Automated Synthesis of Social Laws in STRIPS

Ronen Nir, Alexander Shleyfman, Erez Karpas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Agents operating in a multi-Agent environment must consider not just their actions, but also those of the other agents in the system. Artificial social systems are a well-known means for coordinating a set of agents, without requiring centralized planning or online negotiation between agents. Artificial social systems enact a social law which restricts the agents from performing some actions under some circumstances. A robust social law prevents the agents from interfering with each other, but does not prevent them from achieving their goals. Previous work has addressed how to check if a given social law, formulated in a variant of MA-STRIPS, is robust, via compilation to planning. However, the social law was manually specified. In this paper, we address the problem of automatically synthesizing a robust social law for a given multi-Agent environment. We treat the problem of social law synthesis as a search through the space of possible social laws, relying on the robustness verification procedure as a goal test. We also show how to exploit additional information produced by the robustness verification procedure to guide the search.

Original languageEnglish
Title of host publicationAAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PublisherAAAI press
Pages9941-9948
Number of pages8
ISBN (Electronic)9781577358350
StatePublished - 2020
Externally publishedYes
Event34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States
Duration: 7 Feb 202012 Feb 2020

Publication series

NameAAAI 2020 - 34th AAAI Conference on Artificial Intelligence

Conference

Conference34th AAAI Conference on Artificial Intelligence, AAAI 2020
Country/TerritoryUnited States
CityNew York
Period7/02/2012/02/20

Bibliographical note

Publisher Copyright:
Copyright c 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

The work of Alexander Shleyfman was supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities.

FundersFunder number
Israel Academy of Sciences and Humanities

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