Appropriate methods include indexing, retrieving, and reusing past problem-solving episodes; encoding problem-solving methods from instruction or observation; explanation-based methods; inferring or suggesting improved problem-solving methods; enhancing the efficiency of algorithms based on past problem solving; inductively inferring regularities using, for example, graphical models (Bayesian networks); reinforcing learning methods; understanding emergent behavior; transferring lessons learned in one domain to another domain; using multiagent teams to learn how groups collaborate to solve problems; integrating multiple machine learning techniques; developing toolkits and frameworks that integrate and facilitate adoption of learning methods and provide guidance on the best methods to use in different situations; and experimenting in domains of Air Force relevance.

  • Research on the modeling and simulation of scenarios of importance to the Air Force, including more rapid and cost-effective construction of domain-faithful models of information and influence operations; support for the authoring of scenarios and their translation into the models and databases required for simulations; and software tools to allow users to tailor and understand the behavior and functionality of software components.

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