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Intentional Forgetting in Distributed Artificial Intelligence

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Abstract

In times of Big Data and Industry 4.0, organizational information as well as knowledge availability and quantity are driving complex decision-making tasks. Especially for AI systems, increasing knowledge-bases for elaborate computations lead to a lower efficiency of their inference mechanisms. In contrast to AI, bounded cognitive capacity is a well-known problem in psychology. When humans receive too much information, a state of information overload emerges. In order to cope with limited capacity and prevent information overload, humans adapt their knowledge and delete, override, suppress, or sort out outdated information, i.e., they forget. By transferring theories from human cognition to multiagent systems, the AdaptPRO project adopts intentional forgetting as a strategy for coping with information overload in both human and multiagent teams. This article gives an overview of an interdisciplinary research project with a strong focus on knowledge distributions and knowledge dynamics from a distributed AI perspective. Its core contribution is a formal model for distributing and adapting (meta-) knowledge by intentional forgetting to enable efficient and resilient teamwork.

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Notes

  1. http://www.adaptpro.uni-trier.de.

References

  1. Berndt JO (2018) Self-organizing multiagent negotiations: cooperation and competition of concurrently acting agents with limited knowledge. IOS Press, Amsterdam

    Google Scholar 

  2. Bjork EL, Anderson MC (1998) Varieties of goal-directed forgetting. In: Golding JM, MacLeod C (eds) Intentional forgetting: interdisciplinary approaches. Lawrence Erlbaum, Mahwah, pp 103–137

    Google Scholar 

  3. Dignum M, Dignum F, Furtado V, Melo A, Sonenberg L (2005) Towards a simulation tool for evaluating dynamic reorganization of agents societies. In: Workshop on socially inspired computing

  4. Dignum V, Dignum F (2012) A logic of agent organizations. Logic J IGPL 20(1):283–316

    Article  MathSciNet  MATH  Google Scholar 

  5. Ellwart T, Antoni CH (2017) Shared and distributed team cognition and information overload. evidence and approaches for team adaptation. In: Information and communication overload in the digital age. IGI Global

  6. Ellwart T, Konradt U, Rack O (2014) Team mental models of expertise location: validation of a field survey measure. Small Group Res 45(2):119–153

    Article  Google Scholar 

  7. Eppler MJ, Mengis J (2004) The concept of information overload: a review of literature from organization science, accounting, marketing, mis, and related disciplines. Inf Soc 20:325–344

    Article  Google Scholar 

  8. Ferber J, Gutknecht O, Michel F (2003) From agents to organizations: an organizational view of multi-agent systems. In: International workshop on agent-oriented software engineering. Springer, pp 214–230

  9. Galbraith JR (1974) Organization design: an information processing view. Interfaces 4(3):28–36

    Article  Google Scholar 

  10. Gath M, Herzog O, Vaske M (2016) Concurrent and distributed shortest path searches in multiagent-based transport systems. In: Nguyen N (ed) Transactions on computational collective intelligence XX. Springer, Berlin, pp 140–157

    Google Scholar 

  11. Hollingshead AB, Gupta N, Yoon K, Brandon DP (2012) Transactive memory theory and teams: past, present and future. In: Salas E, Fiore SM, Letsky MP (eds) Theories of team cognition: cross-disciplinary perspectives. Routledge, New York, pp 421–455

    Google Scholar 

  12. Isik Ö, Van den Bergh J, Mertens W (2012) Knowledge intensive business processes: an exploratory study, pp 3817–3826

  13. Kirn S (2006) Flexibility of multiagent systems. In: Kirn S (ed) Multiagent engineering. Theory and applications in enterprises, vol I.3. Springer, Berlin, pp 53–70

    Chapter  Google Scholar 

  14. Kozlowski SWJ, Ilgen DR (2006) Enhancing the effectiveness of work groups and teams. Psychol Sci Public Interest 7(3):77–124

    Article  Google Scholar 

  15. Lorig F, Lebherz DS, Berndt JO, Timm IJ (2017) Hypothesis-driven experiment design in computer simulation studies. In: 2017 winter simulation conference (WSC), pp 1360–1371. https://doi.org/10.1109/WSC.2017.8247880

  16. Müller HJ (1997) Towards agent systems engineering. Data Knowl Eng 23:3

    MATH  Google Scholar 

  17. Nguyen K, Schumann R (2018) A novel agent software architecture inspired by psychology. In: 14th annual social simulation conference

  18. Odell JJ, Parunak HVD, Fleischer M (2002) The role of roles in designing effective agent organizations. In: International workshop on software engineering for large-scale multi-agent systems. Springer, pp 27–38

  19. Oertel R, Antoni CH (2015) Phase-specific relationships between team learning processes and transactive memory development. Eur J Work Organ Psychol 24(5):726–741

