Bot Development for Military Wargaming Simulation
Over the years many techniques have been used by the decision makers to test their theories in a near real world simulated situation. Military war games are one such platform where these theories can be tested and the outcome is recorded. War gaming has evolved from its starting as a board game to now widely played as a computer game. This paper focuses on a military war game simulation which is being used to develop a bot using artificial intelligence techniques. Various modules are developed for working of the bot. Route planning is one of them which is used by units to find a path in the game. Thus, an algorithm is proposed using the A* algorithm as a base. A* algorithm is modified to use influence maps in order to find a safe path for the units which helps the bot succeed in the game. The modified A* algorithm is implemented and compared with a traditional A* algorithm and modified algorithm is found to be more optimal.
KeywordsReal Time Strategy War game Bot Routing Planning Path finding A* search algorithm Influence maps
The authors duly acknowledge I.S. Sharma (Scientist, ISSA, DRDO), Colonel Mukul Dhobal and University of Delhi for the support provided.
- 1.Adil, K., Jiang, F., Shaohui, Z.T., Fu, Y.: State-of-the-art and open challenges in RTS game-AI and Starcraft. IJACSA 8, 16–24 (2017)Google Scholar
- 3.Ontanón, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., Preuss, M.: RTS AI: Problems and Techniques (2015)Google Scholar
- 7.Stene, S.B.: Artificial intelligence techniques in real-time strategy games-architecture and combat behavior, Institutt for datateknikk og informasjonsvitenskap (2006)Google Scholar
- 8.Wojnicki, I., Ernst, S., Turek, W.: A robust heuristic for the multidimensional a-star/wavefront hybrid planning algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9120, pp. 282–291. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19369-4_26CrossRefGoogle Scholar
- 10.Rich, E., Knight, K.: Artificial Intelligence. McGraw-Hill, New York (2008)Google Scholar
- 11.Sturtevant, N., Buro, M.: Partial pathfinding using map abstraction and refinement. AAAI 5, 1392–1397 (2005)Google Scholar
- 13.Silver, D.: Cooperative pathfinding. In: AIIDE, vol. 1 (2005)Google Scholar
- 14.Sturtevant, N., Buro, M.: Improving collaborative pathfinding using map abstraction. In: AIIDE (2006)Google Scholar
- 15.Björnsson, Y., Halldórsson, K.: Improved heuristics for optimal path-finding on game maps. In: AIIDE, vol. 6 (2006)Google Scholar