Knowledge-Based Models of Nonlinear Systems Based on Inductive Learning

2015 ◽  
pp. 85-104 ◽  
Author(s):  
Nataliya N. Bakhtadze ◽  
Vladimir A. Lototsky
Keyword(s):  
Nonlinear Systems ◽  
Inductive Learning ◽  
Knowledge Based

A concept of knowledge-based identification of nonlinear systems

1999 ◽  
Vol 39 (3) ◽  
pp. 191-197 ◽  
Author(s):  
F.F. Pashchenko ◽  
K.R. Chernyshov
Keyword(s):  
Nonlinear Systems ◽  
Knowledge Based

scholarly journals Constructing design methodologies using multiagent systems

2004 ◽  
Vol 18 (2) ◽  
pp. 115-134 ◽  
Author(s):  
CIRRUS SHAKERI ◽  
DAVID C. BROWN
Keyword(s):  
Multiagent System ◽  
Inductive Learning ◽  
Design Teams ◽  
Specialized Knowledge ◽  
Future Design ◽  
Design Methodologies ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Design Projects ◽  
Use Of Knowledge

An innovative approach has been developed for discovering better design methodologies that is based on simulating the design process using a multiagent system that mimics the behavior of a design team. The system implements a knowledge-based model of design in which highly specialized knowledge from expert sources is applied to synthesize a design. The agents activate the pieces of design knowledge when they become applicable. The use of knowledge by agents is recorded by tracing the steps that the agents have taken during a design project. Many traces are generated by solving a large number of design projects that differ in their requirements. A set of design methodologies is constructed by using inductive learning techniques to generalize the traces generated. These methodologies then can be used to guide human design teams through future design projects.

scholarly journals Integrated Commonsense Reasoning and Deep Learning for Transparent Decision Making in Robotics

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Tiago Mota ◽  
Mohan Sridharan ◽  
Aleš Leonardis
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Domain Knowledge ◽  
Inductive Learning ◽  
Commonsense Reasoning ◽  
Knowledge Based ◽  
New Information ◽  
Robot Systems ◽  
Simulated Images ◽  
Relevant Domain

AbstractA robot’s ability to provide explanatory descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing the desired transparency in decision making is challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning methods. As a step towards addressing this challenge, our architecture combines the complementary strengths of non-monotonic logical reasoning with incomplete commonsense domain knowledge, deep learning, and inductive learning. During reasoning and learning, the architecture enables a robot to provide on-demand explanations of its decisions, the evolution of associated beliefs, and the outcomes of hypothetical actions, in the form of relational descriptions of relevant domain objects, attributes, and actions. The architecture’s capabilities are illustrated and evaluated in the context of scene understanding tasks and planning tasks performed using simulated images and images from a physical robot manipulating tabletop objects. Experimental results indicate the ability to reliably acquire and merge new information about the domain in the form of constraints, preconditions, and effects of actions, and to provide accurate explanations in the presence of noisy sensing and actuation.

Intelligent Computation for Manufacturing

2015 ◽  
pp. 1748-1782
Author(s):  
Ashraf Afify
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Inductive Learning ◽  
Knowledge Based Systems ◽  
Computation Techniques ◽  
Practical Applications ◽  
Knowledge Based

Intelligent computation refers to intelligence artificially realised through computation. This chapter reviews six intelligent computation techniques. They are: knowledge-based systems, fuzzy logic, inductive learning, neural networks, genetic algorithms, and swarm intelligent techniques. All of these tools have found many practical applications. Examples of applications in manufacturing are given in the chapter.

Ontology-Based Optimization in Search Engine

2013 ◽  
pp. 285-301
Author(s):  
Leonardo Balduzzi ◽  
Ignacio Cuesta
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Search Engine ◽  
Search Engines ◽  
Inductive Learning ◽  
System Evolution ◽  
Knowledge Based System ◽  
Learning Capacity ◽  
Knowledge Based ◽  
Learning Techniques

The major aim of the chapter is to propose and study the use of ontology-based optimization for positioning websites in search engines. In this sense, using heterogeneous inductive learning techniques and ontology for knowledge representation, a knowledge-based system which is capable of supporting the activity of SEO (Search Engine Optimization) has been designed and implemented. From its knowledge base, the system suggests the most appropriate optimization tasks for positioning a pair (keyword, website) on the first page of search engines and infers the positioning results to be obtained. The system evolution and learning capacity allows optimizing the productivity and effectiveness of the SEO process.

VISUALIZING LEARNING AND COMPUTATION IN ARTIFICIAL NEURAL NETWORKS

1992 ◽  
Vol 01 (03) ◽  
pp. 399-425 ◽  
Author(s):  
MARK W. CRAVEN ◽  
JUDE W. SHAVLIK
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Inductive Learning ◽  
Numerical Data ◽  
Initial Weight ◽  
General Technique ◽  
Knowledge Based ◽  
Decision Making Processes ◽  
Artificial Neural ◽  
Visualization Techniques

Scientific visualization is the process of using graphical images to form succinct and lucid representations of numerical data. Visualization has proven to be a useful method for understanding both learning and computation in artificial neural networks. While providing a powerful and general technique for inductive learning, artificial neural networks are difficult to comprehend because they form representations that are encoded by a large number of real-valued parameters. By viewing these parameters pictorially, a better understanding can be gained of how a network maps inputs into outputs. In this article, we survey a number of visualization techniques for understanding the learning and decision-making processes of neural networks. We also describe our work in knowledge-based neural networks and the visualization techniques we have used to understand these networks. In a knowledge-based neural network, the topology and initial weight values of the network are determined by an approximately-correct set of inference rules. Knowledge-based networks are easier to interpret than conventional networks because of the synergy between visualization methods and the relation of the networks to symbolic rules.

Inductive Learning Methods for Knowledge-Based Decision Support: A Comparative Analysis

1990 ◽  
Vol 3 (2) ◽  
pp. 147-165 ◽  
Author(s):  
Michael J. Shaw ◽  
James A. Gentry ◽  
Selwyn Piramuthu
Keyword(s):  
Decision Support ◽  
Comparative Analysis ◽  
Inductive Learning ◽  
Learning Methods ◽  
Knowledge Based

Intelligent Computation for Manufacturing

2013 ◽  
pp. 211-246 ◽  
Author(s):  
Ashraf Afify
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Inductive Learning ◽  
Knowledge Based Systems ◽  
Computation Techniques ◽  
Practical Applications ◽  
Knowledge Based

Intelligent computation refers to intelligence artificially realised through computation. This chapter reviews six intelligent computation techniques. They are: knowledge-based systems, fuzzy logic, inductive learning, neural networks, genetic algorithms, and swarm intelligent techniques. All of these tools have found many practical applications. Examples of applications in manufacturing are given in the chapter.

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