Authors:
Henrique Krever
1
;
Thiago Leão
1
;
Juliano Pasa
1
;
Edison P. de Freitas
2
;
Raul Nunes
1
and
Luis A. L. Silva
1
Affiliations:
1
Graduate Program in Computer Science, Federal University of Santa Maria, Av. Roraima nº 1000, 97105-900, Santa Maria, Brazil
;
2
Graduate Program in Computer Science, Federal University of Rio Grande do Sul, CP 15064, 91501-970, Porto Alegre, Brazil
Keyword(s):
Topographic Path Planning, Heuristic Learning, Deep Neural Networks, Agent-Based Simulations.
Abstract:
Path planning algorithms with Deep Neural Networks (DNN) are fundamental to Agent-Based Modeling and Simulation (ABMS). Pathfinding algorithms use various heuristic functions while searching for a route with a low cost according to different criteria. When such algorithms are applied to compute agent routes in simulated terrain maps represented by large numbers of nodes and where topographic movement constraints are present, the problem is that traditional heuristic functions lose quality since they do not capture important characteristics for target simulation problems. To approach this issue, this work investigates the training of DNNs with large numbers of (i) topographic path costs and (ii) correction factors for standard Euclidean distance heuristic estimations. The aim is to use these DNNs as heuristic functions to guide the execution of different A∗ -based topographic path planning algorithms in agent-based simulations. The work approaches the heuristic learning and computatio
n of agent routes in topographic terrain maps of different natures. To assess the performance of the proposed techniques, experimental results with path planning algorithms and alternative topographic maps are analyzed according to statistical models.
(More)