Learned Provability Likelihood for Tactical Search

Adversarially Learned Likelihood-ratio

We link the reverse KL divergence with adversarial learning. This insight enables learning to synthesize realistic samples in two settings: (i) Given a set of samples from the true distribution, an adversarially learned likelihood-ratio and a new entropy bound are used to learn a GAN model, that improves synthesized sample quality relative to previous GAN variants. (ii) Given an unnormalized di...

Tactical Strategies to Search for Scarce

This thesis investigates search scenarios in which multiple mobile, self-interested agents, cows in our case, compete to capture targets. The problems considered in this thesis address search strategies that reflect (i) the need to efficiently search for targets given a prior on their location, and (ii) an awareness that the environment in which searching takes place contains other self-interes...

Tactical Synthesis of Efficient Global Search Algorithms

Algorithm synthesis transforms a formal specification into an efficient algorithm to solve a problem. Algorithm synthesis in Specware combines the formal speicifcation of a problem with a highlevel algorithm strategy. To derive an efficient algorithm, a developer must define operators that refine the algorithm by combining the generic operators in the algorithm with the details of the problem s...

Automated Reasoning Across Tactical Stories to Derive Lessons Learned

The Military Analogical Reasoning System (MARS) is a performance support system and decision aid for commanders in Tactical Operations Centers. MARS enhances and supports the innate human ability for using stories to reason about tactical goals, plans, situations, and outcomes. The system operates by comparing many instances of stored tactical stories, determining which have analogous situation...

Leadership lessons learned in Tactical Combat Casualty Care.