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TEC
2002
163views more  TEC 2002»
15 years 6 months ago
Ants can solve constraint satisfaction problems
In this paper, we describe a new incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to us...
Christine Solnon
ICRA
2009
IEEE
132views Robotics» more  ICRA 2009»
16 years 1 months ago
Smoothed Sarsa: Reinforcement learning for robot delivery tasks
— Our goal in this work is to make high level decisions for mobile robots. In particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actio...
Deepak Ramachandran, Rakesh Gupta
AI
1998
Springer
15 years 11 months ago
A Hybrid Convergent Method for Learning Probabilistic Networks
During past few years, a variety of methods have been developed for learning probabilistic networks from data, among which the heuristic single link forward or backward searches ar...
Jun Liu, Kuo-Chu Chang, Jing Zhou
AIEDAM
1998
87views more  AIEDAM 1998»
15 years 6 months ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh
ILP
2004
Springer
16 years 4 days ago
Learning an Approximation to Inductive Logic Programming Clause Evaluation
One challenge faced by many Inductive Logic Programming (ILP) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as ...
Frank DiMaio, Jude W. Shavlik