Barrow HG, 1989. “AI, neural networks and early vision”. AISB Newsletter896–25. This is the text of an invited talk at the AISB-89 conference. It aprovides a good summary of the history of the ideas and methods that led to the development of neural networks.

Carbonell JG, 1982. “Experiential learning in analogical problem solving”. AAAI168–173. This paper describes how analogical reasoning can be used to generate exemplary solutions to related problems, and more general plans can be induced from these solutions.

Carbonell JG and Langley P, 1987. “Machine learning”. In: Shapiro SC and Eckroth D (eds.), Encyclopedia of Artificial Intelligence. Wiley Interscience. The authors provide a survey of the history of machine learning, and describe the methods of machine learning task based categories.

Carbonell JG, Michalski RS and Mitchell T, 1983. “An overview of machine learning”. In: Michalski RS, Carbonell JG and Mitchell T (eds.), Machine Learning: An artificial intelligence approach. Morgan Kaufmann. The authors explain the objectives of machine learning, describe its evolution and various learning methods according to the underlying strategy.

Caudell TP and Dolan CP, 1989. “Parametric connectivity: training of constrained networks using genetic algorithms”. In: Proceedings of the Third International Conference on Genetic Algorithms, pp 370–374. San Mateo, CA:Morgan Kaufmann. The paper describes how genetic algorithms are used in training neural networks.

Cohen PR and Feigenbaum EA (eds.), 1982. The Handbook of Artificial Intelligence, Vol. III. Pitman. This is the first comprehensive handbook of artificial intelligence, covering a wide range of topics. However, I do not know if it has a new edition.

Darden L, 1987. “Viewing the history of science as compiled hindsight”. The AI Magazine8(2) 33–42. The author views the history of science as an important source for developing and testing the computational models of scientific discovery. She also provides informal definitions of logical and extralogical methods of inference such as deduction, induction, abstraction, abduction and analogy.

Davis L, 1989. “Mapping neural networks into classifier systems”. In: Proceedings of the Third International Conference on Genetic Algorithms, pp 375–378. San Mateo, CA:Morgan Kaufmann. The paper describes the functional similarities between classifier systems and neural networks, and argues that any neural network can be transformed into a classifier system that is functionally isomorphic to the former.

De Jong K, 1988. “Learning with genetic algorithsm: an overview”. Machine Learning3121–138. This paper describes how genetic algorithms can be applied to machine learning problems.

Dietterich T, 1986. “Learning at the knowledge level”. Machine Learning1287–316. The author introduces a theory of knowledge and symbol level learning, and analyzes the methods of some of the learning systems. The author examines the describability of the behaviour of these systems at different levels.

Dietterich T and Michalski RS, 1983. “A comparative review of selected methods for learning from examples”. In: Michalski RS, Carbonell JG, and Mitchell TM (eds.), Machine Learning: An artificial intelligence approach, Morgan Kaufmann. The authors examine various learning systems from the standpoint of the adequacy of their methods of representation, their rules of generalization, computational efficiency, and flexibility and extensibility.

Falkenheiner B, 1987. “Scientific theory formation through analogical inference”. In: Proceedings of the Fourth International Workshop on Machine Learning, pp 218–229. Los Altos, CA: Morgan Kaufmann. This paper describes how analogy may be used to discover and refine qualitative models of the physical world.

Fatmi HA and Young RW, 1970. Nature228 (93). The authors define “intelligence” in computational terms, and discuss the implications of their definition.

Feigenbaum EA, 1963. “The simulation of verbal behaviour”. In: Feigenbaum EA and Feldman J (eds.), Computers and Thought, pp 297–309. McGraw-Hill.

Greiner R, 1988. “Learning by understanding analogies”. Artificial Intelligence3581–125. This paper defines analogical inference, and describes how it can be used in problem solving.

Harp SA, Samad T and Guha A, 1989. “Towards the genetic synthesis of neural networks”. In: Proceedings of the Third International Conference on Genetic Algorithms, pp 360–369. San Mateo, CA:Morgan Kaufmann. This paper describes how genetic algorithms can be used in training neural networks. The authors evaluate the efficiency of their methods.

Hinton GE, 1990. “Preface to the special issue on connectionist symbol processing”. Artificial Intelligence461–4. The author draws attention to the representational gulf between neural networks and higher level learning systems, and introduces the reader to the latest attempts to bridge this gulf.

Holland JH, 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press. In this important book the author develops his theory of genetic algorithms.

Holland JH, 1986. “Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems”. In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning: An artificial intelligence approach. Morgan Kaufmann. This paper describes the basic features of genetic algorithms, and explains how they can be combined with rule-based methods to provide general purpose inductive learning systems (or classifier systems) with an example.

