Readings in medical artificial intelligence: the first decade
Readings in medical artificial intelligence: the first decade
A guide to expert systems
Communications of the ACM - Special issue on parallelism
Adaptive self-organizing logic networks
Adaptive self-organizing logic networks
Programming expert systems in PASCAL
Programming expert systems in PASCAL
SOAR: an architecture for general intelligence
Artificial Intelligence
Benchmark problems for formal nonmonotonic reasoning
Proceedings of the 2nd international workshop on Non-monotonic reasoning
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Learning nonrecursive definitions of relations with LINUS
EWSL-91 Proceedings of the European working session on learning on Machine learning
A connectionist model for commonsense reasoning incorporating rules and similarities
Knowledge Acquisition
A conversation with Marvin Minsky about agents
Communications of the ACM
Enabling agents to work together
Communications of the ACM
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Knowledge-based artificial neural networks
Artificial Intelligence
Expert Systems
The Science of Artificial Intelligence
The Science of Artificial Intelligence
Machine Learning
Machine Learning
Learnability of Constrained Logic Programs
ECML '93 Proceedings of the European Conference on Machine Learning
ILA: Combining Inductive Learning with Prior Knowledge and Reasoning
ILA: Combining Inductive Learning with Prior Knowledge and Reasoning
A Hybrid Architecture for Situated Learning of Reactive Sequential Decision Making
Applied Intelligence
Three fundamental misconceptions of Artificial Intelligence
Journal of Experimental & Theoretical Artificial Intelligence
CBR model for the intelligent management of customer support centers
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems.