradigms of logic programming (probabilistic logic programming, defeasible inference, coinductive programming); (ii) reduction of data exchange time between software components and processing time of large knowledge bases (improving pattern matching algorithm for implementing rule-based systems, creating or expanding implementations of inference tools for integration into distributed software systems, developing new implementations of inference engines for specialized programming languages and general-purpose languages); (iii) a combination of conceptually different approaches to inference (a combination of both approaches to inference on logical and productive concepts, the integration of inference paradigms and neural network approach). The combination of software implementations of different concepts, first of all, inference engines and neural network models, gives new opportunities to artificial intelligence, For each area of research, there are presented software tools, as well as examples of areas of their application in accordance with this concept.

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A First Order Logic Benchmark for Defeasible Reasoning Tool Profiling

Cited by 2 publications

References 24 publications

Generation of Test Bases of Rules for the Analysis of Productivity of Logical Inference Engine

Generation of Test Bases of Rules for the Analysis of Productivity of Logical Inference Engine

Technologies of Inference in Software Systems

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