Influence map (IM) is often used as a decision supporting technology in game artificial intelligence (AI). However, the traditional influence map does not describe dynamic information. Some improved IM models can describe dynamic information, but not accurately enough. When an object moves, it would produce large influence in its moving direction than other directions. Therefore, the influence produce by the object to a location depends on the relation between the location and the object's moving direction. This paper proposed a dynamic influence map model based on distance adjustment, DADIM. This model produces different influence values in different direction by adjusting the “distance” between two locations. This method can encode dynamic information into the influence map easily. Experiments show this model avoids the weakness of dynamic influence map with location prediction. Compared with other influence maps, this model could improve the performance of the game AI with time complexity being unchanged.

DADIM: A distance adjustment dynamic influence map model / Lu, X.; Wang, X.; Lio, P.; Hui, P.. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 112:(2020), pp. 1122-1130. [10.1016/j.future.2020.06.020]

DADIM: A distance adjustment dynamic influence map model

Lio P.;
2020

Abstract

Influence map (IM) is often used as a decision supporting technology in game artificial intelligence (AI). However, the traditional influence map does not describe dynamic information. Some improved IM models can describe dynamic information, but not accurately enough. When an object moves, it would produce large influence in its moving direction than other directions. Therefore, the influence produce by the object to a location depends on the relation between the location and the object's moving direction. This paper proposed a dynamic influence map model based on distance adjustment, DADIM. This model produces different influence values in different direction by adjusting the “distance” between two locations. This method can encode dynamic information into the influence map easily. Experiments show this model avoids the weakness of dynamic influence map with location prediction. Compared with other influence maps, this model could improve the performance of the game AI with time complexity being unchanged.
2020
Decision marking; Decision support; Game artificial intelligence; Influence map
01 Pubblicazione su rivista::01a Articolo in rivista
DADIM: A distance adjustment dynamic influence map model / Lu, X.; Wang, X.; Lio, P.; Hui, P.. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 112:(2020), pp. 1122-1130. [10.1016/j.future.2020.06.020]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1719703
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