Foundations of logic programming
Foundations of logic programming
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning automata: an introduction
Learning automata: an introduction
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
From Logic to Logic Programming
From Logic to Logic Programming
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Learning Automata and Stochastic Optimization
Learning Automata and Stochastic Optimization
Learning to Predict by the Methods of Temporal Differences
Machine Learning
OLD Resolution with Tabulation
Proceedings of the Third International Conference on Logic Programming
Statistical Abduction with Tabulation
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Statistical Abduction with Tabulation
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
EM Learning for Symbolic-Statistical Models in Statistical Abduction
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Inductive logic programming: yet another application of logic
INAP'05 Proceedings of the 16th international conference on Applications of Declarative Programming and Knowledge Management
| Hi-index | 0.00 |
In this paper, we describe recent attempts to incorporate learning into logic programs as a step toward adaptive software that can learn from an environment. Although there are a variety of types of learning, we focus on parameter learning of logic programs, one for statistical learning by the EM algorithm and the other for reinforcement learning by learning automatons. Both attempts are not full-fledged yet, but in the former case, thanks to the general framework and an efficient EM learning algorithm combined with a tabulated search, we have obtained very promising results that open up the prospect of modeling complex symbolic-statistical phenomena.