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Computable law as argumentation-based MAS
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1613-0073
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CEUR workshop proceedings, 2020, Vol. 2706, pp. 54-68
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CALEGARI, Roberta, OMICINI, Andrea, SARTOR, Giovanni, Computable law as argumentation-based MAS, CEUR workshop proceedings, 2020, Vol. 2706, pp. 54-68 - https://hdl.handle.net/1814/76644
Abstract
In this paper we sketch a vision of computable law as argumentation-based MAS, i.e., human-centred intelligent systems densely populated by agents (software or human) capable of understanding, arguing, and reporting, via factual assertions and arguments, about what is happening and what they can make possibly happen. A multi-agent system based on argumentation, dialogue, and conversation is, in this vision, the basis for making the law computable: through argumentation, dialogue, and adherence to social judgment, the behaviour of the intelligent system can be reached, shaped, and controlled with respect to the law. In such a scenario, computable law – and related intelligent behaviour – is likely to become associated with the capability of arguing about state and situation, by reaching a consensus on what is happening around and what is needed, and by triggering and orchestrating proper decentralised semantic conversations to decide how to collectively act in order to reach a future desirable state. Interpretability and explainability become important features for that sort of systems, based on the integration of logic-based and sub-symbolic techniques. Within this novel setting, MAS methodologies and technologies become the starting point to achieve computable law, even if they need to be adapted and extended for dealing with new challenges. Accordingly, in this paper we discuss how this novel vision can build upon some readily-available technologies, and the research challenges it poses. We analyse a number of approaches and technologies that should be involved in the engineering of systems and services, and become core expertise for distributed systems engineers. Among the others, these include knowledge representation, machine learning, and logic argumentation.
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Published online: 24 October 2020