A Framework for Automatic Monitoring of Norms that regulate Time
Constrained Actions
- URL: http://arxiv.org/abs/2105.00200v1
- Date: Sat, 1 May 2021 09:29:32 GMT
- Title: A Framework for Automatic Monitoring of Norms that regulate Time
Constrained Actions
- Authors: Nicoletta Fornara, Soheil Roshankish, Marco Colombetti
- Abstract summary: The proposed T-NORM model can be used to express abstract norms able to regulate classes of actions that should or should not be performed in a temporal interval.
We show how the model can be used to formalize obligations and prohibitions and for inhibiting them by introducing permissions and exemptions.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper addresses the problem of proposing a model of norms and a
framework for automatically computing their violation or fulfilment. The
proposed T-NORM model can be used to express abstract norms able to regulate
classes of actions that should or should not be performed in a temporal
interval. We show how the model can be used to formalize obligations and
prohibitions and for inhibiting them by introducing permissions and exemptions.
The basic building blocks for norm specification consists of rules with
suitably nested components. The activation condition, the regulated actions,
and the temporal constrains of norms are specified using the W3C Web Ontology
Language (OWL 2). Thanks to this choice, it is possible to use OWL reasoning
for computing the effects that the logical implication between actions has on
norms fulfilment or violation. The operational semantics of the T-NORM model is
specified by providing an unambiguous procedure for translating every norm and
every exception into production rules.
Related papers
- Constrained Language Generation with Discrete Diffusion Models [61.81569616239755]
We present Constrained Discrete Diffusion (CDD), a novel method for enforcing constraints on natural language by integrating discrete diffusion models with differentiable optimization.
We show how this technique can be applied to satisfy a variety of natural language constraints, including (i) toxicity mitigation by preventing harmful content from emerging, (ii) character and sequence level lexical constraints, and (iii) novel molecule sequence generation with specific property adherence.
arXiv Detail & Related papers (2025-03-12T19:48:12Z) - Extracting Norms from Contracts Via ChatGPT: Opportunities and Challenges [14.602364944958088]
We investigate the effectiveness of ChatGPT in extracting norms from contracts.
We find promising performance in norm extraction without requiring training or fine-tuning.
However, we find some limitations of ChatGPT in extracting these norms that lead to incorrect norm extractions.
arXiv Detail & Related papers (2024-04-02T19:49:34Z) - DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation [57.07295906718989]
Constrained decoding approaches aim to control the meaning or style of text generated by a Pre-trained Language Model (PLM) using specific target words during inference.
We propose a novel decoding framework, DECIDER, which enables us to program rules on how we complete tasks to control a PLM.
arXiv Detail & Related papers (2024-03-04T11:49:08Z) - Towards an Enforceable GDPR Specification [49.1574468325115]
Privacy by Design (PbD) is prescribed by modern privacy regulations such as the EU's.
One emerging technique to realize PbD is enforcement (RE)
We present a set of requirements and an iterative methodology for creating formal specifications of legal provisions.
arXiv Detail & Related papers (2024-02-27T09:38:51Z) - Toward Unified Controllable Text Generation via Regular Expression
Instruction [56.68753672187368]
Our paper introduces Regular Expression Instruction (REI), which utilizes an instruction-based mechanism to fully exploit regular expressions' advantages to uniformly model diverse constraints.
Our method only requires fine-tuning on medium-scale language models or few-shot, in-context learning on large language models, and requires no further adjustment when applied to various constraint combinations.
arXiv Detail & Related papers (2023-09-19T09:05:14Z) - On Regularization and Inference with Label Constraints [62.60903248392479]
We compare two strategies for encoding label constraints in a machine learning pipeline, regularization with constraints and constrained inference.
For regularization, we show that it narrows the generalization gap by precluding models that are inconsistent with the constraints.
For constrained inference, we show that it reduces the population risk by correcting a model's violation, and hence turns the violation into an advantage.
arXiv Detail & Related papers (2023-07-08T03:39:22Z) - Bridging between LegalRuleML and TPTP for Automated Normative Reasoning
(extended version) [77.34726150561087]
LegalRuleML is an XML-based representation framework for modeling and exchanging normative rules.
The TPTP input and output formats are general-purpose standards for the interaction with automated reasoning systems.
We provide a bridge between the two communities by defining a logic-pluralistic normative reasoning language based on the TPTP format.
arXiv Detail & Related papers (2022-09-12T08:42:34Z) - Norm Identification through Plan Recognition [22.387008072671005]
Societal rules aim to provide a degree of behavioural stability to multi-agent societies.
Many implementations of normative systems assume various combinations of the following assumptions.
We develop a norm identification mechanism that uses a combination of parsing-based plan recognition and Hierarchical Task Network (HTN) planning mechanisms.
arXiv Detail & Related papers (2020-10-06T11:18:52Z) - Rewriting a Deep Generative Model [56.91974064348137]
We introduce a new problem setting: manipulation of specific rules encoded by a deep generative model.
We propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory.
We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects.
arXiv Detail & Related papers (2020-07-30T17:58:16Z) - A Norm Emergence Framework for Normative MAS -- Position Paper [0.90238471756546]
We propose a framework for the emergence of norms within a normative multiagent system.
We make the case that, similarly, a norm has emerged in a normative MAS when a percentage of agents adopt the norm.
We put forward a framework for the emergence of norms within a normative MAS, while special-purpose synthesizer agents formulate new norms or revisions in response to these requests.
arXiv Detail & Related papers (2020-04-06T11:42:01Z) - Towards Learning Instantiated Logical Rules from Knowledge Graphs [20.251630903853016]
We present GPFL, a probabilistic learner rule optimized to mine instantiated first-order logic rules from knowledge graphs.
GPFL utilizes a novel two-stage rule generation mechanism that first generalizes extracted paths into templates that are acyclic abstract rules.
We reveal the presence of overfitting rules, their impact on the predictive performance, and the effectiveness of a simple validation method filtering out overfitting rules.
arXiv Detail & Related papers (2020-03-13T00:32:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.