Cognitive computing for coalition situational understanding

The term cognitive computing (CC) refers to computer systems that harness multiple techniques from artificial intelligence (AI) and signal processing (SP). Situational understanding (SU) involves creating and reasoning about models of an environment and events. Coalition operations are defined by multiple partners seeking to achieve a common purpose. This paper characterises the SU problem in a coalition operations context — coalition situational understanding (CSU) — in terms of a set of problem attributes. The paper argues that CSU problems require CC system solutions involving a hybrid of AI and SP approaches. The paper outlines some of the architectural choices for CC CSU systems.

[1]  Marek J. Sergot,et al.  A logic-based calculus of events , 1989, New Generation Computing.

[2]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[3]  John E. Laird,et al.  The Soar Cognitive Architecture , 2012 .

[4]  Stanley Shostak.  Smart Machines: IBM’s Watson and the Era of Cognitive Computing , 2016 .

[5]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[6]  Peter Szolovits,et al.  What Is a Knowledge Representation? , 1993, AI Mag..

[7]  Alun D. Preece,et al.  Distributed opportunistic sensing and fusion for traffic congestion detection , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[8]  Léon Bottou,et al.  From machine learning to machine reasoning , 2011, Machine Learning.

[9]  Alun D. Preece,et al.  Human-machine conversations to support multi-agency missions , 2014, MOCO.

[10]  David L. Hall,et al.  Human-Centered Information Fusion: Artech House Electronic Warfare Library , 2010 .

[11]  Erik Blasch,et al.  Level 5 (User Refinement) issues supporting Information Fusion Management , 2006, 2006 9th International Conference on Information Fusion.

[12]  Christian Jutten,et al.  Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects , 2015, Proceedings of the IEEE.

[13]  Joshua B. Tenenbaum,et al.  Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.

[14]  Tien Pham,et al.  Intelligence, Surveillance, and Reconnaissance fusion for coalition operations , 2008, 2008 11th International Conference on Information Fusion.

[15]  Regina Barzilay,et al.  Rationalizing Neural Predictions , 2016, EMNLP.

[16]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[17]  Kang Chen,et al.  Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual information , 2014, ACM Trans. Graph..

[18]  Alexander Artikis,et al.  A probabilistic logic programming event calculus , 2012, Theory and Practice of Logic Programming.

[19]  John R. Anderson.  How Can the Human Mind Occur in the Physical Universe , 2007 .

[20]  Alexander Binder,et al.  Explaining nonlinear classification decisions with deep Taylor decomposition , 2015, Pattern Recognit..

[21]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .

[22]  Ramanathan V. Guha,et al.  Towards a Model Theory for Distributed Representations , 2014, AAAI Spring Symposia.

[23]  Loren G. Terveen,et al.  Overview of human-computer collaboration , 1995, Knowl. Based Syst..

[24]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[25]  Murat Sensoy,et al.  Human-in-the-loop situational understanding via subjective Bayesian networks , 2017 .