Higher Order Neural Nets for Task Planning in Decomposed State Spaces

A higher-order single-layer relaxation-type recurrent neural network is employed to plan a task in a decomposed state space. The finite state machine model of the state space of the complex task is decomposed into parallel/series combinations of component machines, each of which represents a subtask, using lattice theoretic techniques. Planning the complex task is realized by identifying the transfer sequence, an ordered set of inputs, of the component state machines for the subtasks. A fourth-order stochastic optimization neural network, single-layer relaxationtype recurrent network with simulated annealing, is utilized to perform the search needed to specify the transfer sequence in the state space of the complex task. The effect of decomposition on two essential areas is highlighted: significant reduction in computational resources required to implement the neural algorithm and the need to employ a high order neural network are emphasized.