Artificial intelligence (AI) planning models play an important role in decision support systems for disaster management e.g. typhoon contingency plan development. However, constructing an AI planning model always requires significant amount of manual effort, which becomes a bottleneck to emergency response in a time-critical situation. In this demonstration, we present a framework of automating a domain model of planning domain definition language from natural language input through deep learning techniques. We implement this framework in a typhoon response system and demonstrate automatic generation of typhoon contingency plan from official typhoon plan documents

Chen, Junhan

Chen, Wanqing

Ma, Biyang

Ming, Zhong

Pan, Yinghu

Yao, Zhangrui

Zeng, Yifeng

Zhou, Junxin

Northumbria Research Link

Northumbria Research LinkCitation: Zeng, Yifeng, Yao, Zhangrui, Pan, Yinghu, Chen, Wanqing, Zhou, Junxin, Chen, Junhan, Ma, Biyang and Ming, Zhong (2021) ATPT: Automate Typhoon Contingency Plan Generation from Text. In: AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems. Proceedings of AAMAS . International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), pp. 1788-1790. ISBN 9781450383073 Published by: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)URL: https://doi.org/10.5555/3463952.3464238 <https://doi.org/10.5555/3463952.3464238>This  version  was  downloaded  from  Northumbria  Research  Link: http://nrl.northumbria.ac.uk/id/eprint/46620/Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University’s research output. 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To read and/or cite from the published version of the research, please visit the publisher’s website (a subscription may be required.)                        ATPT: Automate Typhoon Contingency Plan Generation fromText(Demonstration Track)Yifeng Zeng1, Zhangrui Yao2, Yinghui Pan3*, Wanqing Chen3, Junxin Zhou3, Junhan Chen2,Biyang Ma1 and Zhong Ming31Department of Computer and Information Sciences, Northumbria University, UK2Department of Automation, Xiamen University, China3College of Computer Science and Software Engineering, Shenzhen University, ChinaEmail:panyinghui@szu.edu.cn(*CorrespondingAuthor)ABSTRACTArtificial intelligence (AI) planningmodels play an important role indecision support systems for disaster management e.g. typhoon con-tingency plan development. However, constructing an AI planningmodel always requires significant amount of manual effort, whichbecomes a bottleneck to emergency response in a time-critical situa-tion. In this demonstration, we present a framework of automating adomain model of planning domain definition language from naturallanguage input through deep learning techniques. We implementthis framework in a typhoon response system and demonstrateautomatic generation of typhoon contingency plan from officialtyphoon plan documents.KEYWORDSPlanning Domain Definition Language; Natural Language Process;Typhoon Contingency PlanACM Reference Format:Yifeng Zeng1, Zhangrui Yao2, Yinghui Pan3*, Wanqing Chen3, Junxin Zhou3,Junhan Chen2, Biyang Ma1 and Zhong Ming3. 2021. ATPT: Automate Ty-phoon Contingency Plan Generation from Text: (Demonstration Track). InProc. of the 20th International Conference on Autonomous Agents and Multia-gent Systems (AAMAS 2021), Online, May 3–7, 2021, IFAAMAS, 3 pages.1 INTRODUCTIONTyphoon response systems demand time-critical decision making,which expects a planningmodel to be implemented in an emergencymode. In particular, many inputs to build an AI planning modelin order to generate a situation-aware typhoon contingency planare presented in the form of text e.g. typhoon response manuals,a situational report or even a collection of messages from socialmedia platforms. This needs an intelligent agent that is able to readnatural language inputs and build an AI planningmodel accordingly.It challenges a fundamental research issue of automating an AIplanning model, most of which focuses on learning the planningmodel from available data [13].In this research application, we choose the classical AI plan-ning model - Planning Domain Definition Language (PDDL) [4, 9],which has been well studied and enjoys wide applications in manyProc. of the 20th International Conference on Autonomous Agents and Multiagent Systems(AAMAS 2021), U. Endriss, A. Nowé, F. Dignum, A. Lomuscio (eds.), May 3–7, 2021, Online.