Abstract: The paper deals with a problem of intelligent system’s design for complex environments. There is discussed a possibility to integrate several technologies into one basic structure that could form a kernel of an autonomous intelligent robotic system. One alternative structure is proposed in order to form a basis of an intelligent system that would be able to operate in complex environments. The proposed structure is very flexible because of features that allow adapting via learning and adjustment of the used knowledge. Therefore, the proposed structure may be used in environments with stochastic features such as hardly predictable events or elements. The basic elements of the proposed structure have found their implementation in software system and experimental robotic system. The software system as well as the robotic system has been used for experimentation in order to validate the proposed structure - its functionality, flexibility and reliability. Both of them are presented in the paper. The basic features of each system are presented as well. The most important results of experiments are outlined and discussed at the end of the paper. Some possible directions of further research are also sketched at the end of the paper.

Keywords: Artificial intelligence, Inductive reasoning, Deductive reasoning, Case based reasoning, Machine learning, Learning algorithms.

ACM Classification Keywords: I.2.9 Robotics; I.2.3 Deduction and Theorem Proving: Deduction, Induction, Uncertainty, Fuzzy, and Probabilistic Reasoning; I.2.6 Learning

Link:

ROBOT CONTROL USING INDUCTIVE, DEDUCTIVE AND CASE BASED REASONING

Agris Nikitenko

http://www.foibg.com/ijita/vol12/ijita12-4-p13.pdf