Penalization Framework For Autonomous Agents Using Answer Set Programming

Vineel S. K. Tummala

This paper presents a framework for enforcing penalties on intelligent agents that do not comply with authorization or obligation policies in a changing environment. A framework is proposed to represent and reason about penalties in plans, and an algorithm is proposed to penalize an agent's actions based on their level of compliance with respect to authorization and obligation policies. Being aware of penalties an agent can choose a plan with a minimal total penalty, unless there is an emergency goal like saving a human's life. The paper concludes that this framework can reprimand insubordinate agents.

In Enrico Pontelli, Stefania Costantini, Carmine Dodaro, Sarah Gaggl, Roberta Calegari, Artur D'Avila Garcez, Francesco Fabiano, Alessandra Mileo, Alessandra Russo and Francesca Toni: Proceedings 39th International Conference on Logic Programming (ICLP 2023), Imperial College London, UK, 9th July 2023 - 15th July 2023, Electronic Proceedings in Theoretical Computer Science 385, pp. 411–415.
Published: 12th September 2023.

ArXived at: https://dx.doi.org/10.4204/EPTCS.385.50 bibtex PDF
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