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. The first part of the paper begins with a philosophical point of view that seems to arise naturally once we take seriously the idea of actually making an intelligent machine. We go on to the notions of metaphysically and epistemo-logically adequate representations of the world and then to an explanation of can, causes, and knows in terms of a representation of the world by a system of interacting automata. A proposed resolution of the problem of freewill in a deterministic universe and of counterfactual conditional sentences is presented. The second part is mainly concerned with formalisms within which it can be proved that a strategy will achieve a goal. Concepts of situation, fluent, future operator, action, strategy, result of a strategy and knowledge are formalized. A method is given of constructing a sentence of first-order logic which will be true in all models of certain axioms if and only if a certain strategy will achieve a certain goal. The formalism of this paper represents an advance over McCarthy (1963) and Green (1969) in that it permits proof of the correctness of strategies that contain loops and strategies that involve the acquisition of knowledge; and it is also somewhat more concise. The third part discusses open problems in extending the formalism of part 2. The fourth part is a review of work in philosophical logic in relation to problems of artificial intelligence and a discussion of previous efforts to program ‘general intelligence’ from the point of view of this paper.

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Some philosophical problems from the standpoint of artificial intelligence

From the Publisher:
This book presents the data structures and algorithms that underpin much of today's computer programming. The basis of this book is the material contained in the first six chapters of our earlier work, The Design and Analysis of Computer Algorithms. We have expanded that coverage and have added material on algorithms for external storage and memory management. As a consequence, this book should be suitable as a text for a first course on data structures and algorithms. The only prerequisite we assume is familiarity with some high-level programming language such as Pascal.

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Data Structures and Algorithms

The authors extend the definitions of the previously introduced family of data flow testing criteria to apply to programs written in a large subset of Pascal. They then define a family of adequacy criteria called feasible data flow testing criteria, which are derived from the data-flow testing criteria. The feasible data flow testing criteria circumvent the problem of nonapplicability of the data flow testing criteria by requiring the test data to exercise only those definition-use associations which are executable. It is shown that there are significant differences between the relationships among the data flow testing criteria and the relationships among the feasible data flow testing criteria. The authors discuss a generalized notion of the executability of a path through a program unit. A script of a testing session using their data flow testing tool, ASSET, is included. >

An applicable family of data flow testing criteria

It would be useful if computers could learn from experience and thus automatically improve the efficiency of their own programs during execution A simple but effective rote-learning facility can be provided within the framework of a suitable programming language

“Memo” Functions and Machine Learning

Improvements in programming technology have paralleled improvements in computing system architecture and materials. Along with increasing knowledge of the system and program development processes, there has been some notable research into programming project measurement, estimation, and planning. Discussed is a method of programming project productivity estimation. Also presented are preliminary results of research into methods of measuring and estimating programming project duration, staff size, and computer cost.

A method of programming measurement and estimation

By reading, you can know the knowledge and things more, not only about what you get from people to people. Book will be more trusted. As this systematic programming an introduction, it will really give you the good idea to be successful. It is not only for you to be success in certain life you can be successful in everything. The success can be started by knowing the basic knowledge and do actions.

Systematic Programming: An Introduction

: An introduction to LISP is given on an elementary level. Topics covered include the programming system, 240 exercises with solutions, debugging of LISP programs, and styles of programming. More advanced discussions are contained in the following articles: Techniques using LISP for automatically discovering interesting relations in data; Automation, using LISP, of inductive inference on sequences; Application of LISP to machine checking of mathematical proofs; METEOR: A LISP interpreter for string transformations; Notes on implementing LISP for the M-460 computer; LISP as the language for an incremental computer; The LISP system for the Q-2 computer; An auxiliary language for more natural expression -- the A-language. Some applications of the utilization of the LISP programming language are given in the appendices.

Paul Abrahams

Papers

Systematic Programming: An Introduction

The programming language lisp: its operation and applications,

A mathematical theory of global program optimization

The Macro Implementation of SNOBOL4

Computers in Undergraduate Science Education