Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Inductive learning methods for knowledge-based decision support: a comparative analysis
Computer Science in Economics and Management
An object-oriented multiple agent planning system
Distributed Artificial Intelligence (Vol. 2)
Knowledge acquisition from an incomplete domain theory: an application on the stock market
Computer Science in Economics and Management
Supporting distributed office problem solving in organizations
ACM Transactions on Information Systems (TOIS) - Special issue: selected papers from the conference on office information systems
Explanation-Based Generalization: A Unifying View
Machine Learning
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Expert Systems with Applications: An International Journal
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Knowledge acquisition is the process of accumulating new information and relating it to what is already known. Knowledge acquisition has been regarded as the bottleneck in knowledge-based systems development. In this paper, a distributed knowledge acquisition system (DKAS) is introduced for automating decision rules construction from a set of examples in a decision support system. DKAS has the potential to include various learning mechanisms and employs a multi-agent and parallel processing paradigm. The implementation of a DKAS integrates inductive and deductive learning methods that use different learning strategies. A stock selection problem is used to demonstrate the effectiveness of DKAS in solving classification type problems. The performance of the DKAS in portfolio management is compared to the performance of the NYSE and the S&P 500. The results indicate that the rules derived from using the DKAS outperform both the NYSE and the S&P 500.