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A normative approach for resilient multiagent systems

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Abstract

We model a multiagent system (MAS) in socio-technical terms, combining a social layer consisting of norms with a technical layer consisting of actions that the agents execute. This approach emphasizes autonomy, and makes assumptions about both the social and technical layers explicit. Autonomy means that agents may violate norms. In our approach, agents are computational entities, with each representing a different stakeholder. We express stakeholder requirements of the form that a MAS is resilient in that it can recover (sufficiently) from a failure within a (sufficiently short) duration. We present ReNo, a framework that computes probabilistic and temporal guarantees on whether the underlying requirements are met or, if failed, recovered. ReNo supports the refinement of the specification of a socio-technical system through methodological guidelines to meet the stated requirements. An important contribution of ReNo is that it shows how the social and technical layers can be modeled jointly to enable the construction of resilient systems of autonomous agents. We demonstrate ReNo using a manufacturing scenario with competing public, industrial, and environmental requirements.

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Acknowledgements

We dedicate this article to the memory of Professor Aditya Ghose, who passed away unexpectedly in February 2023. This work was initiated with support from a grant from the University of Wollongong and NC State University through a collaboration network called the University Global Partnership Network. MPS additionally thanks the US National Science Foundation (grant IIS-1908374) for partial support.

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Authors and Affiliations

  1. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

    Geeta Mahala, Hoa Dam & Aditya Ghose

  2. School of Computing, University of Kent, Canterbury, Kent, UK

    Özgür Kafalı

  3. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Munindar P. Singh

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Appendix A java code: translation of an STS specification into a PRISM model

Appendix A java code: translation of an STS specification into a PRISM model

1.1 Code snippets for the initial state in the PRISM model

The Listing 5 shows java code to return the initial state of the PRISM model. The list variables have the agent variable and all state variables. The setValue(0, 1) in Line 2 in the Listing 5 shows that value 1 has been assigned to the variable at index 0 in variables list (here the variable agent is at index 0 in the list named variable). Similarly, random values have been assigned to other state variables.

figure g

1.2 Code snippets to calculate the action selection probability

figure h

1.3 Code snippets for state transitions in the PRISM model

The Listing 7 shows the snippet of Java code of state transitions in the model. The function computeTransitionTarget(int i, int offset) is used to compute non-deterministic outcomes for executing all mechanisms of a selected agent. The Line 2 in the Listing 7 shows the current state. From Line 3 to Line 5 turns each agent using round-robin agent execution. In the initial state, the value of the agent variable is 1 (as explained in the use case) meaning that this agent is Company. In the next state, the value of the agent is 2 (i.e., Hospital); then the value of the agent is 3 (i.e., Regulator); and then the value of the agent is again 1 (i.e., Company). The remaining state variables are updated with new values in the Line 8 in the Listing 7.

The function computeTransitionTarget(int i, int offset) is used to compute non-deterministic outcomes of executing all mechanisms for a selected agent and the function getTransitionProbability(int i, int offset) is used to compute the state transition probability for each state transition.

figure i

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Mahala, G., Kafalı, Ö., Dam, H. et al. A normative approach for resilient multiagent systems. Auton Agent Multi-Agent Syst 37, 46 (2023). https://doi.org/10.1007/s10458-023-09627-4

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