Planning in dynamic environments through temporal logic monitoring

Bardh Hoxha, Georgios Fainekos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We present a framework that enables online planning for robotic systems in dynamic environments. The PLANrm framework presented in this work utilizes the theory of robustness and monitoring of Metric Temporal Logic (MTL) specifications to inspect and modify available plans to both avoid obstacles and satisfy specifications in a dynamic environment. The use of MTL allows the practitioner to set complex event and timing based specifications that need to be satisfied in the execution of the plan. The monitoring algorithm inspects the possible paths in a bounded window and selects and adjusts a path to satisfy the specifications. In this paper, we present initial results on the framework and an extended summary of the algorithmic results. The approach is illustrated using a running example of a car-like model with a number of MTL specifications.

Original languageEnglish (US)
Title of host publicationWS-16-01
Subtitle of host publicationArtificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence
PublisherAI Access Foundation
Pages601-607
Number of pages7
VolumeWS-16-01 - WS-16-15
ISBN (Electronic)9781577357599
StatePublished - 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Other

Other30th AAAI Conference on Artificial Intelligence, AAAI 2016
CountryUnited States
CityPhoenix
Period2/12/162/17/16

Fingerprint

Temporal logic
Specifications
Planning
Monitoring
Robotics
Railroad cars

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Hoxha, B., & Fainekos, G. (2016). Planning in dynamic environments through temporal logic monitoring. In WS-16-01: Artificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence (Vol. WS-16-01 - WS-16-15, pp. 601-607). AI Access Foundation.

Planning in dynamic environments through temporal logic monitoring. / Hoxha, Bardh; Fainekos, Georgios.

WS-16-01: Artificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence. Vol. WS-16-01 - WS-16-15 AI Access Foundation, 2016. p. 601-607.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hoxha, B & Fainekos, G 2016, Planning in dynamic environments through temporal logic monitoring. in WS-16-01: Artificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence. vol. WS-16-01 - WS-16-15, AI Access Foundation, pp. 601-607, 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, United States, 2/12/16.
Hoxha B, Fainekos G. Planning in dynamic environments through temporal logic monitoring. In WS-16-01: Artificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence. Vol. WS-16-01 - WS-16-15. AI Access Foundation. 2016. p. 601-607
Hoxha, Bardh ; Fainekos, Georgios. / Planning in dynamic environments through temporal logic monitoring. WS-16-01: Artificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence. Vol. WS-16-01 - WS-16-15 AI Access Foundation, 2016. pp. 601-607
@inproceedings{4897351e345748de89b0f797d70b6a40,
title = "Planning in dynamic environments through temporal logic monitoring",
abstract = "We present a framework that enables online planning for robotic systems in dynamic environments. The PLANrm framework presented in this work utilizes the theory of robustness and monitoring of Metric Temporal Logic (MTL) specifications to inspect and modify available plans to both avoid obstacles and satisfy specifications in a dynamic environment. The use of MTL allows the practitioner to set complex event and timing based specifications that need to be satisfied in the execution of the plan. The monitoring algorithm inspects the possible paths in a bounded window and selects and adjusts a path to satisfy the specifications. In this paper, we present initial results on the framework and an extended summary of the algorithmic results. The approach is illustrated using a running example of a car-like model with a number of MTL specifications.",
author = "Bardh Hoxha and Georgios Fainekos",
year = "2016",
language = "English (US)",
volume = "WS-16-01 - WS-16-15",
pages = "601--607",
booktitle = "WS-16-01",
publisher = "AI Access Foundation",

}

TY - GEN

T1 - Planning in dynamic environments through temporal logic monitoring

AU - Hoxha, Bardh

AU - Fainekos, Georgios

PY - 2016

Y1 - 2016

N2 - We present a framework that enables online planning for robotic systems in dynamic environments. The PLANrm framework presented in this work utilizes the theory of robustness and monitoring of Metric Temporal Logic (MTL) specifications to inspect and modify available plans to both avoid obstacles and satisfy specifications in a dynamic environment. The use of MTL allows the practitioner to set complex event and timing based specifications that need to be satisfied in the execution of the plan. The monitoring algorithm inspects the possible paths in a bounded window and selects and adjusts a path to satisfy the specifications. In this paper, we present initial results on the framework and an extended summary of the algorithmic results. The approach is illustrated using a running example of a car-like model with a number of MTL specifications.

AB - We present a framework that enables online planning for robotic systems in dynamic environments. The PLANrm framework presented in this work utilizes the theory of robustness and monitoring of Metric Temporal Logic (MTL) specifications to inspect and modify available plans to both avoid obstacles and satisfy specifications in a dynamic environment. The use of MTL allows the practitioner to set complex event and timing based specifications that need to be satisfied in the execution of the plan. The monitoring algorithm inspects the possible paths in a bounded window and selects and adjusts a path to satisfy the specifications. In this paper, we present initial results on the framework and an extended summary of the algorithmic results. The approach is illustrated using a running example of a car-like model with a number of MTL specifications.

UR - http://www.scopus.com/inward/record.url?scp=85020214509&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020214509&partnerID=8YFLogxK

M3 - Conference contribution

VL - WS-16-01 - WS-16-15

SP - 601

EP - 607

BT - WS-16-01

PB - AI Access Foundation

ER -