Hopfield JJ and Tank DW, 1986. “Computing with neural circuits”. Science233625–633.

Hunt EB and Hovland CI, 1963. “Programming a model of human concept formation”. In: Feigenbaum EA and Feldman J (eds.), Computers and Thought, pp 310–325. McGraw-Hill.

Hunt EB, 1975. Artificial Intelligence. Academic Press. In this book the author gives a formal description of linear and nonlinear classifiers.

McDermott J, 1979. “Learning to use analogies”. In: Proceedings of the Sixth International Conference on Artificial Intelligence, pp 568–576. This paper is one of the early works on analogy. It examines the constituents of analogical reasoning and describes a rule-based system capable of analogical reasoning.

Michalski RS, 1983. “A theory and methodology of inductive learning”. In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning: An artificial intelligence approach. Morgan Kaufmann. In this work, the author views inductive learning as a heuristic search through a space of symbolic descriptions. The descriptions are generated by the application of inductive and deductive inference methods. The author classifies inductive learning into several different types and provides analytical descriptions of each type.

Michalski RS, 1986. “Understanding the nature of learning: issues and research directions”. In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning. Morgan Kaufmann. In this paper, the author discusses various definitions of learning and formulates the goals and directions in machine learning research. He also examines the current research paradigms in machine learning, and reviews learning strategies.

Michalski RS and Stepp RE, 1983. “Learning from observation: conceptual clustering”. In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning. Morgan Kaufmann. The authors introduce a detailed description of one of the major methods of learning from observation called “conceptual clustering”.

Michalski RS, Amarel S, Lenat DB, Michie D and Winston PH, 1986. “Machine learning: challenges of the Eighties”. In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning. Morgan Kaufmann. The authors discuss the important tasks for machine learning research and the role of machine learning in artificial intelligence. They also discuss the strategies that should be followed in machine learning research in the near future.

Mitchell T, 1983. “Learning and problem solving”. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pp 1139–1151. Karlsruhe, Germany: Morgan Kaufmann.

Mitchell T, Keller RM and Kedar-Cabelli ST, 1986. “Explanation based generalization: A unifying view”. Machine Learning1(1) 47–80. The authors describe one of the most productive methods of machine learning: explanation-based generalization, and explain how the method has been used in various learning tasks.

Naft J, 1989. “A modified Hopfield net approach to multi-objective design optimization for printed circuit board component placement”. Int. J. Neural Networks Research and Applications178–85. This paper describes an application of neural networks to optimization problems.

Newell A, 1982. “The knowledge level”. Artificial Intelligence18(1) 87–127. This is one of the influential papers in artificial intelligence. The author investigates the representational issues of learning and problem solving at several different levels.

Nilsson NJ, 1965. Learning Machines: Foundations of trainable pattern-classifying systems. McGraw-Hill. In this book, the author provides a symbolic description of classifiers.

Odetayo MO and McGregor DR, 1989. “Genetic algorithms for inducing control rules for a dynamic system”. Proceedings of the Third International Conference on Genetic Algorithms, pp 177–182. San Mateo, CA: Morgan Kaufmann. This paper describes how genetic algorithms are used for automatically inducing control rules for a dynamic physical system.

Porter B W and Kibler DF, 1986. “Experimental goal regression: A method for learning problem-solving heuristics”. Machine Learning1(3) 249–286. In this paper, the authors describe episodic learning, an enhanced version of explanation-based generalization. They explain with examples how the method has been implemented in a program.

Quinlan JR, 1983. “Learning efficient classification procedures and their application to chess end games”. In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning: An artificial intelligence approach. Morgan Kaufmann.

Rosenblatt F, 1958. “The perceptron: A probabilistic model for information storage and organization in the brain”. Psychological Review65386–407. This paper in one of the earliest works in connectionist systems.

Rumelhart DE and McClelland JL, 1986. Parallel Distributed Processing: Explorations in the microstructures of cognition. The MIT Press. This book constitutes an important contribution to cognitive science.

Simon HA, 1983a. “Why should machines learn?” In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning. Morgan Kaufmann. In this paper the author describes the objectives of machine learning.

Simon HA, 1983b. “Search and reasoning in problem solving”. Artificial Intelligence21 (1–2) 7–30. In this paper, the author discusses the role of reasoning in problem representation and problem solving.

Simon HA and Lea G, 1974. “Problem solving and rule induction: A unified view”. In: Gregg L (ed.), Knowledge and Cognition. Erlbaum. In this paper, the authors introduce a general model for learning, and describe its constituents.

