Decision Support Based on Human-Machine Collective Intelligence: Major Challenges

The paper discusses a novel class of decision support systems, based on an environment, leveraging human-machine collective intelligence. The distinctive feature of the proposed environment is support for natural self-organization processes in the community of participants. Most of the existing approaches for leveraging human expertise in a computing system rely on a pre-defined rigid workflow specification, and those very few systems that try to overcome this limitation sidestep current body of knowledge of self-organization in artificial and natural systems. The paper outlines the general vision of the proposed environment, identifies main challenges that has to be dealt with in order to develop such environment and describes ways to address them. Potential applications of such decision support environment are ubiquitous and influence virtually all areas of human activities, especially in complex domains: business management, environment problems, and government decisions.

[1]  Björn Hartmann,et al.  Turkomatic: automatic recursive task and workflow design for mechanical turk , 2011, Human Computation.

[2]  Nikolay Shilov,et al.  Service-Based Socio-Cyberphysical Network Modeling for Guided Self-Organization , 2015, CENTERIS/ProjMAN/HCist.

[3]  Michael S. Bernstein,et al.  Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations , 2017, CHI.

[4]  Maurice H. ter Beek,et al.  Guest Editorial for the Special Issue on FORmal methods for the quantitative Evaluation of Collective Adaptive SysTems (FORECAST) , 2018, ACM Trans. Model. Comput. Simul..

[5]  Daniel Schall,et al.  Service-Oriented Crowdsourcing: Architecture, Protocols and Algorithms , 2012 .

[6]  Daniel Schall Service Oriented Protocols for Human Computation , 2013, Handbook of Human Computation.

[7]  Alessandro Bozzon,et al.  Pattern-Based Specification of Crowdsourcing Applications , 2014, ICWE.

[8]  Ece Kamar,et al.  Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence , 2016, IJCAI.

[9]  Björn Hartmann,et al.  Collaboratively crowdsourcing workflows with turkomatic , 2012, CSCW.

[10]  Stefaan Verhulst Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern , 2018, AI & SOCIETY.

[11]  Abraham Bernstein,et al.  CrowdLang: A Programming Language for the Systematic Exploration of Human Computation Systems , 2012, SocInfo.

[12]  Michael S. Bernstein,et al.  No Workflow Can Ever Be Enough , 2017, Proc. ACM Hum. Comput. Interact..

[13]  V. I. Gorodetskii Self-organization and multiagent systems: I. Models of multiagent self-organization , 2012, Journal of Computer and Systems Sciences International.

[14]  Markus Stumptner,et al.  Configuration knowledge representations for Semantic Web applications , 2003, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[15]  Eric Horvitz,et al.  On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems , 2016, AAAI.

[16]  Tim Kraska,et al.  CrowdDB: answering queries with crowdsourcing , 2011, SIGMOD '11.

[17]  Kwong-Sak Leung,et al.  TaskRec: A Task Recommendation Framework in Crowdsourcing Systems , 2015, Neural Processing Letters.

[18]  Greg Little Exploring iterative and parallel human computation processes , 2010, CHI EA '10.

[19]  Fabio Casati,et al.  Modeling, Enacting, and Integrating Custom Crowdsourcing Processes , 2015, TWEB.

[20]  Daniel Schall,et al.  Service-Oriented Crowdsourcing , 2012, SpringerBriefs in Computer Science.

[21]  Daniel C. Richardson,et al.  The self organization of human interaction , 2013 .

[22]  Peng Dai,et al.  Artificial Intelligence for Artificial Artificial Intelligence , 2011, AAAI.

[23]  Pieter Abbeel,et al.  Apprenticeship learning via inverse reinforcement learning , 2004, ICML.

[24]  Marina Kogan,et al.  Digital Traces of Online Self-Organizing and Problem Solving in Disaster , 2016, GROUP.

[25]  Hervé Panetto,et al.  Semantic annotations for semantic interoperability in a product lifecycle management context , 2016 .

[26]  Peng Dai,et al.  Decision-Theoretic Control of Crowd-Sourced Workflows , 2010, AAAI.

[27]  Jacob Beal,et al.  Engineering Resilient Collective Adaptive Systems by Self-Stabilisation , 2017, ACM Trans. Model. Comput. Simul..

[28]  Alexis Battle,et al.  The jabberwocky programming environment for structured social computing , 2011, UIST.