Abstract
To guarantee the overall intended objectives of a multiagent systems, the behavior of individual agents should be controlled and coordinated. Such coordination can be achieved, without limiting the agents’ autonomy, via runtime norm enforcement. However, due to the dynamicity and uncertainty of the environment, the enforced norms can be ineffective. In this paper, we propose a runtime supervision mechanism that automatically revises norms when their enforcement appears to be ineffective. The decision to revise norms is taken based on a Bayesian Network that gives information about the likelihood of achieving the overall intended system objectives by enforcing the norms. Norms can be revised in three ways: relaxation, strengthening, and alteration. We evaluate the supervision mechanism on an urban smart traffic simulation.
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Notes
- 1.
When we refer to nodes of a specific type we use the corresponding notation convention, e.g., N refers to a node in \(\mathbf N \), \(\mathbf c \) refers to an assignment of values to nodes in \(\mathbf C \), \(\mathbf N _{\textit{viol}}\) refers to an assignment of value violated to a set of norm nodes \(\mathbf N \), etc.
- 2.
In the following we omit the context from the conditional probabilities since implicit.
- 3.
Actual trip time over the theoretical time w.r.t. to length and speed limits.
- 4.
Revising one norm leads to a distance of 2–3 from the original configuration, and each configuration has 10%–20% of all configurations in its neighborhood.
- 5.
We obtained 12 variants from pairwise testing with variables: time (day, night), weather (normal, extreme), CNS (none, adaptive, static), and junctions (none, adaptive lights, static lights, priority lanes). We grouped those variants in 4 groups (one per context) and we generated all their combinations to obtain 81 configurations.
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Dell’Anna, D., Dastani, M., Dalpiaz, F. (2018). Runtime Norm Revision Using Bayesian Networks. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_17
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