Simulation-assisted inductive learning
Conference
·
OSTI ID:7135343
Learning by induction can require a large number of training examples. We show the power of using a simulator to generate training data and test data in learning rules for an expert system. The induction program is RL, a simplified version of Meta-DENDRAL. The expert system is ABLE, a rule-based system that identifies and located errors in particle beam lines used in high energy physics. A simulator of beam lines allowed forming and testing rules on sufficient numbers of cases that ABLE's performance is demonstrably accurate and precise. 13 refs., 2 figs.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 7135343
- Report Number(s):
- LA-UR-88-2462; CONF-880835-2; ON: DE88014398
- Resource Relation:
- Conference: 7. national conference on artificial intelligence, St. Paul, MN, USA, 22 Aug 1988; Other Information: Portions of this document are illegible in microfiche products
- Country of Publication:
- United States
- Language:
- English
Similar Records
Selecting durable building envelope systems with machine learning assisted hygrothermal simulations database
Interactive knowledge discovery from marketing questionnarie using simulated breeding and inductive learning methods
Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)
Conference
·
Wed Dec 01 00:00:00 EST 2021
·
OSTI ID:7135343
+4 more
Interactive knowledge discovery from marketing questionnarie using simulated breeding and inductive learning methods
Conference
·
Tue Dec 31 00:00:00 EST 1996
·
OSTI ID:7135343
Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)
Software
·
Fri Mar 18 00:00:00 EDT 2022
·
OSTI ID:7135343
+3 more