Proceedings of the Annual Meeting of the Cognitive Science Society

A connectionist network has been used to simulate the solution, using a hill-climbing heuristic,of the DOG - > CA T puzzle (changing 1 letter at a time, generate a sequence of 3-letter wordsbeginning with DO G and ending with C A T ) and a simpler variant of the 8-tile puzzle, thedog-cat-mouse (DCM ) ^'uzzle devised by Klahr (1985). Distributed representations have beenused to represent the dilFcrent possible states of the puzzles. These states are learned by thenetwork and become local energy minima of the system. T o simulate the sequence of statescorresponding to a solution of the puzzle, the initial state of the network is set to the startstate, and the goal state is presented to the network as a continuous input. A sequence oi"states is generated by habituation, a short-term modification of the connection strengths whenever all the elements in the network are maximally or minimally activated, and by exploitingthe property that successive states comprising the solution are similar.