Machine Intelligence 5. B. Meltzer & D. Michie (eds.), 78-98. Edinburgh: Edinburgh University Press.

"A computer program capable of acting intelligently in the world must have a general representation of the world in terms of which its inputs are interpreted. Designing such a program requires commitments about what knowledge is and how it is obtained. Thus, some of the major traditional problems of philosophy arise in artificial intelligence.More specifically, we want a computer program that decides what to do by inferring in a formal language that a certain strategy will achieve its assigned goal. This requires formalizing concepts of causality, ability, and knowledge. Such formalisms are also considered in philosophical logic." - from the Introduction reprinted in Matthew Ginsberg (ed.), Readings in Nonmonotonic Reasoning, pp. 26-45, San Francisco: Morgan Kaufmann Publishers, Inc., 1987.Stanford web version. D. Michie and B. Meltzer (Eds.), Machine intelligence 4 - Edinburgh: Edinburgh University Press, 463-502

In Meltzer, Bernard and Donald Michie (Eds.), Machine Intelligence 5. Edinburgh University Press.

Note: PDF of full volume downloadable by clicking on title above (32.8 MB). Selected individual chapters available from the links below.CONTENTSINTRODUCTORY MATERIALMATHEMATICAL FOUNDATIONS1 Program scheme equivalences and second-order logic. D. C. COOPER 32 Programs and their proofs: an algebraic approach.R. M. BURSTALL and P. J. LANDIN 173 Towards the unique decomposition of graphs. C. R. SNOW andH. I. SCOINS 45THEOREM PROVING4 Advances and problems in mechanical proof procedures. D. PRAWITZ 595 Theorem-provers combining model elimination and Tesolution.D. W. LOVELAND 736 Semantic trees in automatic theorem-proving. R. KOWALSKI andP. J. HAYES 877 A machine-oriented logic incorporating the equality relation.E. E. SIBERT 1038 Paramodulation and theorem-proving in first-order theories withequality. G. ROBINSON and L. Wos 1359 Mechanizing higher-order logic. J. A. ROBINSON 151DEDUCTIVE INFORMATION RETRIEVAL10 Theorem proving and information retrieval. J. L. DARLINGTON 17311 Theorem-proving by resolution as a basis for question-answeringsystems. C. CORDELL GREEN 183MACHINE LEARNING AND HEURISTIC PROGRAMMING12 Heuristic dendral: a program for generating explanatory hypothesesin organic chemistry. B. BUCHANAN, G. SUTHERLAND andE. A. FEIGENBAUM 20913 A chess-playing program. J. J. SCOTT 25514 Analysis of the machine chess game. I. J. GOOD 26715 PROSE—Parsing Recogniser Outputting Sentences in English.D. B. VIGOR, D. URQUHART and A. WILKINSON 27116 The organization of interaction in collectives of automata. 285V. I. VARSHAVSKY COGNITIVE PROCESSES: METHODS AND MODELS17 Steps towards a model of word selection. G. R. Kiss 31518 The game of hare and hounds and the statistical study of literaryvocabulary. S. H. STOREY and M. A. MAYBREY 33719 The holophone —recent developments. D. J. WILLSHAW andH. C. LONGUET-HIGGINS 349PATTERN RECOGNITION20 Pictorial relationships — a syntactic approach. M. B. CLOWES 36121 On the construction of an efficient feature space for optical characterrecognition. A. W. M. COOMBS 38522 Linear skeletons from square cupboards. C. J. HILDITCH 403PROBLEM-ORIENTED LANGUAGES23 Absys 1: an incremental compiler for assertions; an introduction.J. M. FOSTER and E. W. ELCOCK 423PRINCIPLES FOR DESIGNING INTELLIGENT ROBOTS24 Planning and generalisation in an automaton/environment system.J. E. DORAN 43325 Freddy in toyland. R. J. POPPLESTONE 45526 Some philosophical problems from the standpoint of artificialintelligence. J. MCCARTHY and P. J. HAYES 463INDEX 505 Machine Intelligence Workshop

Methods are presented 1) to partition or decompose a visual scene into the bodies forming it; 2) to position these bodies in three-dimensional space, by combining two scenes that make a stereoscopic pair; 3) to find the regions or zones of a visual scene that belong to its background; 4) to carry out the isolation of the objects in 1) when the input has inaccuracies. Running computer programs implement the methods and many examples illustrate their behavior. The input is a two-dimensional line-drawing of the scene, assumed to contain three-dimensional bodies possessing flat faces (polyhedra); some of them may be partially occluded. Suggestions are made for extending the work to curved objects. Some comparisons are made with human visual perception.

"Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies." IEEE Transactions on Systems Science and Cybernetics, SSC-4 (2), 100-107. See also correction: http://dl.acm.org/citation.cfm?id=1056779.

In Proc. 23rd ACM National Conference.

During the past few years there has been a rapid advance in the technology of time-sharing systems and software to permit quick access to large files of structured data. This has led to a growing interest in communicating with computer files directly in a natural language such as English. The natural language systems described in the literature are largely small-scale research vehicles dealing with small data bases of restricted subject scope. Giuliano (1965), among others, has questioned the generalization of these systems to wider universes of discourse. Developments in this area have been reviewed by Simmons (1966), and by Bobrow, Fraser and Quillan (1967).

Simmons has presented a survey of some fifteen experimental question-answering and related systems which have been constructed since 1959. These systems take input questions in natural English (subject to varying constraints) and attempt to answer the questions on the basis of a body of information, called the data base, which is stored inside the computer. This process can be conceptually divided into three phases---syntatic analysis, semantic analysis, and retrieval, as illustrated schematically in Figure 1. The first phase consists of parsing the input sentence into a structure which explicitly represents the grammatical relationships among the words of the sentence. The remaining phase consists of procedures for either retrieving the answer directly from the data base, or else deducing the answer from information contained in the data base.

Cambridge, Mass.: MIT Press