Model of Representation and Acquisition of New Knowledge by an Autonomous Intelligent Robot Based on the Logic of Conditionally Dependent Predicates

The model for the representation of declarative and procedural knowledge of an autonomous intelligent robot is developed without reference to a specific subject area. The logic of conditionally dependent predicates underlies the construction of this model. Procedures that allow an autonomous intelli...

Full description

Saved in:
Bibliographic Details
Published inJournal of computer & systems sciences international Vol. 58; no. 5; pp. 747 - 765
Main Author Melekhin, V. B.
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.09.2019
Springer Nature B.V
Subjects
Artificial Intelligence
Automation
Automobile industry
Control
Engineering
Logic
Mechatronics
Problem solving
Representations
Robotics
Robots
Online AccessGet full text

Cover

More Information
Summary:The model for the representation of declarative and procedural knowledge of an autonomous intelligent robot is developed without reference to a specific subject area. The logic of conditionally dependent predicates underlies the construction of this model. Procedures that allow an autonomous intelligent robot to automatically generate new knowledge needed for a readout in the process of planning goal-seeking behavior in undetermined conditions of a problem-solving environment are proposed. The method of proving the satisfiability of the formulas under the logic of conditionally dependent predicates with linear complexity is based on the attribution of object variables in them as objects of the problem-solving environment and serves to process knowledge that is used by an autonomous intelligent robot to automatically build plans for goal-seeking behavior under undetermined operating conditions.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1064-2307
1555-6530
DOI:10.1134/S1064230719050101