The Complexity of Data-Driven Norm Synthesis and Revision

• URL: http://arxiv.org/abs/2112.02626v1
• Date: Sun, 5 Dec 2021 17:15:00 GMT
• Title: The Complexity of Data-Driven Norm Synthesis and Revision
• Authors: Davide Dell'Anna, Natasha Alechina, Brian Logan, Maarten L\"offler, Fabiano Dalpiaz, Mehdi Dastani
• Abstract summary: We consider the problem of synthesising a norm from traces of agent behaviour. Each trace is labelled with whether the behaviour satisfies the system objective. We show that the norm synthesis problem is NP-complete.
• Score: 19.235739945526877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Norms have been widely proposed as a way of coordinating and controlling the activities of agents in a multi-agent system (MAS). A norm specifies the behaviour an agent should follow in order to achieve the objective of the MAS. However, designing norms to achieve a particular system objective can be difficult, particularly when there is no direct link between the language in which the system objective is stated and the language in which the norms can be expressed. In this paper, we consider the problem of synthesising a norm from traces of agent behaviour, where each trace is labelled with whether the behaviour satisfies the system objective. We show that the norm synthesis problem is NP-complete.

Related papers

• An Open Knowledge Graph-Based Approach for Mapping Concepts and Requirements between the EU AI Act and International Standards [1.9142148274342772]
  The EU's AI Act will shift the focus of such organizations toward conformance with the technical requirements for regulatory compliance. This paper offers a simple and repeatable mechanism for mapping the terms and requirements relevant to normative statements in regulations and standards.
  arXiv  Detail & Related papers  (2024-08-21T18:21:09Z)
• Normative Requirements Operationalization with Large Language Models [3.456725053685842]
  Normative non-functional requirements specify constraints that a system must observe in order to avoid violations of social, legal, ethical, empathetic, and cultural norms. Recent research has tackled this challenge using a domain-specific language to specify normative requirements. We propose a complementary approach that uses Large Language Models to extract semantic relationships between abstract representations of system capabilities.
  arXiv  Detail & Related papers  (2024-04-18T17:01:34Z)
• A Hybrid System for Systematic Generalization in Simple Arithmetic Problems [70.91780996370326]
  We propose a hybrid system capable of solving arithmetic problems that require compositional and systematic reasoning over sequences of symbols. We show that the proposed system can accurately solve nested arithmetical expressions even when trained only on a subset including the simplest cases.
  arXiv  Detail & Related papers  (2023-06-29T18:35:41Z)
• Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models [80.23791222509644]
  Inconsistent AI models are considered brittle and untrustworthy by human users. We find that state-of-the-art vision-language models suffer from a surprisingly high degree of inconsistent behavior across tasks. We propose a rank correlation-based auxiliary training objective, computed over large automatically created cross-task contrast sets.
  arXiv  Detail & Related papers  (2023-03-28T16:57:12Z)
• Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models [94.30953696090758]
  We build compositional end-to-end spoken language understanding systems. By relying on intermediate decoders trained for ASR, our end-to-end systems transform the input modality from speech to token-level representations. Our models outperform both cascaded and direct end-to-end models on a labeling task of named entity recognition.
  arXiv  Detail & Related papers  (2022-10-27T19:33:18Z)
• NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly [61.77957329364812]
  We introduce a framework for addressing the novel task of conversation-grounded multi-lingual, multi-cultural norm discovery. NormSAGE elicits knowledge about norms through directed questions representing the norm discovery task and conversation context. It further addresses the risk of language model hallucination with a self-verification mechanism ensuring that the norms discovered are correct.
  arXiv  Detail & Related papers  (2022-10-16T18:30:05Z)
• Neural-Symbolic Recursive Machine for Systematic Generalization [113.22455566135757]
  We introduce the Neural-Symbolic Recursive Machine (NSR), whose core is a Grounded Symbol System (GSS) NSR integrates neural perception, syntactic parsing, and semantic reasoning. We evaluate NSR's efficacy across four challenging benchmarks designed to probe systematic generalization capabilities.
  arXiv  Detail & Related papers  (2022-10-04T13:27:38Z)
• Interpretable Reinforcement Learning with Multilevel Subgoal Discovery [77.34726150561087]
  We propose a novel Reinforcement Learning model for discrete environments. In the model, an agent learns information about environment in the form of probabilistic rules. No reward function is required for learning; an agent only needs to be given a primary goal to achieve.
  arXiv  Detail & Related papers  (2022-02-15T14:04:44Z)
• 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)
• 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)
• Mining International Political Norms from the GDELT Database [3.190574537106449]
  This work investigates the role that norms can play in governing agent actions in multi-agent systems. We use the GDELT dataset, containing machine-encoded records of international events extracted from news reports. We apply a Bayesian norm mining mechanism to identify norms that best explained the observed behaviour.
  arXiv  Detail & Related papers  (2020-03-31T08:48:37Z)

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.