Winning strategies for robotic wars: defense applications of linguistic geometry

2000 ◽  
Vol 4 (3) ◽  
pp. 148-155 ◽  
Author(s):  
Boris Stilman ◽  
Vladimir Yakhnis ◽  
Oleg Umansky
Author(s):  
Boris Stilman ◽  
◽  
Vladimir Yakhnis ◽  
Oleg Umanskiy ◽  

This paper describes the current status of research and applications of Linguistic Geometry (LG), a type of game theory scalable to the level of real life defense systems. LG is compared to conventional gaming approaches with respect to their applicability to real world problems. LG generates winning strategies for all sides in a conflict in real time by constructing them out of a limited set of blocks called zones. Several examples of zones are introduced. The paper describes the process of discovery of new zones essential for various types of military operations, which represent different classes of abstract board games. We also included a brief explanation of scalability of the LG applications. This paper describes a universal tool kit, LG-PACKAGE, which allows a user to build his/her own applications of LG. This tool kit includes GDK (Game Development Kit), GIK (Game Integration Kit), GRT (Game Resource Tool), GST (Game Solving Tool), GNS (Game Network Services) and GMI (Game Mobile Interface). In the end we included a description of the most advanced experiments with the LG applications conducted by DARPA and US Army and assessments of these experiments by military experts.


Author(s):  
Natasha Alechina ◽  
Hans van Ditmarsch ◽  
Rustam Galimullin ◽  
Tuo Wang

AbstractCoalition announcement logic (CAL) is one of the family of the logics of quantified announcements. It allows us to reason about what a coalition of agents can achieve by making announcements in the setting where the anti-coalition may have an announcement of their own to preclude the former from reaching its epistemic goals. In this paper, we describe a PSPACE-complete model checking algorithm for CAL that produces winning strategies for coalitions. The algorithm is implemented in a proof-of-concept model checker.


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