Presents SEATER/spl minus/an object-oriented environment in which any general telephone traffic routing problem can be set up, tested, and simulated using a variety of routing methods. The routing methods available are the fixed rule, random routing, and routing utilizing a complete assortment of different learning automata. The paper first describes the general telephone traffic routing problem, and various existing fixed rule routing schemes supported by the system are explained. This is followed by a brief motivation and description of learning automata routing techniques, and a survey of those schemes which are supported by the prototype system implemented. The paper then highlights the considerations that were taken in the design and implementation of the object-oriented prototype, SEATER. These automata schemes supported by SEATER are compared to the existing fixed rule algorithms in terms of minimizing the blocking probability of the network. The simulations show that the former solutions are far superior to any fixed rule solutions. The advantage of the former lies in their adaptability to changes in telephone traffic. The system is written in SMALLTALK V and runs on a MAC II.>

Let Lambda be a finite plaintext alphabet and V be a cypher alphabet with the same cardinality as Lambda . In all one-to-one substitution cyphers, there exists the property that each element in V maps onto exactly one element in Lambda and vice versa. This mapping of V onto Lambda is represented by a function T*, which maps any v in V onto some lambda in Lambda (i.e., T*(v)= lambda ). The problem of learning the mapping of T* (or its inverse (T*)/sup -1/) by processing a sequence of cypher text is discussed. The fastest reported method to achieve this is a relaxation scheme that utilizes the statistical information contained in the unigrams and trigrams of the plaintext language. A new learning automaton solution to the problem called the cypher learning automaton (CLA) is given. The proposed scheme is fast, and the advantages of the scheme in terms of time and space requirements over the relaxation method have been listed. Simulation results comparing both cypher-breaking techniques are presented.>

The authors consider the problem of assigning more than one symbol of a finite alphabet A to the same key on a keyboard. Since multiple symbols of the alphabet A reside on the same key, the representations of all the words in a finite dictionary H need not be unique. The problem is one of optimally assigning the symbols of the alphabet to the keys of a given keyboard with a view to minimize the total number of words that have ambiguous representation. The problem is proven to be NP-hard. After presenting the only reported solution to the problem, a fast learning-automaton-based solution to this problem is reported. Experimental results demonstrating the power of this solution are presented.>