Titlebook: Inductive Logic Programming; 23rd International C Gerson Zaverucha,Vítor Santos Costa,Aline Paes Conference proceedings 2014 Springer-Verla

Infiltrate

assail

Learning Through Hypothesis Refinement Using Answer Set Programming,mes the scalability problem of ASPAL by breaking the learning process up into small manageable steps and using theory revision over the meta-level representation of the hypothesis space to improve the hypothesis computed at each step. We empirically evaluate the computational gain with respect to ASPAL using two different answer set solvers.

entrance

A Direct Policy-Search Algorithm for Relational Reinforcement Learning,gorithm is easy to comprehend and is biased towards compactness. The results obtained show that . is competitive in both the standard testing environment and in . and ., two large and complex game environments.

Vaginismus

0302-9743 , held in Rio de Janeiro, Brazil, in August 2013. .The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining

defile

On Differentially Private Inductive Logic Programming,ypotheses is “short” and “narrow,” we might be able to get meaningful results. To prove our intuition, we implement a differentially private version of Aleph, and our experimental results show that our algorithm is able to produce accurate results for those two cases.

可卡

Accelerating Imitation Learning in Relational Domains via Transfer by Initialization,nsfer the models learned in these environments. Our experiments demonstrate that our learner learns a very good initial model from the simple scenario and effectively transfers the knowledge to the more complex scenario thus achieving a jump start, a steeper learning curve and a higher convergence in performance.

AWRY