Paper Info
Title
A Hyper-Heuristic Framework for Agent-Based Crowd Modeling and Simulation: (Extended Abstract).
Abstract
This paper proposes a hyper-heuristic crowd modeling framework to generate realistic crowd dynamics that can match video data. In the proposed framework, motions of agents are driven by a high-level heuristic (HH) which intelligently selects way-points for agents based on the current situations. Three low-level heuristics are defined and used as building blocks of the HH. Based on the newly defined building blocks and fitness evaluation function, the Self-Learning Gene Expression Programming (SL-GEP) is utilized to automatically evolve a suitable HH. To test its effectiveness, the proposed framework is applied to learn suitable HHs based on real video data. The best HH learned is then applied to generate crowd simulations and the simulation results demonstrate that the proposed method is effective to generate realistic crowd dynamics.
Year
DOI
Venue
2016
10.5555/2936924.2937145
AAMAS
Field
DocType
Citations 
Gene expression programming,Heuristic,Computer science,Evaluation function,Genetic programming,Hyper-heuristic,Heuristics,Crowd simulation,Artificial intelligence,Crowd modeling,Machine learning
Conference
1
PageRank 
References 
Authors
0.38
3
2
Name
Order
Citations
PageRank
Jing-hui Zhong138033.00
Wentong Cai21928197.81