Learning Effective Changes for Software Projects

The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by "planning" about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu

A study for active control research and validation using the Total In-Flight Simulator (TIFS) aircraft

The results of a feasibility study and preliminary design for active control research and validation using the Total In-Flight Simulator (TIFS) aircraft are documented. Active control functions which can be demonstrated on the TIFS aircraft and the cost of preparing, equipping, and operating the TIFS aircraft for active control technology development are determined. It is shown that the TIFS aircraft is as a suitable test bed for inflight research and validation of many ACT concepts

Cogeneration computer model assessment: Advanced cogeneration research study

Cogeneration computer simulation models to recommend the most desirable models or their components for use by the Southern California Edison Company (SCE) in evaluating potential cogeneration projects was assessed. Existing cogeneration modeling capabilities are described, preferred models are identified, and an approach to the development of a code which will best satisfy SCE requirements is recommended. Five models (CELCAP, COGEN 2, CPA, DEUS, and OASIS) are recommended for further consideration

Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a lookup table for the world model and implement the Q function with a neural net. Time limitations prevented the combination of these two approaches. The final section discusses the results and gives clues for future work

DYNAMIC COMPLEMENTARITY IN EXPORT PROMOTION: THE MARKET ACCESS PROGRAM IN FRUITS AND VEGETABLES

Government-supported promotion in foreign markets may justified when market failures exist, such as spillover externalities, where promotion of one commodity positively influences exports of another, or when market uncertainties cause planning horizons to be shorter than the persistent effects of promotion. A dynamic model of U.S. apple, almond, grape, and wine export supply is developed to test for these market failures. Promotion is viewed as an investment in establishing and maintaining a productÂ’'s image. Evidence supporting the existence of each market failure is found. Exporters and program administrators may fail to account for them in export promotion planning.International Relations/Trade,