Algorithmic Tools for Adversarial Games

Securboration, working with University of Connecticut researchers Dr. Eugene Santos Jr. is pleased to propose the Dynamic Adversarial Gaming Algorithm (DAGA). DAGA will focus on expanding adversarial gaming algorithms to support an agent based dynamic adversarial environment. DAGA will provide the following innovation to the adversarial gaming domain. 1) a natural mechanism to dynamically control the game based on current observables. 2) Support the interaction between agents through Web Ontology Language (OWL) based Common Operating Ontology. 3) Provided the ability for agent to split into multiple sub-agents as the population being represented diverges. 4) The use of Episodic learning to effect the group's behavior based on its experience over a period of time. Each of the listed innovation are required to support asymmetric adversarial games that represent the interaction between blue forces, red forces, and their interaction to influence green forces. The overriding goal of the DAGA service is to make accurate predication centered on a given actions ability to influence a "community of interest" to achieve a desired effect. The use of Bayesian Knowledge Fragments leverages the prediction strength of Bayesian base algorithms, along with the ability to account for prior knowledge in the prediction. In addition Bayesian Knowledge Fragments avoid the computationally cost and complexity of developing probability table typically associated with Bayesian approaches.