QuEST for information fusion

Qualia-based Exploitation of Sensing Technology (QuEST) is an approach to create a cognitive exoskeleton to improve human-machine decision quality. In this paper, we discuss the motivation of QuEST as it pertains to man-machine information fusion. Operator-based situation awareness includes both elements of external sensory perception and internal cognitive explanation. We outline QuEST elements and tenets towards a reasoning approach that achieves human intelligence amplification (IA) as opposed to data aggregation from machine artificial intelligence (AI). In a use case example for automatic target exploitation, we showcase the need for enhanced understanding of the man (mind-body cognition) and the machine (sensor-based reasoning) for establishing a cohesive narrative of situational activities.

[1]  Byron J. Pierce,et al.  System Dynamics Modeling of Sensory-Driven Decision Priming , 2013 .

[2]  Kenneth W. Bauer,et al.  The life and death of ATR/sensor fusion and the hope for resurrection , 2008, SPIE Defense + Commercial Sensing.

[3]  M. Oxley,et al.  Probabilistic situations for reasoning , 2012, 2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support.

[4]  Erik Blasch,et al.  Towards unbiased evaluation of uncertainty reasoning: The URREF ontology , 2012, 2012 15th International Conference on Information Fusion.

[5]  B. V. K. Vijaya Kumar,et al.  Maximum Margin Correlation Filter: A New Approach for Localization and Classification , 2013, IEEE Transactions on Image Processing.

[6]  Vilayanur S. Ramachandran,et al.  Three laws of qualia: what neurology tells us about the biological functions of consciousness , 1997 .

[7]  Kannappan Palaniappan,et al.  Contemporary concerns in Geographical/Geospatial Information Systems (GIS) processing , 2011, Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON).

[8]  Audun Jøsang,et al.  URREF self-confidence in information fusion trust , 2014, 17th International Conference on Information Fusion (FUSION).

[9]  Erik Blasch,et al.  Pattern Activity Clustering and Evaluation (PACE) , 2012, Defense + Commercial Sensing.

[10]  Erik Blasch,et al.  Activity recognition using Video Event Segmentation with Text (VEST) , 2014, Defense + Security Symposium.

[11]  Jonathan Evans,et al.  Science Perspectives on Psychological , 2022 .

[12]  C. Cowell,et al.  Minds, Machines and Qualia: A Theory of Consciousness , 2001 .

[13]  Gary Klein,et al.  Making Sense of Sensemaking 1: Alternative Perspectives , 2006, IEEE Intelligent Systems.

[14]  E. Blasch,et al.  Assembling a distributed fused information-based human-computer cognitive decision making tool , 2000, IEEE Aerospace and Electronic Systems Magazine.

[15]  Mica R. Endsley,et al.  Measurement of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[16]  Eloi Bosse,et al.  High-Level Information Fusion Management and System Design , 2012 .

[17]  Erik Blasch,et al.  Joint data management for MOVINT data-to-decision making , 2011, 14th International Conference on Information Fusion.

[18]  Jens Rasmussen,et al.  Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[19]  Robert F. Mills,et al.  Narratives as a Fundamental Component of Consciousness , 2014, CMN.

[20]  Genshe Chen,et al.  Context aided video-to-text information fusion , 2014, 17th International Conference on Information Fusion (FUSION).

[21]  E. Blasch,et al.  Sensor Management Fusion Using Operating Conditions , 2008, 2008 IEEE National Aerospace and Electronics Conference.

[22]  Erik Blasch,et al.  Extraction of Semantic Activities from Twitter Data , 2013, STIDS.

[23]  Zhonghai Wang,et al.  Video-based activity analysis using the L1 tracker on VIRAT data , 2013, 2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[24]  S. Cooper,et al.  Computing Machinery and Intelligence , 2013 .

[25]  Gilbert Harman Some philosophical issues in cognitive science: quality, intentionality, and the mind-body problem , 1989 .

[26]  Chun Yang,et al.  Kalman Filtering with Nonlinear State Constraints , 2009 .

