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Are lasers the future of anti-drone warfare?

Al Jazeera

Are lasers the future of anti-drone warfare? A drone appears on the grainy, gray-scaled image of the thermal camera. This is the type of drone used by groups such as Hezbollah, Hamas and the Yemeni Houthis. Seconds later, the wing of the drone snaps off, sending it tumbling down, exploding when it hits the ground. This is a video shared by the Israeli Ministry of Defence and arms producer Rafael, a hint towards the future of anti-drone warfare.





The Learnability of In-Context Learning

Neural Information Processing Systems

Our theoretical analysis reveals that in this setting, in-context learning is more about identifying the task than about learning it, a result which is in line with a series of recent empirical findings.



Ex ante coordination and collusion in zero-sum multi-player extensive-form games

Neural Information Processing Systems

Recent milestones in equilibrium computation, such as the success of Libratus, show that it is possible to compute strong solutions to two-player zero-sum games in theory and practice. This is not the case for games with more than two players, which remain one of the main open challenges in computational game theory. This paper focuses on zero-sum games where a team of players faces an opponent, as is the case, for example, in Bridge, collusion in poker, and many non-recreational applications such as war, where the colluders do not have time or means of communicating during battle, collusion in bidding, where communication during the auction is illegal, and coordinated swindling in public. The possibility for the team members to communicate before game play--that is, coordinate their strategies ex ante--makes the use of behavioral strategies unsatisfactory. The reasons for this are closely related to the fact that the team can be represented as a single player with imperfect recall. We propose a new game representation, the realization form, that generalizes the sequence form but can also be applied to imperfect-recall games. Then, we use it to derive an auxiliary game that is equivalent to the original one. It provides a sound way to map the problem of finding an optimal ex-antecoordinated strategy for the team to the well-understood Nash equilibrium-finding problem in a (larger) two-player zero-sum perfect-recall game. By reasoning over the auxiliary game, we devise an anytime algorithm, fictitious team-play, that is guaranteed to converge to an optimal coordinated strategy for the team against an optimal opponent, and that is dramatically faster than the prior state-of-the-art algorithm for this problem.