Unified framework for implementing inaccurate knowledge in quantum symbolic artificial intelligence models

Abstract

[Abstract]; Symbolic models of Artificial Intelligence are based on defining declarative knowledge that is connected through procedural knowledge forming symbolic graphs through which reasoning flows. Both declarative and procedural knowledge can be inaccurate, which has led to the definition of different models to represent this inaccuracy. Since the functioning of quantum computers is inherently probabilistic, it has been proposed to take advantage of this nature to implement inaccurate knowledge more effectively. In this paper, we present different models for implementing inaccurate knowledge in quantum computers and propose a unified framework to represent and implement the common features of all of them.