Sowa J, 1987. “Semantic networks”. In: Shapiro SC (ed.), Encyclopedia of Artificial Intelligence. Wiley Interscience. The author provides a review of semantic networks, including a historical survey, early work, and discusses the relationships between logic, frame representation and semantic networks.

Stirling L, 1984. “Logical levels of problem solving”. J. Logic Programming2151–163. This paper identifies three levels of knowledge necessary for intelligent problem solving: The levels of domain knowledge, method and strategies, and planning. The author relates these levels to the distinction between object and meta languages.

Whitley D, Starkweather T and Fuquay D, 1989. “Scheduling problems and traveling salesmen: The genetic edge recombination operator”. Proceedings of the Third International Conference on Genetic Algorithms, pp 133–140. San Mateo, CA: Morgan Kaufmann. This paper describes an application of genetic algorithms to one of the classic problems in artificial intelligence.

Winston PH, (ed.), 1975. The Psychology of Computer Vision, pp 157–209. McGraw-Hill. Learning and discovery systems

Akl S, 1987. “Checkers-playing programs”. In: Shapiro SC (ed.), Encyclopedia of Artificial Intelligence. Wiley Interscience. Various checkers playing programs and their learning methods are described.

Friedland P, 1979. “Knowledge-based experiment design in molecular genetics”. In: Proceedings of the Sixth International Joint Conference on Artificial Intelligence,, pp 285–287. The paper describes MOLGEN, a rule-based system that designs experiments in molecular genetics.

Kocabas S, 1990. “Conflict resolution as discovery in particle physics”. Machine Learning (in press). This paper describes an impasse resolution system that models certain discoveries in particle physics.

Kottai RM and Bahill T, 1989. “Expert systems made with neural networks”. Int. J. Neural Networks Research and Applications1211–226. This paper examines the similarities and differences between expert systems built on neural networks and conventional shells.

Kulkarni D and Simon HA, 1988. “The processes of scientific discovery: The strategy of experimentation”. Cognitive Science12139–175. This paper describes a theory driven system that models empirical discovery in biochemistry.

Laird JE, Newell A and Rosenbloom PS, 1986. “Chunking in SOAR”. Machine Learning111–46. The paper describes a complex general system SOAR, which incorporates different learning methods.

Laird JE, Rosenbloon PS and Newell A, 1987. “SOAR: An architecture for general intelligence”. Artificial Intelligence331–64. This paper describes SOAR's control structure and its capabilities.

Langley P, 1978. “BACONI: A general discovery system”. In: Proceedings of the Second National Conference of the Canadian Society for Computational Studies. The paper describes the earliest version of BACON, a well known quantitative discovery program.

Langley P, 1981. “Data-driven discovery of physical laws”. Cognitive Science531–54. In this paper, the author describes the general heuristics that are used in quantitative discovery.

Langley P, Simon HA, Bradshaw GL and Zutkow JM, 1987. Scientific Discovery: Computational explorations of the creative processes. The MIT Press. This book is an excellent introduction to the current research into the computational modelling of scientific discovery. It describes several discovery systems such as GLAUBER, STAHL and BACON.

Lenat DB, 1979. “On automated scientific theory formation: a case study using the AM program”. In: Hayes J, Michie D and Mikulich LI (eds.), Machine Intelligence9, pp 251–283. Halstead. The author describes one of the most important discovery programs, AM, which rediscovers many arithmetical concepts starting with the basic concepts of set theory. The paper explains how AM's heuristics work.

Lenat DB, 1983. “EURISKO: A program that learns new heuristics and domain concepts”. Artificial Intelligence21(1–2) 61–98. In this paper, the author describes EURISKO, a theory driven discovery system developed as a successor to the AM program. EURISKO has the additional capability of discovering new problem solving heuristics.

Nordhausen B and Langley P, 1987. “Towards an integrated discovery system”. In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp 198–200. In this interesting paper, the authors describe IDS, a discovery system that integrates qualitative and quantitative methods. IDS is the first fully implemented discovery system with the capability of sensing and changing its environment.

O'Rorke P, Morris S and Schulenburg D, 1990. “Theory formation by abduction”. In: Shrager J and Langley P (eds.), Computational Models of Scientific Discovery and Theory Formation. Morgan Kaufmann. In this paper, the authors view abduction as a general method for theory revision. They describe a discovery program, AbE, and its application to the paradigm shift in the eighteenth century chemistry.

Rajamoney SA, 1990. “A computational approach to theory revision”. In: Shrager J and Langley P (eds.), Computational Models of Scientific Discovery and Theory Formation. Morgan Kaufmann. This paper describes a qualitative theory revision program with the capability of designing experiments to resolve contradictions in its explanations.

Rose D and Langley P, 1986. “Chemical discovery as belief revision”. Machine Learning1423–452. The paper describes one of the early examples of theory revision systems which has been applied to eighteenth century chemistry.

