Merging local patterns using an evolutionary approach

This paper describes a Decentralized Agent-based model for Theory Synthesis (DATS) implemented by MASETS, a Multi-Agent System for Evolutionary Theory Synthesis. The main contributions are the following: first, a method for the synthesis of a global theory from distributed local theories. Second, a conflict resolution mechanism, based on genetic algorithms, that deals with collision/contradictions in the knowledge discovered by different agents at their corresponding locations. Third, a system-level classification procedure that improves the results obtained from both: the monolithic classifier and the best local classifier. And fourth, a method for mining very large datasets that allows for divide-and-conquer mining followed by merging of discoveries. The model is validated with an experimental application run on 15 datasets. Results show that the global theory outperforms all the local theories, and the monolithic theory (obtained from mining the concatenation of all the available distributed data), in a statistically significant way.