An introduction to symbolic dynamics and coding
An introduction to symbolic dynamics and coding
Multimodal reasoning for automatic model construction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An ontology for transitions in physical dynamic systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Proceedings of the First International Workshop on Hybrid Systems: Computation and Control
HSCC '98 Proceedings of the First International Workshop on Hybrid Systems: Computation and Control
Generalized Physical Networks for Automated Model Building
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
| Hi-index | 0.00 |
The goal of input-output modeling is to apply a test input to a system, analyze the results, and learn something useful from the cause-effect pair. Any automated modeling tool that takes this approach must be able to reason effectively about sensors and actuators and their interactions with the target system. The granulation level of the information involved in this process ranges from low-level data analysis techniques to abstract, qualitative observations about the system. This chapter describes a knowledge representation and reasoning framework that allows this process to be automated.