Michie, D. | Spiegelhalter, C.C.
This book (originally published in 1994 by Ellis Horwood) is now out of print. The copyright now resides with the editors who have decided to make the material freely available on the web.This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.
Although our approach is quite general, we are really only interested in programs which evaluate "semihard" functions, believing that the evaluation of such functions constitutes the defining aspiration of machine intelligence work. If a function is less hard than "semihard," then we can evaluate it by pure algorithm (trading space for time) or by pure lookup (making the opposite trade), with no need to talk of knowledge, advice, machine intelligence, or any of those things. Our defmition of "semihard" adopts computer programs as the sole representational form for solution algorithms, and explicitly recognises both categories of resource-bound, namely time (e.g. A "semihard" problem is a non-"hard" problem whose space-minimal solution Assuming Case A, chess meets the first half of the definition.
Michie, D. | Chambers, R. A.
In brief, we believe that programs for learning large games will need to have at their disposal good rules for learning small games. Each separate box functions as a separate learning machine: it is only brought into play when the corresponding board position arises, and its sole task is to arrive at a good choice of move for that specific position. The demon's task is to make his choices in successive plays in such a way as to maximise his expected number of wins over some specified period. By a development of Laplace's Law of Succession we can determine the probability, This defines the score associated with the node N. To make a move the automaton examines all the legal alternatives and chooses the move leading to the position having the highest associated score, ties being decided by a random choice.
I shall discuss automatic methods of search for solutions in problems susceptible of a particular formal representation, namely that on which the Graph Traverser program (Doran & Michie 1966, and see Doran p. 105) has been based. One approach, based on state-evaluation, generates all the states of the problem which can be reached in a small number of moves from the current state, and then seeks by some process of evaluation to decide which state shall form the next point of departure. In the classical studies of Newell, Shaw & Simon (1960) selection is applied by going down a priority sequence of operators, applying to each in turn a number of tests, first of applicability to the current state and then of whether the operator conduces towards one or another of various desirable intermediate states, or subgoals.