Distributed Artificial Intelligence • For Classical and Quantum Computers (book trailer)

I present my new book Distributed Artificial Intelligence • Artificial Reasoning for Classical and Quantum Computers. It is now available on Amazon. I started working on it three years ago when I realized how AI and knowledge management are related.

I provide you with an insight into the book and present the introduction. I also give an overview of the main topics.

Intro

The domain of a knowledge engineering model is a science that is related to reasoning. The distributed Inventor algorithm for the classical computer and its variant for the quantum computer solve tasks and subtasks of the domain conception. It uses a distributed and multi-dimensional reinforcement learning technique. Reasoning techniques solve the dynamic system of the keystone species and derive scientific principles. The Inventor analyzes and synthesizes scientific principles and reasoning techniques. It applies logical inference techniques to process scientific texts on three levels.

The multidimensional agent learns knowledge and generalizes information in order to create new knowledge. The agent solves tasks which require different skills and transfers shared features from one skill to another. The agent learns a strategy and uses this strategy to solve a new task and to achieve long-term results for complex new tasks even in different domains. It processes the laws of science and the universe.

Amazon

amazon.com/author/zahrammasadiq

Keystone species

The keystone species is a model of game theory that uses a pyramid scheme to represent the interdependency of the agent’s dimensions.

Dynamic system

The Inventor is a distributed AI system that processes neural differential equations which depend on each other and therefore form a dynamic system.

Reduction by implication

I relate the keystone species to a form of reductionism that I call reduction by logical implication.

Metapattern and distributed focus

I use a distributed focus technique to perform complex pattern recognition tasks.

Multidimensional Reinforcement learning

The multidimensional and bidirectional reinforcement learning technique uses Bayesian techniques. The agent learns how the net that represents the semantic and logical relations including the hierarchy of abstraction is structured. The quantum Inventor is the quantum version of the Inventor. It processes a quantum master equation. The quantum Inventor algorithm uses techniques which comply with the laws of quantum mechanics.

The content of this book is original research. Thank you for supporting my work.