subsymbolischen

Subsymbolischen is a German adjective that translates to "subsymbolic" in English. It is primarily used in the fields of artificial intelligence, cognitive science, and philosophy of mind to describe approaches or systems that do not rely on explicit, symbolic representations of knowledge or reasoning.

In contrast to symbolic AI, which manipulates discrete symbols (like words or logical propositions) to represent and process information, subsymbolic approaches deal with representations that are distributed, continuous, and often emergent. This can include things like connectionist models (neural networks), fuzzy logic, and other forms of statistical learning. These systems learn from data and their internal states or parameters, rather than being explicitly programmed with rules.

The idea behind subsymbolic processing is that complex cognitive phenomena, such as perception, learning, and pattern recognition, might arise from the interactions of many simple, non-explicitly defined components. It suggests that intelligence can be achieved through the collective behavior of these units, without needing to break down problems into a series of logical steps. This often leads to systems that are more robust to noisy or incomplete data and can exhibit emergent properties. The term is used to distinguish these methods from earlier, more rule-based or logic-driven AI.