A logic-based calculus of events
New Generation Computing
Solving the frame problem: a mathematical investigation of the common sense law of inertia
Solving the frame problem: a mathematical investigation of the common sense law of inertia
New Generation Computing
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Learning Programs in the Event Calculus
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Prediction is deduction but explanation is abduction
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Inductive logic programming algorithm for estimating quality of partial plans
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Hypothesizing about causal networks with positive and negative effects by meta-level abduction
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Inducing causal laws by regular inference
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Interleaved inductive-abductive reasoning for learning complex event models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Completing causal networks by meta-level abduction
Machine Learning
Learning from interpretation transition
Machine Learning
Hi-index | 0.00 |
An Event Calculus program to control the navigation of a real robot was generated using Theory Completion techniques. This is an application of ILP in the non-observational predicate learning setting. This work utilized 1) extraction-case abduction; 2) the simultaneous completion of two, mutually related predicates; and 3) positive observations only learning. Given time-trace observations of a robot successfully navigating a model office and other background information, Theory Completion was used to induce navigation control programs in the event calculus. Such programs consisted of many clauses (up to 15) in two mutually related predicates. This application demonstrates that abduction and induction can be combined to effect nonobservational multi-predicate learning.