"Chunking" "Artificial intelligence" Learning

"Chunking" 

"Artificial intelligence" Learning

Chunking refers to the organization of information. The process of encoding memories into long term memory for later recall is the same whether the information is chunked or unchunked.

"Chunking" refers to the organization of information. The process of encoding memories into long term memory for later recall is the same whether the information is chunked or unchunked.

Chunking is a process similar in flavor to macro-operators. The idea of Chunking comes from the psychological literature on memory and problem solving. Its computational basis is in production system.

Chunking is a universal learning method, i.e., it can account for all types of learning in intelligent systems. 

SOAR solves problems by firing productions, which are stored in long-term memory. Some of those firings turn out to be more useful than others. When SOAR detects a useful sequence of production firings, it creates a chunk, which Is essentially a large production that does the work of an entire sequence of smaller ones. As in MACROPS, chunks are generalized before they are stored. 

SOAR is a uniform processing architecture. Problems like choosing which subgoals to tackle and which operators to try (i.e., search control problems) are solved with the same mechanisms as problems in the original problem space.

As a result, SOAR is able to learn within trials as well as across trials. Chunks learned during the initial stages of solving a problem are applicable in the later stages of the same problem-solving episode. After a solution is found, the chunks remain in memory, ready-for-use in the next problem. 

Chunking emphasizes how learning can occur during problem-solving, while macro tables are usually built during a preprocessing stage.

SOAR has used Chunking to replicate the macro-operator results described in the last section.

Example :-  

solving the 8-puzzle, for example, SOAR learns how to place a given tile without permanently disturbing the previously placed tiles. Given the way that SOAR learns, several chunks may encode a single macro-operator, and one chunk may participate in a number of macro sequences. Chunks are generally applicable toward any goal state. This contrasts with macro tables, which are structured toward reaching a particular goal state from any initial state. Also, Chunking emphasizes how learning can occur during problem-solving, while macro tables are usually built during a preprocessing stage.