Symbolic AI
An AI approach that performs reasoning and problem-solving by manipulating symbols representing concepts and rules.
Detailed Definition
Symbolic AI, also known as classical AI or rule-based AI, is an artificial intelligence approach that performs reasoning and solves problems by explicitly manipulating symbols representing concepts, facts, and rules (such as logical expressions and production rules). It contrasts with connectionist AI (such as neural networks) that primarily relies on data-driven learning. Symbolic AI has important applications in knowledge representation, logical reasoning, planning, and expert systems. Modern AI research often explores the combination of symbolic AI and connectionist AI (neuro-symbolic AI) to leverage the strengths of both approaches for more robust and interpretable AI systems.
Core ConceptsMore in this Category
Action Model
An internal model used by AI agents to predict the potential outcomes of their actions in specific states.
Agent
An AI system capable of taking actions autonomously to achieve specific goals in an environment.
Algorithm
A set of rules or instructions designed to solve a specific problem or perform a task.
Artificial Intelligence (AI)
The simulation of human intelligence in machines programmed to think and learn like humans.
Machine Learning
A subset of AI that enables systems to learn and improve automatically from experience without explicit programming.
Software Agent
A computer program that acts autonomously on behalf of users or other programs to perform specific tasks in a computer system or network.