Which approaches rely on symbolic representations and logical inference instead of statistical methods?

The chosen answer is correct because symbolic approaches fundamentally focus on using symbols and rules to represent knowledge and reasoning processes. These approaches utilize logical inference to draw conclusions from the information represented symbolically. This contrasts sharply with statistical methods, which rely on data patterns and probabilities rather than defined rules and logical structures.

In symbolic approaches, knowledge can be explicitly articulated as a set of symbols and relations, allowing for reasoning and manipulation based on logical principles. This method is particularly effective in domains where clear, interpretable reasoning is crucial, often aligning with traditional artificial intelligence techniques that prioritize understanding and interpretability.

In contrast, deep learning, neural networks, and reinforcement learning are methods that predominantly utilize statistical techniques and data-driven strategies to learn from examples or optimize performance based on feedback. These approaches emphasize pattern recognition and learning from large datasets rather than the explicit logical representation of knowledge. Therefore, they do not align with the symbolic representation and logical inference characteristics that define symbolic approaches.