ACM Computing Surveys (CSUR)
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Towards an Automated Approach to Dynamic Interpretation of Simulations
AMS '07 Proceedings of the First Asia International Conference on Modelling & Simulation
Intelligent management of data driven simulations to support model building in the social sciences
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Semi-automated simulation transformation for DDDAS
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
Symbiotic Simulation Control in Semiconductor Manufacturing
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Computational & Mathematical Organization Theory
Research issues in symbiotic simulation
Winter Simulation Conference
Perennial simulation of a legacy traffic model: implementation, considerations, and ramifications
Proceedings of the Winter Simulation Conference
A Review for the Validation of Social Simulation on Artificial Social Organization
International Journal of Agent Technologies and Systems
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This paper presents a prototype implementation of an intelligent assistance architecture for data-driven simulation specialising in qualitative data in the social sciences. The assistant architecture semi-automates an iterative sequence in which an initial simulation is interpreted and compared with real-world observations. The simulation is then adapted so that it more closely fits the observations, while at the same time the data collection may be adjusted to reduce uncertainty. For our prototype, we have developed a simplified agent-based simulation as part of a social science case study involving decisions about housing. Real-world data on the behaviour of actual households is also available. The automation of the data-driven modelling process requires content interpretation of both the simulation and the corresponding real-world data. The paper discusses the use of Association Rule Mining to produce general logical statements about the simulation and data content and the applicability of logical consistency checking to detect observations that refute the simulation predictions.