An efficient hypothetical reasoning system for predicate-logic knowledge-base

2002 ◽  
Author(s):  
A. Kondo ◽  
T. Makino ◽  
M. Ishizuka
Keyword(s):  
Knowledge Base ◽  
Predicate Logic ◽  
Hypothetical Reasoning ◽  
Reasoning System

A Speedup Algorithm for Repetition of Hypothetical Reasoning

2006 ◽  
Vol 10 (2) ◽  
pp. 207-218
Author(s):  
Haruhiko Kimura ◽  
◽  
Tadanobu Misawa ◽  
Koji Abe ◽  
Yasuhiro Ogoshi ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Predicate Logic ◽  
Background Knowledge ◽  
Performance Evaluations ◽  
Inclusion Relation ◽  
Hypothetical Reasoning ◽  
First Order ◽  
Recursive Structures ◽  
First Order Predicate Logic

This paper presents performance evaluations of a speedup method for hypothetical reasoning to a first-order predicate logic knowledge base in the case when reasoning is executed repeatedly replacing hypothetical knowledge and keeping background knowledge and the goal intact. The proposed method consists of substituting hypotheses for predicate knowledge which has recursive structures, deriving bit patterns of solutions from background knowledge and the goal in advance when all the hypotheses are true, and finding actual solutions from inclusion relation with bit patterns of hypothetical knowledge.

scholarly journals Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Shaobo Wang ◽  
Pan Zhao ◽  
Biao Yu ◽  
Weixin Huang ◽  
Huawei Liang
Keyword(s):  
Neural Network ◽  
Knowledge Base ◽  
Prior Knowledge ◽  
Prediction Models ◽  
Short Term Memory ◽  
A Priori ◽  
Urban Environments ◽  
Intelligent Vehicles ◽  
Trajectory Prediction ◽  
Reasoning System

An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.

Legal Reasoning Using Abductive Logic Programming

1997 ◽  
Vol 1 (2) ◽  
pp. 114-120
Author(s):  
Takashi Kanai ◽  
◽  
Susumu Kunifuji
Keyword(s):  
Logic Programming ◽  
Knowledge Base ◽  
Legal Reasoning ◽  
The Other ◽  
Abductive Logic ◽  
Reasoning System ◽  
Abductive Logic Programming ◽  
Reasoning Systems ◽  
Legal Arguments

In this paper, we propose a new legal reasoning system using abductive logic programming (ALP). The system can deal with ambiguities of described facts and exceptions which is not described in relevant articles. In addition, the goal, queried to a legal reasoning system, differs in compliance with whether the user is a plaintiff or defendant. In usual deductive legal reasoning systems, there are two major problems in treating legal arguments. One is that legal facts usually have ambiguities, and the other is that two conflicting conclusions must be derived from one knowledge base, depending on whether a plaintiff of defendant is involved. To overcome these difficulties, abductive logic programming is used in our legal reasoning system, which can deal with implicit exceptions and generate presumptions according to the user’s needs.

Research on Consistency Checking of OntoUML Model

2012 ◽  
Vol 433-440 ◽  
pp. 2862-2867
Author(s):  
Bin Yang ◽  
Yu Dong Qi ◽  
Xiu We Wang ◽  
Ya Ning Wang
Keyword(s):  
Knowledge Base ◽  
Conceptual Model ◽  
Description Logics ◽  
Conceptual Modeling ◽  
Modeling Language ◽  
Consistency Checking ◽  
Model Based ◽  
Reasoning System ◽  
Uml Metamodel

OntoUML is a conceptual modeling language which is built with a lightweight expansion of UML metamodel, but it doesn’t provide mechanism of consistency checking on conceptual model. The correctness of the syntax and semantics of the model still need artificial check. The paper introduced OntoUML briefly and put forward a scheme of consistency checking of OntoUML model based on description logics. By transforming into knowledge base of description logics, the detection of consistencies at the OntoUML model can be realized using existing mature reasoning system.

Telederm

2010 ◽  
pp. 154-176
Author(s):  
Geoff West ◽  
Mihai Lazarescu ◽  
Monica Ou
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
General Practitioners ◽  
Large Scale ◽  
Case Based Reasoning ◽  
Web Based ◽  
Query Process ◽  
Reasoning System ◽  
Knowledge Validation ◽  
Case Based

In this chapter we describe a web-based decision support system called Telederm that has been developed with the aim of helping general practitioners diagnose skin ailments from a knowledge base while allowing incremental updates of the knowledge base as cases occur. We outline the two major challenges in developing the Telederm system: developing a general practitioner query process that is easily accessible and building knowledge validation in a case-based reasoning system. We provide a detailed description of our approaches to address these problems which involve the use of artificial intelligence classification and reasoning techniques. The system was deployed in a large scale trial in the Eastern Goldfields of Western Australia and we present the results and feedback obtained from an evaluation by the general practitioners involved.

Application Research on Expert System of Incremental In-Plane Bending

2008 ◽  
Vol 575-578 ◽  
pp. 600-605
Author(s):  
Wen Ting Tang ◽  
Chao Li Tang ◽  
Lei Huang ◽  
He Yang
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Visual Basic ◽  
Application Research ◽  
Client Server ◽  
Reasoning System ◽  
Search Mechanism ◽  
Bending Radius ◽  
Plane Bending ◽  
And Control

Incremental in-plane bending is a flexible and laborsaving manufacturing technology for short production runs in a variety of sizes and shapes. But the technology parameters are interactive intimately and it is hard to forecast and control the bending radius accurately. Based on the features of incremental in-plane bending and the advantage of expert system dealing with problems, an expert system of incremental in-plane bending by designing knowledge base of representation of creation regulation, reasoning system based on rules and search mechanism and explaining system based on prefabricating text has been researched and developed in this paper. The system, making use of ADO technology, Access database and Visual Basic, develops knowledge base, database and reasoning system. Based on client / server model, the system realizes a seamless link between Visual Basic and Matlab by using ActiveX. The system can perform functions of bending radius forecasting and forming program evaluating and optimizing. It has a friendly interface and it is easy to operate and convenient for maintenance.

A fuzzy logic system for expert systems

1988 ◽  
Vol 2 (3) ◽  
pp. 183-193 ◽  
Author(s):  
Te-Chuan Chang ◽  
C. William Ibbs ◽  
Keith C. Crandall
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Knowledge Base ◽  
Fuzzy Logic System ◽  
Future Research ◽  
Rule Based ◽  
Reasoning System ◽  
Fuzzy Production Rules ◽  
Future Research Directions ◽  
Logic System

Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.

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