The term „table-based learning“ stems primarily from the fields of Artificial Intelligence, Big Data and Smart Data, and automation. Here, it describes a method whereby machines or computers learn from previously collected information presented in tabular form.
Imagine a large table that precisely records which action worked best in a particular situation in the past. The machine then checks if it has seen this or a similar situation before. If so, it refers back to this information and makes a decision that has already proven itself. This way, the machine can gradually improve because it has a growing number of examples to draw upon.
A simple example: When controlling robots in a factory, movement data and measured situations are recorded in tables. If the robot encounters an obstacle, it searches this table for a solution that was previously successful. This makes the work process more efficient and safer.
Table-based learning is therefore a practical, easy-to-understand way to make machines progressively more intelligent by learning from stored data.
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