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The HyDE team is currently focusing on the following problems

  • Incorporation of Bayesian Network Inference in the HyDE Reasoning Scheme - This involves using Bayesian networks for Candidate Generation. The bayesian network should be automatically generated from the existing models and the reasoning should query the Bayesian Network for the next best candidate when new candidates are needed.
  • Improvement of timing and memory performance of HyDE guided by real mission requirements - This involves a analysis of HyDE's timing and memory performance through profiling and identifying ways to improve the performance of the elements that take the most memory and time. Performance can also be improved by implementing a Lazy Candidate Generation algorithm that generates candidates only when asked.
  • Addressing expected flight requirements - We are pro-actively identifying possible flight requirements and trying to make HyDE ready to satisfy those requirements. Some areas we have identified are exception handling, real-time OS operation, use of STL etc.
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