Sociologists are concerned to predict the effect of changes on future society.But is prediction in principle possible when intelligence is involved? Ifintelligence is the production of novelty, accurate prediction might seem to bestrictly impossible. However this may be, it seems that the present troubleabout social prediction is simply that there are no adequate theoreticalmodels of societies. This means that politicians are almost powerless topredict, plan, or control, except with incredible errors. We find ourselves injust this position in trying to assess the implications of future intelligence.Machine Intelligence 6

"Problem-solving methods using some sort of heurstically guided search process have been the subject of much research in Artificial Intelligence. This paper groups these problem-solving methods under three major headings: the State-Space Approach, the Problem-Reduction Approach and the Formal-Logic Approach." New York: McGraw-Hill.

The problem we consider in this paper is that of discovering formal ruleswhich will enable us to decide when a question posed in English can beanswered on the basis of one or more declarative English sentences. Toillustrate how this may be done in very simple cases we give rules whichtranslate certain declarative sentences and questions involving the quantifiers'some', 'every', 'any', and 'no' into a modified first-order predicate calculus,and answer the questions by comparing their translated forms with those ofthe declaratives. We suggest that in order to capture the meanings of morecomplex sentences it will be necessary to go beyond the first-order predicatecalculus, to a notation in which the scope of words other than quantifiersand negations is clearly indicated.Machine Intelligence 6

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 goal of such analysis is to recognize (by computer) such forms and shapes when present in the input scene; that is, to name (correctly) as many parts of the scene as possible: finger, hand, girl, dance, and so on. Complications occur because each input scene contains several such objects, partially occluding each other and in varying degrees of orientation, size, and so on. 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?' Thus, shape drastically limits the values that a component could have, and further disambiguation is possible only by using global information (derived from several components and their interrelations or interconnections) under the assumption that the scene as a whole is meaningful. This paper proposes a way to solve'the context problem' in the paradigm of coloring book drawings. We have not implemented this approach; indeed, a purpose of this paper is to collect criticisms and suggestions.

In N. V. Findler and B. Meltzer (Eds.), Artificial intelligence and heuristic programming. New York: American Elsevier, 81-100.

Machine Intelligence 6

A general game-playing program must know the rules of the particular playing game. These rules are:(1) an algorithm indicating the winning state;(2) an algorithm enumerating legal moves. A move gives a set of changes from the present situation.There are two means of giving these rules:(1) We can write a subroutine which recognizes if we have won and another which enumerates legal moves. Such a subroutine is a black box giving to the calling program the answer: 'you win' or 'you do not win', or the list of legal moves. But it cannot know what is in that subroutine.(2) We can also define a language in which we describe the rules of a game. The program investigates the rules written with this language and finds some indications to improve its play. Artificial Intelligence and Heuristic Programming Edinburgh University Press

Machine Intelligence 6

Inference systems Τ and search strategies E for T are distinguished from proof procedures β = (T,E) The completeness of procedures is studied by studying separately the completeness of inference systems and of search strategies. Completeness proofs for resolution systems are obtained by the construction of semantic trees. These systems include minimal α-restricted binary resolution, minimal α-restricted M-clash resolution and maximal pseudo-clash resolution. Certain refinements of hyper-resolution systems with equality axioms are shown to be complete and equivalent to refinements of the pararmodulation method for dealing with equality. The completeness and efficiency of search strategies for theorem-proving problems is studied in sufficient generality to include the case of search strategies for path-search problems in graphs. The notion of theorem-proving problem is defined abstractly so as to be dual to that of and" or tree. Special attention is given to resolution problems and to search strategies which generate simpler before more complex proofs. For efficiency, a proof procedure (T,E) requires an efficient search strategy E as well as an inference system T which admits both simple proofs and relatively few redundant and irrelevant derivations. The theory of efficient proof procedures outlined here is applied to proving the increased efficiency of the usual method for deleting tautologies and subsumed clauses. Counter-examples are exhibited for both the completeness and efficiency of alternative methods for deleting subsumed clauses. The efficiency of resolution procedures is improved by replacing the single operation of resolving a clash by the two operations of generating factors of clauses and of resolving a clash of factors. Several factoring methods are investigated for completeness. Of these the m-factoring method is shown to be always more efficient than the Wos-Robinson method. The University of Edinburgh

This paper describes an effort to design a heuristic problem-solving program which accepts problems stated in a nondeterministic programming language and applies constraint satisfaction methods and heuristic search methods to find solutions. The use of nondeterministic programming languages for stating problems is discussed, and ref, the language accepted by the problem solver arf, is described. Various extensions to ref are considered. The conceptual structure of the program is described in detail and various possibilities for extending it are discussed. The use of the input language and the behaviour of the program are described and analyzed in sixteen sample problems.