Welcome to my website.
I am interested in Artificial Intelligence (AI) in general. In specific, I am interested in automated planning. I am also interested in knowledge representation and reasoning, modeling and remodeling problems in AI, Constraint Satisfaction Problems (CSP) (e.g. Proposition Satisfiability problem (SAT)), statistical models in AI (e.g. Bayesian Nets), and Machine Learning.
My name is Maher A. Alhossaini. I am currently an assistant professor at the Computer Science department of the College of Computer and Information Sciences at King Saud University. I received my Bachelor degree in Computer and Information Sciences from King Saud University in Riyadh, Saudi Arabia in 2000. In 2006 I received the Master of Science degree in Computer Science from Stanford University, California, USA with specialization in Artificial Intelligence. In 2013, I received the Ph.D degree in Computer Science from the University of Toronto, Canada. The title of my Ph.D Thesis was: “Remodeling Planning Domains Using Macro Operators and Machine Learning”.
I am interested in Artificial Intelligence (AI) in general. In specific, I am interested in automated planning. I am also interested in knowledge representation and reasoning, modeling and remodeling problems in AI, Constraint Satisfaction Problems (CSP) (e.g. Proposition Satisfiability problem (SAT)), statistical models in AI (e.g. Bayesian Nets), and Machine Learning.
Introduction to AI problem solving - Search - Local Search - Knowledge representation - Constraint Satisfaction - Propositional Logic - First-Order Logic - Automatic theorem proving -...
Introduction to AI problem solving - Search - Local Search - Knowledge representation: First-Order Logic - Automatic theorem proving - Planning - Reasoning - reasoning with uncertainty -...
Topic: Automated Planning. Contents: Introduction to AI and search - Classical planning - Planning representation - State-space planning - Planning grpahs - Planning as Satisfiability -...
The thesis of this dissertation is that automating domain remodeling in AI planning using macro operators and making remodeling more flexible and applicable can improve the planning...