Home > Uncategorized > Weekly QuEST Discussion Topics and News, 3 Apr

Weekly QuEST Discussion Topics and News, 3 Apr

QuEST 3 April 2015

  • I mentioned last week that I was extending my upcoming plenary talk at the Defense sensing symposia to include not only a discussion of autonomy and specifically autonomy for offensive cyber operations to also include a discussion of autonomous ISR with a focus on persistence and coalition issues.  I would like to present the flow of the presentation for comments / insertion of ideas from the QuEST group on these topics.
  • We also want to return to the topic we briefly mentioned last week as we closed – formalism for defining the unexpected query (UQ) – taken from the transfer learning literature – specifically a 2010 survey article by Pan – A Survey of Transfer Learning, IEEE transactions on knowledge and data engineering, vol 22, no 10, oct 2010.  We want to define the term ‘query’ and then ‘unexpected query’ using their formalism and also address the question from our colleague Andres R. on how does the UQ relate to generalization?  Lastly we need to establish a position on transfer learning and consciousness.  So if one of the purposes of consciousness is to respond to the UQ – AND – transfer learning is an area of research that attempts to respond to the UQ – what is it we think consciousness (QuEST) brings to transfer learning?  In the article section 2.3 provides a means to have this discussion
  • In transfer learning, we have the following three main research issues – so what does QuEST bring to these areas?:
  • 1) What to transfer – What:  asks which part of knowledge can be transferred across domains or tasks. Some knowledge is specific for individual domains or tasks, and some knowledge may be common between different domains such that they may help improve performance for the target domain or task.  In our terms we have experience responding to queries (recall how we defined a query – as an agent capturing a stimuli and responding).  We capture those experiences for later use (knowledge).  Some knowledge is specific for very specific types of queries but some knowledge may be useful for improving performance for other task/queries.  Our previous discussions on Gist should be re-introduced here.
  • 2) How to transfer – After discovering which knowledge can be transferred,learning algorithms need to be developed to transfer the knowledge, which corresponds to the “how to transfer” issue.  Under this topic we need to enforce our ideas of situating / simulating.
  • 3) When to transfer – When:  asks in which situations, transferring skills should be done. Likewise, we are interested in knowing in which situations, knowledge should not be transferred.  In some situations, when the source domain and target domain are not related to each other, brute-force transfer may be unsuccessful. In the worst case, it may even hurt the performance of learning in the target domain, a situation which is often referred to as negative transfer.  Under this topic we need to discuss our ideas of the competing narratives to generate a stable/consistent / useful confabulation.
  • So the question becomes does consciousness provide an advantage along any or all of these transfer learning axes?

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