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ECAI
1994
Springer
13 years 12 months ago
Exploiting Causal Domain Knowledge for Learning to Control Dynamic Systems
This paper introduces a simple yete ective method for using causal domain knowledge for learning to control dynamic systems. Elementary qualitative causal dependencies of the domai...
Achim G. Hoffmann
IJCAI
2007
13 years 9 months ago
Transferring Learned Control-Knowledge between Planners
As any other problem solving task that employs search, AI Planning needs heuristics to efficiently guide the problem-space exploration. Machine learning (ML) provides several tec...
Susana Fernández, Ricardo Aler, Daniel Borr...
IJCAI
1997
13 years 9 months ago
Combining Knowledge Acquisition and Machine Learning to Control Dynamic Systems
This paper presents an interactive method for building a controller for dynamic systems by using a combination of knowledge acquisition and machine learning techniques. The aim is...
G. M. Shiraz, Claude Sammut
FLAIRS
2004
13 years 9 months ago
Using Previous Experience for Learning Planning Control Knowledge
Machine learning (ML) is often used to obtain control knowledge to improve planning efficiency. Usually, ML techniques are used in isolation from experience that could be obtained...
Susana Fernández, Ricardo Aler, Daniel Borr...
EWCBR
2004
Springer
14 years 1 months ago
Explanations and Case-Based Reasoning: Foundational Issues
By design, Case-Based Reasoning (CBR) systems do not need deep general knowledge. In contrast to (rule-based) expert systems, CBR systems can already be used with just some initial...
Thomas Roth-Berghofer