    Article  Google Scholar 

  20. Padgham L, Lambrix P (2005) Formalisations of capabilities for BDI-agents. Auton Agents Multi-Agent Syst 10(3):249–271

    Article  Google Scholar 

  21. Reuter L, Berndt JO, Timm I (2018) Simulating psychological experiments: an agent-based modeling approach. In: Folds D, Berndt J (eds) Fourth international conference on human and social analytics (HUSO 2018). IARIA, Wilmington, pp 5–10

    Google Scholar 

  22. Reuter L, Berndt JO, Timm IJ (2017) Towards simulation-based role optimization in organizations. In: KI 2017: advances in artificial intelligence—40th annual German conference on AI, Dortmund, Germany, September 25–29, 2017, Proceedings, pp 359–365

  23. Salas E, Fiore SM, Letsky MP (2012) Theories of team cognition—cross-disciplinary perspectives. Taylor & Francis Group, New York

    Google Scholar 

  24. Sandholm T, Lesser VR (1997) Coalitions among computationally bounded agents. Artif Intell 94(1):99–137 (Special Issue on Principles of Multiagent Systems)

    Article  MathSciNet  MATH  Google Scholar 

  25. Schillo M, Fischer K, Fley B, Florian M, Hillebrandt F, Spresny D (2004) FORM—a sociologically founded framework for designing self-organization of multiagent systems. In: Lindemann G, Moldt D, Paolucci M (eds) Regulated agent-based social systems. First international workshop, RASTA 2002, lecture notes in computer science, vol 2934. Springer, Berlin, pp 156–175

  26. Schillo M, Spresny D (2005) Organization: the central concept for qualitative and quantitative scalability. In: Fischer K, Florian M, Malsch T (eds) Socionics. Scalability of complex social systems, lecture notes in artificial intelligence (LNAI), vol 3413. Springer, Berlin, pp 84–103

    Google Scholar 

  27. Stuckenschmidt H, Timm IJ (2002) Adapting communication vocabularies using shared ontologies. In: Crane-field S (ed) Workshop on ontologies in agent systems, AAMAS, pp 6–12. Bologna

  28. Timm IJ, Berndt JO, Reuter L, Ellwart T, Antoni C, Ulfert AS (2017) Towards multiagent-based simulation of knowledge management in teams. In: Leyer M, Richter A, Vodanovich S (eds) Flexible knowledge practices and the Digital Workplace (FKPDW). KIT, Karlsruhe, pp 25–40

    Google Scholar 

  29. Timm IJ, Knirsch P, Kreowski HJ, Timm-Giel A (2007) Autonomy in software systems. Springer, Berlin, pp 255–273

    Google Scholar 

  30. Timm IJ, Lorig F (2015) A survey on methodological aspects of computer simulation as research technique. In: Proceedings of the 2015 winter simulation conference, WSC ’15. IEEE Press, Piscataway, pp 2704–2715

  31. Timm IJ, Scholz T, Herzog O (2006) Emerging capabilities in intelligent agents for flexible production control. Adv Eng Inform J 20(3):247–259 (Special Issue on Emergent Synthesis)

    Article  Google Scholar 

  32. Tushman ML, Nadler DA (1978) Information processing as an integrating concept in organizational design. Acad Manag Rev 3(3):613–624

    Google Scholar 

  33. Wooldridge M (2000) Reasoning about rational agents. Bradford books. MIT Press, London

    MATH  Google Scholar 

  34. Wooldridge M (2009) An introduction to multiagent systems, 2nd edn. Wiley, Oxford

    Google Scholar 

  35. Wooldridge M, Lomuscio A (2000) Multi-agent VSK logic. In: European workshop on logics in artificial intelligence. Springer, pp 300–312

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Acknowledgements

The project AdaptPRO: Adaptive Process and Role Design in Organizations (TI 548/-1) is funded by the German Research Foundation (DFG) within the Priority Program “Intentional Forgetting in Organizations” (SPP 1921).

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Authors and Affiliations

  1. Trier Research Laboratory for Simulation (TriLabS), Center for Informatics Research and Technology (CIRT), Trier University, Behringstraße 21, 54296, Trier, Germany

    Lukas Reuter, Jan Ole Berndt & Ingo J. Timm

  2. Business, Work, and Organizational Psychology, Trier University, Universtitätsring 15, 54296, Trier, Germany

    Anna-Sophie Ulfert, Conny H. Antoni & Thomas Ellwart

Authors
  1. Lukas Reuter
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  2. Jan Ole Berndt
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  3. Anna-Sophie Ulfert
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  4. Conny H. Antoni
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  5. Thomas Ellwart
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  6. Ingo J. Timm
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Correspondence to Lukas Reuter.

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Reuter, L., Berndt, J.O., Ulfert, AS. et al. Intentional Forgetting in Distributed Artificial Intelligence. Künstl Intell 33, 69–77 (2019). https://doi.org/10.1007/s13218-018-0566-4

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