Constraint logic programming (CLP) is a generalization of logic programming (LP) where unification, the basic operation of LP languages, is replaced by constraint handling in a constraint system. The resulting languages combine the advantages of LP (declarative semantics, nondeterminism, relational form) with the efficiency of constraint-solving algorithms. For some classes of combinatorial search problems, they shorten the development time significantly while preserving most of the efficiency of imperative languages. This paper surveys this new class of programming languages from their underlying theory, to their constraint systems, and to their applications to combinatorial problems.

Learning is one of the important research fields in artificial intelligence. This paper begins with an outline of the definitions of learning and intelligence, followed by a discussion of the aims of machine learning as an emerging science, and an historical outline of machine learning. The paper then examines the elements and various classifications of learning, and then introduces a new classification of learning based on the levels of representation and learning as knowledge-, symboland device-level learning. Similarity- and explanation-based generalization and conceptual clustering are described as knowledge level learning methods. Learning in classifiers, genetic algorithms and classifier systems are described as symbol level learning, and neural networks are described as device level systems. In accordance with this classification, methods of learning are described in terms of inputs, learning algorithms or devices, and outputs. Then there follows a discussion on the relationships between knowledge representation and learning, and a discussion on the limits of learning in knowledge systems. The paper concludes with a summary of the results drawn from this review.

Many problem domains exhibit inherent parallelism, and parallel systems which capture and exploit this can be used to look for efficient solutions to AI problems. Functional programming languages are expected to be efficiently realisable on fifth generation hardware. A rational reconstruction of AI programming paradigms is used to investigate the programmability and performance of functional languages in this particular area.

Three languages—Standard ML, Hope+ and Miranda—are used in the rational reconstruction, each language being used to implement three applications. Results indicate that functional programming languages have become much more useable in recent years, and have the potential to become useful tools in AI problem solving. A brief annotated bibliography of texts which covers the introduction to, theory and implementation issues of, functional programming languages, is included.