© 2021 International Foundation for Autonomous Agents and Multiagent Systems(www.ifaamas.org). All rights reserved.fields [6], as the typhoon contingency planning model in the demon-stration. Another benefit of using PDDL arises from its clear se-mantics that facilitates the learning process particularly involvinga free text. A PDDL plan model contains two basic files: a domainfile and a problem file. A problem file specifies a plan goal, initialstates and a collection of objects involving in a planning problem,and it reflects decision makers’ needs in the plan. In contrast, adomain file encodes important properties of the planning models,namely Predicates and Actions, that compose the planning dynamicsunder uncertainty. In principle, the domain file is the key elementof a PDDL model and decides the planning quality when the PDDLmodel is executed. The domain file specification requires a largeamount of domain knowledge and can not be built by a generaluser in a straightforward way. Hence, the automatic constructionof a PDDL model lies in learning both Predicates and Actions in adomain file, namely domain model learning, from domain inputs.There has seen growing interest in building domain models ofPDDL from natural language inputs [7, 12, 14]. The model learn-ing is either conducted through the standard natural languageprocessing (NLP) techniques [2], e.g. OpenNLP [3] and StanfordCoreNLP [8], or developed using a deep reinforcement learningmethod [15]. However, most of the previous work still demands toannotate the inputs of the natural language sentences and treatsthe sentences in a separated way. It leads to more duplicate actionsthan what are needed in a PDDL domain model. In this research,we consider a more natural input where a text document or a se-ries of messages are presented to an autonomous planning system.This is a general setting in a typhoon response system since atyphoon contingency plan development mainly depends on bothofficial documents prescribing response strategies and a steam oflive information in an emergency situation.2 FROM TEXT TO PLAN MODELSWe are building an autonomous typhoon response system (ATRS)for the purpose of generating typhoon tracking charts and devel-oping typhoon contingency plans in practice. The backbone is anoperational planning model that can be constructed from availableinformation including data and text inputs. In this demonstration,we show the PDDL domain model learning from text.We present the end-to-end PDDL planning model developmentframework in Fig. 1. This framework aims to generate both predi-cates and actions for a domain model in PDDL. Instead of dealingDemonstration Track  AAMAS 2021, May 3-7, 2021, Online1788 Action set: (issued, reports, forwarded, expected) Object set: (City Meteorological Station,                      City Flood Control Office,                      City Flood Control Department)  The City Meteorological Station issued typhoon news. It is expected that the typhoon may affect our city in the future. The City Flood Control Office promptly forwarded the typhoon message .  The City Flood Control Department  closely monitors the typhoon development trend...… BERT for     embedding BiLSTM    CRFNN VVInclude: Combining similar termsClassifying objectsCompiling planning problem fileAction pre-condition and Effect (Optional)...User Interaction(City Meteorological Station, issued, typhoon news)(City Meteorological Station, expected, typhoon may affect our city)(City Flood Control Office, forwarded, typhoon message)(City Flood Control Department, monitors, typhoon development trend) ...