INFORMS Journal on Applied Analytics · 1991 · 69 citations · 30 references
ComputingComputer ScienceArtificial IntelligenceKnowledge RepresentationMachine LearningAutomated ReasoningCognitive ModelingExpert SystemsNeural NetworksBlack Box ModelingBusiness Intelligence SystemsHuman IntelligenceLearning In Expert SystemsQualitative ReasoningWhite Box Modeling
Artificial intelligence and expert systems use a black‑box, deductive approach with external learning, while neural networks model the brain as a white‑box, inductive system with internal learning, both aiming to formalize qualitative business problems alongside quantitative methods. The paper proposes integrating AI/ES and neural networks to tackle qualitative business problems beyond current methods.
Artificial intelligence (including expert systems) (AI/ES) and neural networks (NN) provide methods for formalizing qualitative aspects of business systems. They complement quantitative methods in solving business problems. While AI and NN have the common goal of simulating human intelligence, they use different methods. AI/ES assumes the brain is a black box and imitates the human reasoning process. It processes knowledge sequentially, represents it explicitly, and mostly uses deductive reasoning. Learning takes place outside the system. NN treats the brain as a white box and imitates its structure and function, using a parallel approach to simulate human intelligence. It represents knowledge implicitly within its structure and applies inductive reasoning to process knowledge. Learning takes place within the system. Both AI/ES and NN have great potential to solve qualitative problems, and their integration could provide a powerful tool for dealing with problems outside the domain of current problem-solving methods.
30
Optimization by Simulated Annealing
Scott Kirkpatrick, C. D. Gelatt, M.P. Vecchi · Science · 1983 · 44.6K citations