A Reasoning System is a part of an AI system that solves problems, makes decisions, or draws inferences, essentially simulating the cognitive process of human thinking. It interprets and applies knowledge to new situations or to assess existing ones.


Think of a Reasoning System as the brain-like part of an AI. Just like when humans have to decide something, the AI uses its ‘brain’ (the Reasoning System) to figure out what to do. It uses what it knows and applies it to new situations, similar to how you would use what you’ve learned to solve a new problem at school.

In-depth explanation

Reasoning Systems are a central aspect of Artificial Intelligence, providing the cognitive capabilities that allow an AI to interpret, infer, and draw conclusions from provided data or existing knowledge. Reasoning Systems attempt to simulate human cognitive processes, relying on different kinds of logical and decision-making systems to arrive at conclusions or solutions to problems.

There are several types of reasoning systems, each with a distinct approach to interpreting data and making decisions. For example, Deductive Reasoning Systems apply general rules to specific situations (for example, all birds have wings therefore, a robin, which is a bird, has wings). Inductive Reasoning Systems, on the other hand, infer general principles from specific examples (seeing many white swans may lead to the general conclusion that all swans are white).

Another type of reasoning system is Abductive Reasoning System. It aims at finding the best explanation for observed phenomena. If you see water on the floor, and it’s raining outside, the most likely explanation would be that the rainwater came inside.

Implementing a Reasoning System can be an elaborate task, especially considering the complexity and diversity of Real-world decisions. Probabilistic Reasoning Systems are employed where uncertainty is a key factor. They are typically based on Bayesian inference, which provides a mathematical approach to estimate the probability of a hypothesis given observed evidence.

Graph-based Reasoning Systems use entities and relationships for processing; they are effective in scenarios where visual reasoning and spatial awareness are required.

Finally, the development of Reasoning Systems also involves addressing critical ethical considerations, to ensure that the AI’s decision-making is fair, unbiased, and transparent.

Deductive Reasoning, Inductive Reasoning, Abductive Reasoning, Probabilistic Reasoning, Graph-based Reasoning, Decision Making, Inference, Cognitive Science, Knowledge Representation, Bayesian Inference.