A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Instance-Based Learning Algorithms
Machine Learning
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Constraint reasoning based on interval arithmetic: the tolerance propagation approach
Artificial Intelligence - Special volume on constraint-based reasoning
Qualitative-numeric simulation with Q3
Recent advances in qualitative physics
Obtaining quantitative estimates from monotone relationships
Recent advances in qualitative physics
Arc-consistency for continuous variables
Artificial Intelligence
Extracting and representing qualitative behaviors of complex systems in phase space
Artificial Intelligence
Results on controlling action with projective visualization
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
ELA—A new Approach for Learning Agents
Autonomous Agents and Multi-Agent Systems
Hi-index | 0.00 |
Many industrial processes are not understood enough and are too complex for inductive learning methods. The author's technique combines qualitative and memory-based reasoning to model and predict such processes. He has applied this technique to coffee roasting and decaffeination.At the Swiss Federal Institute of Technology's Artificial Intelligence Laboratory, we have developed an approach that combines qualitative reasoning and memory-based reasoning, thereby exploiting their strengths and compensating for their weaknesses. We have applied this approach to two processes: coffee roasting and decaffeination. Both applications used large amounts of data collected from plants operated by Nestle in the UK and Spain. Roasting and decaffeination are processes for which existing models can provide only very inaccurate predictions. In both cases, attempts to predict behavior using statistical methods and neural networks have not provided usable predictions. In contrast, the qualitative models used in memory-based reasoning take into account subtleties of the processes that purely statistical criteria are likely to miss. The results are thus significantly better than what conventional methods could produce.