Moteur d'inférence
Un moteur d'inférence est un composant crucial de intelligence artificielle (AI) systems, particularly in the context of systèmes experts and systèmes basés sur la connaissance. Its primary function is to process information from a knowledge base using a set of predefined rules to make deductions, draw conclusions, or solve problems.
Le moteur d'inférence fonctionne en appliquant raisonnement logique aux données qu'il reçoit. Cela peut impliquer deux principaux types de raisonnement :
- Chaîne avant: This method starts with the available data and applies inference rules to extract more data until a goal is reached. It is a data-driven approach.
- Chaîne arrière: In contrast, this approach begins with a hypothesis or goal and works backward to determine if the available data supports it, making it a goal-driven method.
Inference engines can utilize various algorithms and techniques, such as rule-based reasoning, case-based reasoning, and model-based reasoning, to process information and generate outputs. They play a critical role in applications like traitement du langage naturel, decision support systems, and robotics.
In summary, the inference engine acts as a bridge between the knowledge stored within a system and the intelligent conclusions that can be drawn from that knowledge. Its efficiency and accuracy are vital for the performance of any AI application that relies on logical reasoning to function effectively.