Multi-Agent Simulation of the Battle of Ankara, 1402
By Ruili Tang
BA Honors Thesis, Union College – Schenectady, NY, 2017
Battle of Ankara, Mughal painting
Abstract: In 1402, at the north of the city of Ankara, Turkey, a battle between Ottoman Empire and Tamerlane Empire decided the fate of Europe and Asia. Although historians largely agree on the general battle procedure, the details are still open to dispute.
Several factors may have contributed to the Ottoman defeat, such as the overwhelming size of Tamerlane’s army, poisoned water, the tactical formations of the military units, and betrayal by the Tartar cavalry in the Ottoman left wing.
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The approach is divided into two stages: the simulation stage, which provides data to analyze the complex interactions of autonomous agents, and the analysis stage, which uses data mining to examine the battle outcomes. The simulation is built on a finite state machine to evaluate the current situation of each agent and then choose the most appropriate action. To achieve historical accuracy, the simulation takes into account the topography of the battlefield, line-of-sight issues, period-specific combat tactics, and the armor and weapons used by the various military units at that time. The analysis stage uses WEKAs Attribute Selection Classifier to evaluate the association strength between the battle outcome and the various factors that historians consider crucial to the outcome.
Multi-Agent Simulation of the Battle of Ankara, 1402
By Ruili Tang
BA Honors Thesis, Union College – Schenectady, NY, 2017
Abstract: In 1402, at the north of the city of Ankara, Turkey, a battle between Ottoman Empire and Tamerlane Empire decided the fate of Europe and Asia. Although historians largely agree on the general battle procedure, the details are still open to dispute.
Several factors may have contributed to the Ottoman defeat, such as the overwhelming size of Tamerlane’s army, poisoned water, the tactical formations of the military units, and betrayal by the Tartar cavalry in the Ottoman left wing.
The approach is divided into two stages: the simulation stage, which provides data to analyze the complex interactions of autonomous agents, and the analysis stage, which uses data mining to examine the battle outcomes. The simulation is built on a finite state machine to evaluate the current situation of each agent and then choose the most appropriate action. To achieve historical accuracy, the simulation takes into account the topography of the battlefield, line-of-sight issues, period-specific combat tactics, and the armor and weapons used by the various military units at that time. The analysis stage uses WEKAs Attribute Selection Classifier to evaluate the association strength between the battle outcome and the various factors that historians consider crucial to the outcome.
Click here to read this thesis from Union College
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