Name
Abstract | ||
|---|---|---|
Engagement-level simulation is a quantitative way to evaluate the effectiveness of weapon systems before construction and acquisition, minimizing the risk of investment. Though contractors have built simulation systems with high fidelity models of weapon systems and battlefields, developing competent tactics to give full play to new weapon systems in simulation experiments is labor intensive, as most classical tactics tend to be out of date. In this work, we proposed a tactics exploration framework (TEF) that applied grammar-based genetic programming (GP) to generating and evolving tactics in the engagement-level simulation. Tactics are represented with modular behavior trees (BTs) for compatibility with the genetic operators. Experiments to explore submarine tactics have been conducted to observe and study the exploration process. The experimental results show that the TEF based on GP is efficient to explore tactics in the formalism of BTs. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.2991/ijcis.2017.10.1.53 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS |
Keywords | Field | DocType |
tactics exploration framework, grammar-based genetic programming, behavior trees, submarine warfare simulation | Computer science,Inductive programming,Genetic programming,Artificial intelligence,Behavior Trees,Machine learning | Journal |
Volume | Issue | ISSN |
10 | 1 | 1875-6891 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||