Communications of the ACM
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
The bombe—a remarkable logic machine
Cryptologia
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Recursive functions of symbolic expressions and their computation by machine, Part I
Communications of the ACM
Algorithmic Program DeBugging
Logic for Problem Solving
Prolog - the language and its implementation compared with Lisp
Proceedings of the 1977 symposium on Artificial intelligence and programming languages
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Probabilistic inductive logic programming: theory and applications
Probabilistic inductive logic programming: theory and applications
Machine Learning
Meta-interpretive learning: application to grammatical inference
Machine Learning
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During the centennial year of his birth Alan Turing 1912--1954 has been widely celebrated as having laid the foundations for Computer Science, Automated Decryption, Systems Biology and the Turing Test. In this paper we investigate Turing's motivations and expectations for the development of Machine Intelligence, as expressed in his 1950 article in Mind. We show that many of the trends and developments within AI over the last 50 years were foreseen in this foundational paper. In particular, Turing not only describes the use of Computational Logic but also the necessity for the development of Machine Learning in order to achieve human-level AI within a 50 year time-frame. His description of the Child Machine a machine which learns like an infant dominates the closing section of the paper, in which he provides suggestions for how AI might be achieved. Turing discusses three alternative suggestions which can be characterised as: 1 AI by programming, 2 AI by ab initio machine learning and 3 AI using logic, probabilities, learning and background knowledge. He argues that there are inevitable limitations in the first two approaches and recommends the third as the most promising. We compare Turing's three alternatives to developments within AI, and conclude with a discussion of some of the unresolved challenges he posed within the paper.