(define (domain Typhoon)    (:classes department attribute location)    (:predicates (department ?m)　　　　    )　　　 (:action releases    :parameters(?d-department ?a-attribute ?l-location)             :precondition         (and 　　　　　　　　　            (at ?d ?l)             (has ?d ?a)            (releases-can ?d) (releases-can ?a)        )...PDDL Domain ModelExtract ternaryGenerateRemovingduplicatesFigure 1: A deep learning based framework for automatinga PDDL domain model.with the inputs of individual sentences, we receive an entire para-graph of text and convert the action extraction into a sequencelabelling problem. To identify a set of correct words ( Predicates andActions), we resort to deep learning based NLP techniques, e.g. bidi-rectional encoder representations from transformers (BERT) [1], toprocess the inputs and use a bidirectional long-short term memory-conditional random field (BiLSTM- CRF) model [11] to learn anaction representation with a triplet (subject, predicate, object) fromthe processed text. The BERT model functions as a segmentationmechanism to divide the input paragraphs and subsequently trans-forms the sentences and associated words into a low-dimensionsemantic representation in a hidden feature space. The BiLSTM-CRF model deals with the sequence labelling problem and identifiesa proper set of predicates and actions from the vectors that embedthe features of the paragraphs. In our text, we add the constraintsof typhoon states and relevant control strategies in the CRF modelso that it could learn the triplets in a more accurate manner.As the learning process may still mis-understand some termi-nologies in the typhoon context, we implement a user interactioncomponent where users have chance to refine the learned triplets.The refined triplets become the inputs to learning PDDL domainmodels. By using the triplets, the system fills in the domain modelsfollowing the PDDL structure. In addition, a problem file is specifiedin the user interaction and is compiled together with the domainmodel to build a PDDL model. By doing this, we can develop atyphoon contingency plan based on the learned PDDL model. Aswe use pre-trained BERT models, the actual execution of buildingthe PDDL model is rather efficient. In addition, we could transferthe knowledge in the built PDDLmodel to cope with a typhoon con-tingency plan in different cities. It builds up a timely and consistentresponse in ATRS.3 AUTONOMOUS TYPHOON RESPONSESYSTEMWe implement a web-based autonomous typhoon response sys-tem (ATRS) that has the main functionalities of generating a ty-phoon contingency plan, plotting typhoon tracks and maintaininga knowledge graph of typhoon response systems in Fig. 2. Po-tential end-users include decision makers for typhoon emergencyresponse, academic researchers in disaster management and thegeneral public.The system first asks for a text document to be uploaded and thenprocesses the document to retrieve the corresponding triplets usingthe deep learning framework. The identified triplets are presentedto users who could add more fields into the list. Subsequently, theusers can upload a problem file that joins with the learned domainfile to compose the final PDDL plan model. The model is to bedownloaded and run by some well developed PDDL solvers [10],which provides a typhoon contingency plan to the users.In addition, we provide the comparison between our techniquesand the latest domain model learning methods that is used for thepurpose of generating a storyline [5]. The users can choose eitherof them to learn the domain model and expect similar interactionsto refine the learned outputs. In the current format, our new frame-work has better performance than others, and can deal with generaltext inputs. We intend to accommodate more PDDL model learningtechniques in the future, and improve all the ATRS functionalitiesto publish the external link for a general use. The link of our demon-stration video could be found below [16]. We add elaborative textat the bottom of every screen in the video.Figure 2: Web-based autonomous typhoon response sys-tem (ATRS) provides an automatic generation of typhooncontingency plan through the PDDL model learning frame-work.4 CONCLUSIONOur new learning framework makes a further step of widening thetype of inputs for learning the PDDL model when a text documentis provided to a typhoon response system. We have implemented itto generate a proper typhoon contingency plan in the autonomoustyphoon response system. This work contributes into an automaticgeneration of planning models for intelligent agent planning andscheduling in intelligent systems. Learning PDDL domain mod-els