LegalRuleML: XML-Based Rules and Norms

Palmirani, Monica; Governatori, Guido; Rotolo, Antonino; Tabet, Said; Boley, Harold; Paschke, Adrian

doi:10.1007/978-3-642-24908-2_30

Cited by 63 publications

(31 citation statements)

References 28 publications

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“…Frameworks for representing and reasoning with norms have been developed in legal computing (Hoekstra et al 2007;Palmirani et al 2011;Lam, Hashmi, and Scofield 2016), multi-agent systems (Vasconcelos, Kollingbaum, and Norman 2009;, and social robotics (Malle, Scheutz, and Austerweil 2017). Recent work has also explored the problems of norm learning and norm identification, through approaches such as belieftheoretic learning (Sarathy, Scheutz, and Malle 2017), priority weight learning for conflicting norms (Kasenberg and Scheutz 2018a), inference from social sanctions in multiagent systems (Savarimuthu et al 2010;Cranefield et al 2015), and rule induction from iterated design specifications (Corapi et al 2011).…”

Section: Related Workmentioning

confidence: 99%

That’s Mine! Learning Ownership Relations and Norms for Robots

Tan

Brawer

Scassellati

2019

AAAI

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The ability for autonomous agents to learn and conform to human norms is crucial for their safety and effectiveness in social environments. While recent work has led to frameworks for the representation and inference of simple social rules, research into norm learning remains at an exploratory stage. Here, we present a robotic system capable of representing, learning, and inferring ownership relations and norms. Ownership is represented as a graph of probabilistic relations between objects and their owners, along with a database of predicate-based norms that constrain the actions permissible on owned objects. To learn these norms and relations, our system integrates (i) a novel incremental norm learning algorithm capable of both one-shot learning and induction from specific examples, (ii) Bayesian inference of ownership relations in response to apparent rule violations, and (iii) perceptbased prediction of an object's likely owners. Through a series of simulated and real-world experiments, we demonstrate the competence and flexibility of the system in performing object manipulation tasks that require a variety of norms to be followed, laying the groundwork for future research into the acquisition and application of social norms.

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Section: Related Workmentioning

confidence: 99%

That’s Mine! Learning Ownership Relations and Norms for Robots

Tan

Brawer

Scassellati

2019

AAAI

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“…Furthermore, they could capture legal knowledge and be used to ensure that access to data is lawful. Capturing legal knowledge using business rules is not new; for instance, it is discussed in the work of Gong and Janssen (2014) and Palmirani et al (2011).…”

Section: Business Rulesmentioning

confidence: 99%

Design of a software architecture supporting business-to-government information sharing to improve public safety and security

2017

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To ensure public safety and security, it is vitally important for governments to collect information from businesses and analyse it. Such information can be used to determine whether transported goods might be suspicious and therefore require physical inspection. Although businesses are obliged to report some information, they are reluctant to share additional information for fear of sharing competitively sensitive information, becoming liable and not being compliant with the law. These reasons are often overlooked in the design of software architectures for information sharing. In the present research, we followed a design science approach to develop a software architecture for business-to-government information sharing. Based on literature and a case study, we elicited the requirements an architecture that provides for the sharing of information should meet to make it acceptable to businesses. We then developed the architecture and evaluated it against the requirements. The architecture consists of a blockchain that stores events and rules for information sharing that are controlled by businesses. For each event, two parties use their private keys to encrypt its Merkle root to confirm that they know the data are correct. This makes it easy to check whether information is reliable and whether an event should be accepted. Access control, metadata and context information enable the context-based sharing of information. This is combined with the encryption and decryption of data to provide access to certain data within an organisation. Sélinde van Engelenburg

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“…We define a simple yet expressive rule-based language for representing the legal statements that compose a legal domain and we show how a legal domain regulates the operations on the data. Furthermore, we define guidelines to translate LegalRuleML [1] documents into our rule-based language. The paper is organized as follows: in Section 2 an overview of the related work; Section 3 introduces the Legal Domain Model; Section 4 presents the architecture we define for integrating such model into a relational DBMS; Section 5 sketches a translation methodology from standard LegalRuleML to our model; Section 6 outlines possible future work and draws the conclusions.…”

Section: Ideas '13mentioning

confidence: 99%

Querying data across different legal domains

Taddeo

Trombetta

Montesi

et al. 2013

Proceedings of the 17th International Database Engineering &Amp; Applications Symposium on - IDEAS '13

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The management of legal domains is gaining great importance in the context of data management. In fact, the geographical distribution of data as implied -for example -by cloud-based services requires that the legal restrictions and obligations are to be taken into account whenever data circulates across different legal domains. In this paper, we start to investigate an approach for coping with the complex issues that arise when dealing with data spanning different legal domains. Our approach consists of a conceptual model that takes into account the notion of legal domain (to be paired with the corresponding data) and a reference architecture for implementing our approach in an actual relational DBMS.

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LegalRuleML: XML-Based Rules and Norms

Cited by 63 publications

References 28 publications

That’s Mine! Learning Ownership Relations and Norms for Robots

That’s Mine! Learning Ownership Relations and Norms for Robots

Design of a software architecture supporting business-to-government information sharing to improve public safety and security

Querying data across different legal domains

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