Normative rule extraction from implicit learning into explicit representation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017203" target="_blank" >RIV/62690094:18450/20:50017203 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.3233/FAIA200555" target="_blank" >http://dx.doi.org/10.3233/FAIA200555</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/FAIA200555" target="_blank" >10.3233/FAIA200555</a>

Result language

angličtina

Original language name

Normative rule extraction from implicit learning into explicit representation

Original language description

Normative multi-agent research is an alternative viewpoint in the design of adaptive autonomous agent architecture. Norms specify the standards of behaviors such as which actions or states should be achieved or avoided. The concept of norm synthesis is the process of generating useful normative rules. This study proposes a model for normative rule extraction from implicit learning, namely using the Q-learning algorithm, into explicit norm representation by implementing Dynamic Deontics and Hierarchical Knowledge Base (HKB) to synthesize useful normative rules in the form of weighted state-action pairs with deontic modality. OpenAi Gym is used to simulate the agent environment. Our proposed model is able to generate both obligative and prohibitive norms as well as deliberate and execute said norms. Results show the generated norms are best used as prior knowledge to guide agent behavior and performs poorly if not complemented by another agent coordination mechanism. Performance increases when using both obligation and prohibition norms, and in general, norms do speed up optimum policy reachability. © 2020 The authors and IOS Press. All rights reserved.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-64368-114-6

ISSN

0922-6389

e-ISSN

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Number of pages

14

Pages from-to

88-101

Publisher name

IOS Press BV

Place of publication

Amsterdam

Event location

Japonsko

Event date

Oct 22, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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