Logic in AI
- [Véronique du Boisrouvray]
- Overview
Logic is the basic concept of artificial intelligence (AI). It allows AI systems to draw conclusions and inferences from data.
Logical AI involves the use of logical statements to represent an agent's knowledge, goals, and current situation. The agent then decides what to do by inferring that a certain action or course of action is appropriate to achieve the goal.
Logical thinking AI goes beyond pattern recognition and statistical learning. It aims to imitate human cognitive processes and decision-making. Logical thinking AI focuses on building the ability to:
- Draw logical conclusions
- Detect inconsistencies
- Reasoning through complex problems
Formal logic in AI is important so that agents or systems can think and act like humans. It ensures that information is shared with minimal errors and that AI conclusions are either correct or incorrect.
Logical AI involves representing an agent's knowledge of the world, its goals, and its current situation through logical sentences. An agent decides what to do by inferring that a certain action or course of action is appropriate to achieve a goal.
Human-level AI requires programs that can handle common sense information situations. Human-level logical AI requires expanding the way logic is used in the formalization of branches of mathematics and physical science. It also seems to require extensions to logic itself, both in the formalism used to express knowledge and in the reasoning used to arrive at conclusions.
The five logical symbols in AI are: negation, conjunction, disjunction, implication, and biconditional.
[More to come ...]