[27]  Eliot R. Smith,et al.  Dual-Process Models in Social and Cognitive Psychology: Conceptual Integration and Links to Underlying Memory Systems , 2000 .

[28]  Kevin A. Gluck,et al.  A quantification of robustness , 2013 .

[29]  Erik Blasch,et al.  Information fusion for information superiority , 2000, Proceedings of the IEEE 2000 National Aerospace and Electronics Conference. NAECON 2000. Engineering Tomorrow (Cat. No.00CH37093).

[30]  Erik Blasch,et al.  Visualization of graphical information fusion results , 2014, Defense + Security Symposium.

[31]  James Llinas,et al.  High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges , 2012, IEEE Aerospace and Electronic Systems Magazine.

[32]  Erik Blasch Book review [3C Vision: Cues, Content, and Channels (Cantoni, V. et al.; 2011)] , 2013 .

[33]  John J. Salerno,et al.  Summary of human social, cultural, behavioral (HSCB) modeling for information fusion panel discussion , 2013, Defense, Security, and Sensing.

[34]  I. Kadar,et al.  Resource management coordination with level 2/3 fusion issues and challenges [Panel Report] , 2008, IEEE Aerospace and Electronic Systems Magazine.

[35]  Robert Earl Patterson,et al.  Training Intuitive Decision Making in a Simulated Real-World Environment , 2013, Hum. Factors.

[36]  Laurie Fenstermacher,et al.  Information fusion: telling the story (or threat narrative) , 2014, Defense + Security Symposium.

[37]  Nadya Belov,et al.  Plan-Driven Fusion: Shaping the Situation Awareness Process using Empirical Plan Data , 2006, 2006 9th International Conference on Information Fusion.

[38]  Wright-Patterson Afb,et al.  Modeling Intuitive Decision Making in ACT-R , 2012 .

[39]  Jeffrey L. Vagle,et al.  Cognitive Agents for Sense and Respond Logistics , 2005, DAMAS.

[40]  Tuong Le,et al.  Bridging Semantic eGovernment Applications using Ontology-to-Ontology Message Translation , 2006, AAAI Spring Symposium: Semantic Web Meets eGovernment.

[41]  S. Plano,et al.  DFIG level 5 (user refinement) issues supporting situational assessment reasoning , 2005, 2005 7th International Conference on Information Fusion.

[42]  Erik Blasch,et al.  Dynamic Data Driven Applications Systems (DDDAS) modeling for automatic target recognition , 2013, Defense, Security, and Sensing.

[43]  Erik Blasch,et al.  Enhanced air operations using JView for an air-ground fused situation awareness udop , 2013, 2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC).

[44]  S. Chaiken,et al.  Dual-process theories in social psychology , 1999 .

[45]  Gary Klein,et al.  Implicit Learning, Tacit Knowledge, Expertise Development, and Naturalistic Decision Making , 2010 .

[46]  D. Dennett Quining Qualia , 1993 .

[47]  Erik Blasch,et al.  Multi-source Multi-modal Activity Recognition in Aerial Video Surveillance , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[48]  Erik Blasch,et al.  Measures of effectiveness for high-level fusion , 2010, 2010 13th International Conference on Information Fusion.

[49]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[50]  Erik Blasch,et al.  Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance , 2014, Sensors.

[51]  Anna L. Buczak,et al.  Dynamic Agent Composition from Semantic Web Services , 2004, SWDB.

[52]  Genshe Chen,et al.  Information fusion in a cloud computing era: A systems-level perspective , 2014, IEEE Aerospace and Electronic Systems Magazine.

[53]  Erik Blasch,et al.  Decisions-to-Data using Level 5 information fusion , 2014, Defense + Security Symposium.

[54]  Erik Blasch,et al.  Revisiting the JDL model for information exploitation , 2013, Proceedings of the 16th International Conference on Information Fusion.

[55]  Erik Blasch,et al.  Issues and Challenges in Situation Assessment (Level 2 Fusion) , 2006, J. Adv. Inf. Fusion.