The motivation for this software is to demonstrate that an ANN can store data in memory, and make use of that data. This is something that cannot be done by the most common types of ANNs, such as MLP's. Feed-forward ANNs have only the equivalent of ROM; they have no writeable memory. Training or evolving the ANN determines a set of weights and biases that can handle a given problem. This memory is analogous to ROM. It is also analogous to species learning, as done by ants or bees, whereby the species as a whole has learned much, but the individuals learn little. In contrast, there are problems that require learning after the training or evolution is completed. In principle, an ANN with recurrent connections can act as a RAM and store information even with fixed weights & biases. The challenge was to create an ANN that "knows" how to make use of this ability. Therefore a problem was chosen in which active, RAM-like, memory was central to the solution. The 4-card puzzle in 4play requires the ANN to remember the locations of two cards, and also the identity of those cards. Then it must make correct decisions based on that information.
This puzzle is loosely based on the game of Concentration, where cards are spread face down on a table and the players get to turn over two cards, trying to form pairs. Each turn starts with the cards face down, since the main point is to remember what cards were exposed, and where they are located.
This is the problem to be solved by an ANN: There is a deck of 4 cards, 2 aces & 2 deuces. The cards are shuffled and dealt face down on the table. This pattern of 4 face down cards is presented to the ANN. Then one card is exposed and the new pattern is presented to the ANN. A second card, not matching the first, is then exposed, and this pattern is presented. Then the cards are turned face down again, this pattern is presented to the ANN, and this time the ANN makes a choice of a card to be exposed. The chosen card is exposed and the 4-card pattern presented to the ANN. It then makes its 2nd choice to conclude the game. If its two choices form a pair, it wins. Otherwise it loses.
The goal is to evolve ANNs that are perfect players of this puzzle. This has been achieved. The 4play package includes "chromosomes" that create perfect ANNs when read into the evolving population.
Much more information about 4play is included in the downloadable files at:
The Annevolve team is experimenting with the Evolution of Artificial Neural Networks (ANNs). They are combining the two fields of Artificial Neural Networks and Evolutionary Computation. Find out more about the other neuroevolution programs at: