"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.

Arguably the first article discussing the uses of AI in the law beyond straightforward information retrieval.Although the computer has worked its way out of the laboratory and into common experience, lawyers have made slim progress towards finding useful computer applications. Research in artificial intelligence, a branch of computer science, has illuminated our capacity to use computers to model human thought processes. This research suggests that computer science may assist lawyers in both the study and performance of their reasoning processes. In this Article we will argue that the time has come for serious interdisciplinary work between lawyers and computer scientists to explore the computer's potential in law.Stanford Law Review vol.23, no.1, November, 1970

Some of the world's most advanced artificial intelligence (AI) systems, at least the ones the public hear about, are famous for beating human players at chess or poker. Other algorithms are known for their ability to learn how to recognize cats or their inability to recognize people with darker skin. But are current AI systems anything more than toys? Sure, their ability to play games or identify animals is impressive, but does this help toward creating useful AI systems? To answer this, we need to take a step back and question what the goals of AI are.

Machine learning has ties to statistics. It allows you to detect patterns in the sometimes chaotic mathematical models that exist. Online machine learning courses teach you how to make use of machine learning algorithms in a programming language. The disruptions caused by (and anticipated disruptions of) artificial intelligence is a reality we have come to live with. You do not need to be a techie to appreciate the many inroads artificial intelligence has made into everyday life.

"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

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

"The purpose of this paper is to clarify some basic issues of choice of representation for problems of reasoning about actions. The general problem of re- Presentation is concerned with the relationship between different ways of formulating a problem to a problem solving system and the efficiency with which the system can be expected to find a solution to the problem. An understanding of the relationship between problem formulation and problem solving efficiency is a prerequisite for the design of procedures that can automatically choose the most `appropriate' representation of a problem ( they can find a `point of view' of the problem that maximally simplifies the process of finding a solution).Many problems of practical importance are problems of reasoning about actions. In these problems, a course of action has to be found that satisfies a number of specified conditions. A formal definition of this class of problems is given in the next section, in the context of a general conceptual framework for formulating these problems for computers. Everyday examples of reasoning about actions include planning an airplane trip, organizing a dinner party, etc. There are many examples of industrial and military problems in this category, such as scheduling assembly and transportation processes, designing a program for a computer, planning a military operation, etc."In D.Michie (Ed.), Machine intelligence 3. New York: American Elsevier,131-171

Note: PDF of full volume downloadable by clicking on title above (26 MB). Selected individual chapters available from the links below. CONTENTSINTRODUCTION MATHEMATICAL FOUNDATIONS1 The morphology of prex—an essay in meta-algorithmics. J. LAS KS 32 Program schemata. M. S. PATE RSON 193 Language definition and compiler validation. J. J. FLORENTIN 334 Placing trees in lexicographic order. H. I.S COINS 43 THEOREM PROVING5 A new look at mathematics and its mechanization. B. M ELTZER 636 Some notes on resolution strategies. B. MELTZER 717 The generalized resolution principle. J. A. ROBINSON 778 Some tree-paring strategies for theorem proving. D.LUCKHAM 959 Automatic theorem proving with equality substitutions andmathematical induction. J. L. D ARLINGTON 113 MACHINE LEARNING AND HEURISTIC PROGRAMMING10 On representations of problems of reasoning about actions.S.AMAREL 13111 Descriptions. E.W.ELCOCK 17312 Kalah on Atlas. A.G.BELL 18113 Experiments with a pleasure-seeking automaton: J. E. DORAN 19514 Collective behaviour and control problems. V.I.VARSHAVSKY 217 MAN—MACHINE INTERACTION15 A comparison of heuristic, interactive, and unaided methods ofsolving a shortest-route problem. D.MICHIE, J. G. FLEMING andJ. V.OLDFIELD 24516 Interactive programming at Carnegie Tech. A.H.BOND 25717 Maintenance of large computer systems—the engineer's assistant.M.H.J.BAYLIS 269 COGNITIVE PROCESSES: METHODS AND MODELS18 The syntactic analysis of English by machine. J.P.THORNE,P.BRATLEY and H.DEWAR 28119 The adaptive memorization of sequences. H.C.LONOUETHIGGINSand A.ORTONY 311 PATTERN RECOGNITION20 An application of Graph Theory in pattern recognition.C.J.HILDITCH 325 PROBLEM-ORIENTED LANGUAGES21 Some semantics for data structures. D. PARK 35122 Writing search algorithms in functional form. R.M.BURSTALL 37323 Assertions: programs written without specifying unnecessaryorder. J.M.FOSTER 38724 The design philosophy of Pop-2. R.J.POPPLESTONE 393 INDEX 403 Machine Intelligence Workshop

In Michie, Donald (Ed.), Machine Intelligence 3. Edinburgh University Press pp.195-216.

"Among the new languages for instructing computers is a remarkable one called LISP. The name comes from the first three letters of LIST and the first letter of PROCESSING. Not only is LISP a language for instructing computers but it is also a formal mathematical language, in the same way as elëmentary algebra when rigorously defined and used is a formal mathematical language.The LISP language and its implementation on the IBM 7090 computer were worked out by a group including John McCarthy, Stephen B. Russell , Daniel J. Edwards, Paul W. Abrahams, Timothy P. Hart, Michael I. Levin, Marvin L. Minsky, and others.LISP is designed primarily for processing data consisting of lists of symbols. It has been used for symbolic calculations in differential and integral calculus, electrical circuit theory, mathematical logic , game playing, and other fields of intelligent handling of symbols."Information International, Inc, Cambridge, Mass.