Thagard P, 1988. Computational Philosophy of Science. The MIT Press. This is an important book that relates machine learning and discovery to philosophy of science. It also describes a program that models theoretical discovery.

Thagard P and Holyoak K, 1985. “Discovering the wave theory of sound: Inductive inference in the context of problem solving”. In: Proceedings of the Ninth International Joint Conference on Artificial Intelligence, pp 610–612. In this paper the authors describe a program that models the discovery of theory of sound.

Zytkow JM and Simon HA, 1986. “A theory of historical discovery: The construction of componential models”. Machine Learning1107–137. The authors describe a data driven system that simulates the discoveries of the componential models of substances in the seventeenth century chemistry.

Brewka G, 1987. “The logic of inheritance in frame systems”. In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp 438–488. This paper presents a formal description of inheritance in frame systems, together with a formalism for transforming knowledge represented in a frame into predicate logic statements.

Carnap R, 1958. Introduction to Symbolic Logic and its Applications. Dover Publications. This book is interesting in its being one of the earliest and best examples of the linguistic philosophical approach to logic.

Charniak E and McDermott J, 1985. Introduction to Artificial Intelligence. Addison Wesley. This book is one of the well known textbooks in artificial intelligence.

Engelmore R and Morgan T (eds.), 1988. Blackboard Sytems. Addison Wesley. This book provides a detailed description of the methods used blackboard systems, and describes a number of applications.

Fikes R and Kehler T, 1985. “The role of frame-based representation in reasoning”. Commun. ACM28(9) 904–920. The authors describe the elements and the methods of the frame-based representation with examples. The paper does not include the new techniques used in frame systems.

Forbus KD, 1984. “Qualitative process theory”. Artificial Intelligence2485–168. The author describes the principles of qualitative representation of physical processes.

Frost R, 1986. Introduction to Knowledge Base Systems. Collins Professional and Technical Books. This is one of the earliest and most detailed books on knowledge-based systems.

Hayes-Ruth F, 1985. “Rule-based systems”. Commun ACM26(9) 921–932. This paper describes rule-based representation and its methods with examples.

Kocabas S, 1989. Functional Categorization of Knowledge: Applications in modeling scientific research and discovery. PhD thesis, Department of Electronic and Electrical Engineering, King's College London. This work introduces a methodology for organizing knowledge into functional categories, so that different types of inference can be implemented more efficiently.

Kowalski R, 1979. Logic for Problem Solving. North Holland. The book introduces first order predicate logic as a language for general problem solving.

Kowalski R, 1984. “Logic programming in the fifth generation”. The Knowledge Engineering Review1(1) 26–38. The paper describes how Prolog is used in knowledge representation and problem solving.

Minsky M, 1977. “Frame system theory, thinking: readings in cognitive science”. In: Johnson-Laird PN and Wason PC (eds.), Open University Set Book. Cambridge University Press, pp 355–376. This is one of the most influential papers in the development of knowledge-based systems.

Brugge JA and Buchanan BG, 1989. “Evolution of a knowledge based system for determining structural components of proteins”. Expert Systems6(3) 144–156. The authors describe a knowledge-based system, ABC, which determines the structural components of proteins. The system is based on the blackboard control architecture.

Buchanan BG and Feigenbaum EA, 1978. “Dendral and Meta-Dendral: Their application dimension”. Artificial Intelligence115–24. The paper describes DENDRAL, which constitutes one of the earliest successful implementations of the rule-based methods in scientific research, and Meta-DENDRAL, a system that discovers the heuristics to be used by the former.

Feigenbaum EA, 1989. “An interview”. Expert Systems6(2) 112–115. The author discussed the current developments in artificial intelligence, and makes projections into the future of the discipline is a leading researcher.

Lenat DB, Prakesh M and Shepherd M, 1986. “CYC: using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks”. The AI Magazine7(4) 65–85. This paper describes some of the design ideas of one of the largest research projects in artificial intelligence. The project is called CYC, and aims at building a general knowledge base including a large amount of commonsense knowledge.

Leant DB, and Feigenbaum EA, 1987. “On the threshold of knowledge”. In: Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pp 1173–1182. In this paper, the authors discuss the problems of building complex intelligent systems. Even though it contains some controversial views, the paper is an important contribution to artificial intelligence and should not be ignored.

Lenat DB and Guha RV, 1991. Building large Knowledge Based Systems: Representation and inference in the CYC Project. Addison Wesley. This is a new book in which the authors expand on Lenat et al.'s (1986) paper, and describe the knowledge representation and inference methods of CYC. Should not be missed by anyone who is interested in building large knowledge bases.