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projects:year8:8a.004.uva [2019/09/25 17:08]
sally.johnson [Table]
projects:year8:8a.004.uva [2019/10/21 17:12] (current)
matt.delcambre
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 </WRAP> </WRAP>
 ===== Project - Team ===== ===== Project - Team =====
-^ Team Member    ^ Role                ^ Email              ^ Phone Number  ^ Academic Site/IAB       ^ +^ Team Member    ^ Role                ^ Email                ^ Phone Number    ^ Academic Site/IAB       ^ 
-| Peter Beling   | PI                  | pb3a@virginia.edu                | University of Virginia +| Peter Beling   | PI                  | pb3a@virginia.edu    (804) 982-2066  | University of Virginia 
-| Stephen Adams  | Research Scientist                                  | University of Virginia +| Stephen Adams  | Research Scientist sca2c@virginia.edu   (757) 870-4954  | University of Virginia 
-| Alex Langevin  | Student                                              | University of Virginia +| Alex Langevin  | Student             arl4zk@virginia.edu  Not available   | University of Virginia 
-|                |                                                      | Funded by: **U-ANGEL**  |+|                |                                                          | Funded by: **U-ANGEL**  |
 ===== Project - Novelty of Approach ===== ===== Project - Novelty of Approach =====
 <WRAP leftalign box > <WRAP leftalign box >
  
-The usability of data-driven decision-making solutions in safety-critical applications is limited by the absence of human-like inference and reasoning about decisions. Moreover, most models overlook the possibility of more than one feasible decision and end up optimizing on average behavior. This project is expected to improve decision-making models by incorporating human expert knowledge and interpret ability in the model. The project will focus on maritime domain that has different conditions from land or air based research projects focusing on autonomous agents.+This project seeks to determine the feasibility and performance of feature occlusion and other techniques as general methods of explainable AI with test case implementation in IAB member defined settings
  
  
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 ===== Project - Deliverables ====== ===== Project - Deliverables ======
  
-^    ^ Deliverables                       +^    ^ Deliverables                                                                    
-| 1  | Briefing of model design           +| 1  | Source code                                                                     
-| 2  | Literature review document         | +| 2  | Report of results and list of significant predictive features and interactions  |
-| 3  | Software and associated artifacts  +
-| 4  | Final report                       |+
 ===== Project - Benefits to IAB ===== ===== Project - Benefits to IAB =====
 <WRAP leftalign box > <WRAP leftalign box >
    
-This project will directly inform technology development for autonomous systems. It will benefit any IAB member that is developing autonomous agents.+  * Improved model accuracy while maintaining interpretability 
 +  * Ability to explain model results where legally or ethically required 
 +  * In commercial or engineering settings, improved business intelligence and diagnostic data gathering
  
 </WRAP> </WRAP>
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 {{filelist>*8a.004.uva*&sort=mtime&style=table&tableheader=1&showdate=1&showsize=1}} {{filelist>*8a.004.uva*&sort=mtime&style=table&tableheader=1&showdate=1&showsize=1}}
 +
 +===== Life Form Feedback =====
 +
 +<doodle
 +  title="Year 8 Project Poster Session Feedback (Fall 2019)"
 +  auth="user"
 +  adminUsers="matt.delcambre|sally.johnson"
 +  adminGroups="admin"
 +  voteType="single"
 +  fieldwidth="auto|123px"
 +  closed=“2019-11-05 14:00:00"
 +  printUser="fullname"
 +  showMode="all"
 +  showSum="true"
 +  userlist="vertical"
 +>
 +   * Great Progress 
 +   * On Course
 +   * Needs Change
 +   * Off Course
 +   * Abstain
 +</doodle>
 +
  
 ~~DISCUSSION~~ ~~DISCUSSION~~
projects/year8/8a.004.uva.1569449280.txt.gz · Last modified: 2019/09/25 17:08 by sally.johnson