The Inductive Net is made up of a set of horizontal lines (input lines) crossing at right angles a set of vertical lines (output lines), binary switches being placed at the intersections so formed. Each possible feature value has one horizontal line and one vertical line identified with it. A pattern is stored in the Net by exciting the horizontal lines and the vertical lines identified with its feature values, and turning on each switch which receives excitation along both the lines on which it is placed. We do this by giving the Inductive Net additional horizontal lines, each of which has a mask placed on the front of it which causes the line to fire when a particular combination of feature values occur together in the input pattern.
We describe the analysis of visual scenes consisting of black on white drawings formed with curved lines, depicting familiar objects and forms: houses, trees, persons, and so on; for instance, drawings found in coloring books. The analysis of these line drawings is an instance of'the context problem', which can be stated as'given that a set (a scene) is formed by components that locally (by their shape) are ambiguous, because each shape allows a component to have one of several possible values (a circle can be sun, ball, eye, hole) or meanings, can we make use of context information stated in the form of models, in order to single out for each component a value in such manner that the whole set (scene) is consistent or makes global sense?' This paper proposes a way to solve'the context problem' in the paradigm of coloring book drawings. The problem we are trying to solve is the Context Problem, which can be stated in general words as'given that a set (a scene) is formed by components that locally (by their shape) are ambiguous, because they can have one of several possible values (a circle sun, ball, eye, hole) or meanings, can we make use of By analyzing each component, we come to several possible interpretations of such component, and further disambiguation is possible only by using global information (information derived from several components, or by the interconnection or interrelation between two or more components), under the assumption that the scene as a whole'makes global sense' or is'consistent'.
Ph.D. dissertation "Bi-directional and heuristic search in path problems" (Stanford, Computer Science, 1970) summarized in this article in Machine Intelligence 6 (1971).In the uni-directional algorithms, the search proceeds from an initial nodeforward until the goal node is encountered. Problems for which the goal nodeis explicitly known can be searched backward from the goal node. Analgorithm combining both search directions is bi-directional.This method has not seen much use because book-keeping problems werethought to outweigh the possible search reduction. The use of hashingfunctions to partition the search space provides a solution to some of theseimplementation problems. However, a more serious difficulty is involved.To realize significant savings in bi-directional search, the forward andbackward search trees must meet in the 'middle' of the space. The potentialbenefits from this technique motivates this paper's examination of thetheoretical and practical problems in using bi-directional search.
Attempts to write'intelligent' computer programs have commonly involved the choice for attack of some particular aspect of intelligent behaviour, together with the choice of some relevant task, or range of tasks, which the program must perform. Toda (1962), in a whimsical and illuminating paper, has discussed the problems facing an automaton in a simple artificial environment. The reader may find it illuminating to imagine himself (the automaton) before a screen on which is displayed a complex pattern which changes from time to time (sequence of states). These are: 1. the subjective environment graph (figure 1), 2. the stored graph which is that portion of the subjective environment graph which the automaton has stored in its memory as a result of its experience (figure 2 (b)), and 3. the option graph which is that fragment of the stored graph which the automaton'knows' how to reach (figure 2(c)).
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.
Before discussing in detail an example of a simple heuristic problemsolving program, the Graph Traverser, I shall give an indication of the most important work carried out in this field, stressing the more fundamental ideas. Bobrow (1964) describes a question-answering system for high school algebra word problems'. I shall distinguish four: (1) the external application problem to which the program has been applied; (2) the internal problem which the program generates from the application problem and which it must solve in order to produce a solution to the application problem (this internal problem will be an'idealised' version of the application problem, and will typically vary little from one application to another. It is important to realise that more than one internal problem may be capable of derivation from a given application problem); (3) the strategy which the program uses to solve the internal problem; (4) the translation process from the application problem to the internal problem.