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The United States Air Force has described aerial refueling as a key capability. Currently, the Air Force uses two aircraft for aerial refueling: the KC-135 and the KC-10. While the KC-10 fleet has an average age greater than 20 years, the KC-135 fleet averages more than 46 years and is the oldest combat weapon system in the Air Force inventory. Consequently, the Air Force intends to replace or recapitalize the KC-135 first. According to Air Force officials, the recapitalization process may cost between $72 billion and $120 billion and will span decades. Because of broad congressional interest, the Government Accountability Office (GAO) is currently reviewing, under the Comptroller General's authority to conduct evaluations on his own initiative, the Analysis of Alternatives for the recapitalization of the KC-135 aircraft. On December 15, 2006, the GAO briefed congressional staff on our preliminary observations. This letter expands on the information discussed in that briefing and includes recommendations to the Secretary of Defense. The GAO plans to complete its review of the Analysis of Alternatives and report the results in early summer 2007., Report to Congressional committees.

The Architecture Tradeoff Analysis Method(Service Marks) (ATAM(Service Marks)) is an architecture evaluation technique currently evolving at the Software Engineering Institute (SEI). ATAM has been applied to a number of command and control, real time, and information systems. As collaborative, autonomous agents become a significant software technology, the demand for evaluating the quality attributes of the architectures of agent based systems will increase. Very broadly, agents may be thought of as software entities that have the ability to undertake action autonomously in their particular embedded environment, according to a typically general set of requests or desired goals, and that are able to communicate with other agents as determined by their own initiative. Given an agent system architecture, we need scenarios that could be applicable for conducting ATAM evaluations on instances of that agent architecture. This report identifies a few features in agent based systems that could be used to classify agent system architectures and to guide the generation of scenarios applicable to these architectures.

The process of understanding a source code in a high-level programming language involves complex computation. Given a piece of legacy code and a library of program plan templates, understanding the code corresponds to building mappings from parts of the source code to particular program plans. These mappings could be used to assist an expert in reverse engineering legacy code, to facilitate software reuse, or to assist in the translation of the source into another programming language. In this paper we present a model of program understanding using constraint satisfaction. Within this model we intelligently compose a partial global picture of the source program code by transforming knowledge about the problem domain and the program itself into sets of constraints. We then systematically study different search algorithms and empirically evaluate their performance. One advantage of the constraint satisfaction model is its generality; many previous attempts in program understanding could now be cast under the same spectrum of heuristics, and thus be readily compared. Another advantage is the improvement in search efficiency using various heuristic techniques in constraint satisfaction.

We extend the hierarchical, precondition-elimination abstraction of Abstrips to nonlinear, least-commitment planners such as Tweak. Specifically, we show that the combined planning system, AbTweak, satisfies the monotonic property, whereby the existence of a lowest level solution the implies the existence of a highest level solution that is structurally similar to II. This property enables one to prune a considerable amount of the search space without loss of completeness. In addition, we develop a criteria for good abstraction hierarchies, and develop a novel, complete search strategy called Left,Wedge that is optimized for good abstraction hierarchies. We demonstrate the utility of both the monotonic property and the Left-Wedge strategy through a series of empirical tests.