GFT-AIML'2022 will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Artificial Intelligence and Machine Learning. The Conference looks
for significant contributions to all major fields of the Artificial Intelligence, Machine Learning in
theoretical and practical aspects.
The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of AI and Machine Learning.
The Automated reasoning is an area of cognitive science (involves knowledge representation and reasoning) and metalogic dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science, and even philosophy.
Case-based reasoning is based on the paradigm of human thought in cognitive psychology that contends that human experts derive their knowledge from solving numerous cases in their problem domain.
The term cognitive computing is typically used to describe AI systems that aim to simulate human thought. ... A number of AI technologies are required for a computer system to build cognitive models that mimic human thought processes, including machine learning, deep learning, neural networks, NLP and sentiment analysis (Wiki).
Commonsense reasoning is one of the branches of artificial intelligence (AI) that is concerned with simulating the human ability to make presumptions about the type and essence of ordinary situations they encounter every day.These assumptions include judgments about the physical properties, purpose, intentions and behavior of people and objects, as well as possible outcomes of their actions and interactions (Wiki).
Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning (O’Reilly).
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow. It does so by ranking alternatives. The Heuristic is any device that is often effective but will not guarantee work in every case (data-flair.training).
The conference is seeking submissions related to the following conference topics : all aspects of Machine Learning, Artificial Intelligence, and approaches to computational intelligence and Data Sciences. Other related topics will also be considered.