still faces many challenges and user interaction is needed toremedy the learning process in order to generate a precise PDDLmodel. We are continuously improving the PDDL model learningand providing more friendly user interactions in ATRS.ACKNOWLEDGMENTSProfessor Yifeng Zeng and Dr. Biyang Ma would like to thank thesupports from the EPSRC project (Grant No. EP/S011609/1) in theUK. Others are supported in part by the National Natural ScienceFoundation of China (Grants No.61772442, 61836005 and 61806089).Demonstration Track  AAMAS 2021, May 3-7, 2021, Online1789REFERENCES[1] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert:Pre-training of deep bidirectional transformers for language understanding. arXivpreprint arXiv:1810.04805 (2018).[2] HankzHankui Zhuo Feng,Wenfeng and Subbarao Kambhampati. 2018. ExtractingAction Sequences from Texts based on Deep Reinforcement Learning. arXivpreprint arXiv:1803.02632 (2018).[3] Apache Software Foundation. 2004. OpenNLP. website. https://opennlp.apache.org/docs/[4] Maria Fox and Derek Long. 2003. PDDL2.1: An Extension to PDDL for ExpressingTemporal Planning Domains. Journal of Artificial Intelligence Research 20 (2003),61–124.[5] Thomas Hayton, Julie Porteous, Joao Ferreira, Alan Lindsay, and Jonathon Read.2017. Storyframer: From input stories to output planning models. InWorkshopon Knowledge Engineering for Planning and Scheduling. The 27th InternationalConference on Automated Planning and Scheduling. 1–9.[6] Jorg Hoffmann, Holger Hermanns, Michaela Klauck, Marcel Steinmetz, ErezKarpas, and Daniele Magazzeni. 2020. let’s Learn Their Language? A Case forPlanning with Automata-Network Languages from Model Checking. 34 (2020),13569–13575.[7] Yongfeng Huo, Jing Tang, Yinghui Pan, Yifeng Zeng, and Langcai Cao. 2020.Learning a Planning Domain Model From Natural Language Process Manuals.IEEE Access 8 (2020), 143219–143232.[8] Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J.Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural LanguageProcessing Toolkit. In Association for Computational Linguistics (ACL) SystemDemonstrations. 55–60. http://www.aclweb.org/anthology/P/P14/P14-5010[9] Drew McDermott, Malik Ghallab, Adele Howe, Craig Knoblock, Ashwin Ram,Manuela Veloso, Daniel Weld, and David Wilkins. 1998. PDDL - The PlanningDomain Definition Language. (1998). http://www.cs.yale.edu/homes/dvm.[10] Christian Muise, Hanbin Li, Qianyu Zhang, Mau Magnaguagno, Nir Lipovetzky,Florian GeiÃ§er, Steve Levine, and JÃűrg Hoffmann. 2015. PDDL editor. website.http://editor.planning.domains[11] Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, ChristopherClark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized wordrepresentations. arXiv preprint arXiv:1802.05365 (2018).[12] Julie Porteous, João F Ferreira, Alan Lindsay, and Marc Cavazza. 2020. Extend-ing narrative planning domains with linguistic resources. In Proceedings of the19th International Conference on Autonomous Agents and Multiagent Systems.International Foundation for Autonomous Agents and Multiagent Systems.[13] Adithya Raam Sankar, Prashant Doshi, and Adam Goodie. 2020. Evacuate or Not?A POMDP Model of the Decision Making of Individuals in Hurricane EvacuationZones. In Proceedings of The 35th Uncertainty in Artificial Intelligence Conference(Proceedings of Machine Learning Research, Vol. 115), Ryan P. Adams and VibhavGogate (Eds.). 669–678.[14] Miglani Shivam. 2020. NL to PDDL: One-Shot Learning of Planning Domains fromNatural Language Process Manuals. Delft University of Technology Repositories(2020).[15] Avirup Sil, Fei Huang, and Alexander Yates. 2010. Extracting action and eventsemantics from web text. In 2010 AAAI Fall Symposium Series.[16] Yifeng Zeng, Yinghui Pan Zhangrui Yao, Wanqing Chen, Junhan ChenJunxin Zhou, Biyang Ma, and Zhong Min. 2021. ATPT: Automate TyphoonContingency Plan Generation from Text. Video. Retrieved January 19, 2021from https://youtu.be/5BnT-RGULbIDemonstration Track  AAMAS 2021, May 3-7, 2021, Online1790

ATPT: Automate Typhoon Contingency Plan Generation from Text

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ATPT: Automate Typhoon Contingency Plan Generation from Text

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