Cognitive Architecture for Adaptive Social Robotics

We describe a general adaptive cognitive architecture that is applicable to a wide variety of situations from video surveillance to robotics. A cognitive system must have a set of built-in motivations or primary goals to drive its behavior. In addition, the system must have an adequate model of the motivations and primary goals of humans to be able to interact effectively with humans. Rapid causal learning is used to learn about the causalities in the world, which in turn form the grounded knowledge necessary for problem solving to achieve these primary goals as well as secondary goals derived from them. A general adaptive cognitive system using this architecture has the ability to understand human motivation and emotion, and observe and learn from the environment and human behavior. This architecture is an ideal platform for adaptive social robotics.

[1]  Edmund T. Rolls,et al.  Memory, Attention, and Decision-Making , 2007 .

[2]  Song-Chun Zhu,et al.  Learning Perceptual Causality from Video , 2013, AAAI Workshop: Learning Rich Representations from Low-Level Sensors.

[3]  Manfred Pinkal,et al.  Learning Script Knowledge with Web Experiments , 2010, ACL.

[4]  Seng-Beng Ho Principles of Noology: Toward a Theory and Science of Intelligence , 2016 .

[5]  Cornelius J. König,et al.  Integrating Theories of Motivation , 2006 .

[6]  Roger C. Schank,et al.  SCRIPTS, PLANS, GOALS, AND UNDERSTANDING , 1988 .

[7]  Kewei Tu,et al.  Joint Video and Text Parsing for Understanding Events and Answering Queries , 2013, IEEE MultiMedia.

[8]  Yunde Jia,et al.  Parsing video events with goal inference and intent prediction , 2011, 2011 International Conference on Computer Vision.

[9]  Seng-Beng Ho,et al.  On Effective Causal Learning , 2014, AGI.

[10]  Seng-Beng Ho Principles of Noology , 2016, Socio-Affective Computing.

[11]  Larry S. Davis,et al.  Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos , 2009, CVPR.

[12]  Boon-Kiat Quek,et al.  Attaining Operational Survivability in an Autonomous Unmanned Ground Surveillance Vehicle , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[13]  Seng-Beng Ho,et al.  Knowledge Representation, Learning, and Problem Solving for General Intelligence , 2013, AGI.

[14]  A. Maslow Motivation and Personality , 1954 .

[15]  Benjamin Z. Yao,et al.  Unsupervised learning of event AND-OR grammar and semantics from video , 2011, 2011 International Conference on Computer Vision.

[16]  J. Reeve,et al.  Understanding motivation and emotion , 1991 .