Name
Abstract | ||
|---|---|---|
In this paper a planning framework based on Ant Colony Optimization techniques is presented. Optimal planning is a very hard computational problem which has been coped with different methodologies. Approximate methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good, often optimal, solutions. Our proposal is to use an Ant Colony Optimization approach, based both on backward and forward search over the state space, using different pheromone models and heuristic functions in order to solve sequential optimization planning problems. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10291-2_22 | AI*IA |
Keywords | Field | DocType |
heuristic function,approximate method,different pheromone model,different methodology,ant colony optimization technique,sequential optimization planning problem,optimal planning,ant colony optimization approach,hard computational problem,planning framework,ant colony optimization,state space | Ant colony optimization algorithms,Computational problem,Heuristic,Mathematical optimization,Computer science,Meta-optimization,Optimal planning,Artificial intelligence,State space,Completeness (statistics),Metaheuristic | Conference |
Volume | ISSN | Citations |
5883 | 0302-9743 | 2 |
PageRank | References | Authors |
0.38 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Baioletti | 1 | 17 | 2.13 |
| A. Milani | 2 | 14 | 2.35 |
| V. Poggioni | 3 | 6 | 1.86 |
| Fabio Rossi | 4 | 52 | 6.62 |