Systematic literature review on intent-driven systems

An intent-driven system is a compositional system of human actors and machine actors. The aim of intent-driven systems is to capture stakeholders’ intents and transform these into a form that enables computer processing of the intents. Only then are different machine actors able to negotiate with each other on behalf of their respective stakeholders and their intents, and suggest a mutually beneficial collaboration. The aim is to find existing methods/techniques which could be used as building blocks to construct intent-driven systems. This is used to provide insight into what is needed to enable intent-driven systems with the help of these methods/techniques. As a part of a design science study, a Systematic Literature Review is conducted. The existences of methods/techniques which can be used as building blocks to construct intent-driven systems exist in the literature. How these methods/techniques can interact in order to enable realisations of intent-driven systems is not evident in the existing literature. The synthesis shows a need for further research regarding the semantic interchange of information, actor interaction in intent-driven systems, and the governance of intent-driven systems.

[1]  Jie Lu,et al.  An intelligent situation awareness support system for safety-critical environments , 2014, Decis. Support Syst..

[2]  Pradeep Kumar Ray,et al.  Adaptive policy framework: A systematic review , 2013, J. Netw. Comput. Appl..

[3]  Minjie Zhang,et al.  Adaptive conceding strategies for automated trading agents in dynamic, open markets , 2009, Decis. Support Syst..

[4]  Amir Masoud Rahmani,et al.  Cloud computing service negotiation: A systematic review , 2018, Comput. Stand. Interfaces.

[5]  Lei Gao,et al.  Ranking management strategies with complex outcomes: An AHP-fuzzy evaluation of recreational fishing using an integrated agent-based model of a coral reef ecosystem , 2012, Environ. Model. Softw..

[6]  Nelson Alfonso Gómez-Cruz,et al.  Agent-based simulation in management and organizational studies: a survey , 2017 .

[7]  Michael Hiete,et al.  Decision maps: A framework for multi-criteria decision support under severe uncertainty , 2011, Decis. Support Syst..

[8]  Carlos Ramos,et al.  The Fabricare system: a multi-agent-based scheduling prototype , 2004 .

[9]  Tony Gorschek,et al.  A method for evaluating rigor and industrial relevance of technology evaluations , 2011, Empirical Software Engineering.

[10]  Krzysztof Wnuk,et al.  Supporting Continuous Changes to Business Intents , 2017, Int. J. Softw. Eng. Knowl. Eng..

[11]  Sandro Morasca,et al.  Supporting the semi-automatic semantic annotation of web services: A systematic literature review , 2015, Inf. Softw. Technol..

[12]  David Naso,et al.  A genetic approach for adaptive multiagent control in heterarchical manufacturing systems , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[13]  Christopher D. Wickens,et al.  A model for types and levels of human interaction with automation , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[14]  Nicholas R. Jennings,et al.  Agents That Reason and Negotiate by Arguing , 1998, J. Log. Comput..

[15]  Esperanza Marcos,et al.  Applying MDE to the (semi-)automatic development of model transformations , 2013, Inf. Softw. Technol..

[16]  Antonio Ruiz Cortés,et al.  Building and implementing policies in autonomous and autonomic systems using MaCMAS , 2007, Innovations in Systems and Software Engineering.

[17]  Guilherme Carvalho Januario,et al.  A Survey of Policy Refinement Methods as a Support for Sustainable Networks , 2016, IEEE Communications Surveys & Tutorials.

[18]  Jinglei Liu,et al.  Exact Dominance Querying Algorithm on CP-nets , 2016 .

[19]  Jose Jesus Castro-Schez,et al.  Supporting multi-criteria decisions based on a hierarchical structure by taking advantage of acquired knowledge , 2013, Appl. Soft Comput..

[20]  Edwin Lughofer,et al.  On-line assurance of interpretability criteria in evolving fuzzy systems - Achievements, new concepts and open issues , 2013, Inf. Sci..

[21]  Giovanni Pezzulo,et al.  Coordinating with the Future: The Anticipatory Nature of Representation , 2008, Minds and Machines.

[22]  Cathy Macharis,et al.  Range-based Multi-Actor Multi-Criteria Analysis: A combined method of Multi-Actor Multi-Criteria Analysis and Monte Carlo simulation to support participatory decision making under uncertainty , 2018, Eur. J. Oper. Res..

[23]  Stephen Russell,et al.  Assisting decision making in the event-driven enterprise using wavelets , 2008, Decis. Support Syst..

[24]  Rajiv Kishore,et al.  Enterprise integration using the agent paradigm: foundations of multi-agent-based integrative business information systems , 2006, Decis. Support Syst..

[25]  M. BENAROCH Declarative representation of strategic control knowledge , 2001, Int. J. Hum. Comput. Stud..

[26]  Okan Topçu,et al.  Adaptive decision making in agent-based simulation , 2014, Simul..

[27]  Iraj Mahdavi,et al.  iCoSim-FMS: An intelligent co-simulator for the adaptive control of complex flexible manufacturing systems , 2011, Simul. Model. Pract. Theory.

[28]  Z. Wang,et al.  Corporate dashboards for integrated business and engineering decisions in oil refineries: An agent-based approach , 2012, Decis. Support Syst..

[29]  L. Mönch,et al.  An agent-based planning approach within the framework of distributed hierarchical enterprise management , 2011 .

[30]  Hamid Beigy,et al.  A new fuzzy negotiation protocol for grid resource allocation , 2014, J. Netw. Comput. Appl..

[31]  Vasant Honavar,et al.  Representing and Reasoning with Qualitative Preferences for Compositional Systems , 2011, J. Artif. Intell. Res..

[32]  Sal March,et al.  A provenance-based approach to semantic web service description and discovery , 2014, Decis. Support Syst..