Integrated Commonsense Reasoning and Deep Learning for Transparent Decision Making in Robotics

A robot’s ability to provide explanatory descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing the desired transparency in decision making is challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning me...

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Vydané v:	SN computer science Ročník 2; číslo 4; s. 242
Hlavní autori:	Mota, Tiago, Sridharan, Mohan, Leonardis, Aleš
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Singapore Springer Singapore 01.07.2021 Springer Nature B.V
Predmet:	Actuation Advances in Multi-Agent Systems Research: EUMAS 2020 Extended Selected Papers Algorithms Classification Cognition & reasoning Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Decision making Deep learning Descriptions Image manipulation Information Systems and Communication Service Knowledge representation Logic Original Research Pattern Recognition and Graphics Planning Reasoning Robot learning Robotics Robots Scene analysis Semantic web Software Engineering/Programming and Operating Systems Vision Non-monotonic logical reasoning Deep learning Scene understanding Explainable reasoning and learning Robotics
ISSN:	2662-995X, 2661-8907
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Abstract	A robot’s ability to provide explanatory descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing the desired transparency in decision making is challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning methods. As a step towards addressing this challenge, our architecture combines the complementary strengths of non-monotonic logical reasoning with incomplete commonsense domain knowledge, deep learning, and inductive learning. During reasoning and learning, the architecture enables a robot to provide on-demand explanations of its decisions, the evolution of associated beliefs, and the outcomes of hypothetical actions, in the form of relational descriptions of relevant domain objects, attributes, and actions. The architecture’s capabilities are illustrated and evaluated in the context of scene understanding tasks and planning tasks performed using simulated images and images from a physical robot manipulating tabletop objects. Experimental results indicate the ability to reliably acquire and merge new information about the domain in the form of constraints, preconditions, and effects of actions, and to provide accurate explanations in the presence of noisy sensing and actuation.
AbstractList	A robot’s ability to provide explanatory descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing the desired transparency in decision making is challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning methods. As a step towards addressing this challenge, our architecture combines the complementary strengths of non-monotonic logical reasoning with incomplete commonsense domain knowledge, deep learning, and inductive learning. During reasoning and learning, the architecture enables a robot to provide on-demand explanations of its decisions, the evolution of associated beliefs, and the outcomes of hypothetical actions, in the form of relational descriptions of relevant domain objects, attributes, and actions. The architecture’s capabilities are illustrated and evaluated in the context of scene understanding tasks and planning tasks performed using simulated images and images from a physical robot manipulating tabletop objects. Experimental results indicate the ability to reliably acquire and merge new information about the domain in the form of constraints, preconditions, and effects of actions, and to provide accurate explanations in the presence of noisy sensing and actuation.
ArticleNumber	242
Author	Sridharan, Mohan Mota, Tiago Leonardis, Aleš
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In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, and Garnett R. editors. Advances in neural information processing systems. Montreal, Canada. 2018. SridharanMMeadowsBTowards a theory of explanations for human-robot collaborationKunstliche Intelligenz201933433134210.1007/s13218-019-00616-y SridharanMGelfondMZhangSWyattJREBA: a refinement-based architecture for knowledge representation and reasoning in roboticsJ Artif Intell Res20196587180399043610.1613/jair.1.11524 Koh PW, Liang P. Understanding black-box predictions via influence functions. In: International conference on machine learning. pp. 1885–1894. 2017. LeCunYBottouLBengioYHaffnerPGradient-based learning applied to document recognitionProc IEEE.199886112278232410.1109/5.726791 Mota T, Sridharan M. Commonsense reasoning and knowledge acquisition to guide deep learning on robots. In: Robotics science and systems. 2019. Assaf R, Schumann A. Explainable deep neural networks for multivariate time series predictions. 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In: Twenty-fourth international conference on automated planning and scheduling. 2014 MillerGAWordNet: a lexical database for EnglishCommun ACM.19953811394110.1145/219717.219748 MillerTExplanations in artificial intelligence: insights from the social sciencesArtif Intell.2019267138387451110.1016/j.artint.2018.07.007 KontopoulosEBassiliadesNAntoniouGVisualizing semantic web proofs of defeasible logic in the dr-device systemKnowl Based Syst.201124340641910.1016/j.knosys.2010.12.001 Anjomshoae S, Najjar A, Calvaresi D, Framling K. Explainable agents and robots: results from a systematic literature review. In: International conference on autonomous agents and multiagent systems. Montreal, Canada. 2019. 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Eng.200864366268710.1016/j.datak.2007.10.006 ErdemEPatogluVApplications of ASP in roboticsKunstliche Intelligenz2018322–314314910.1007/s13218-018-0544-x J Fandinno (573_CR8) 2019; 19 573_CR6 573_CR5 573_CR4 573_CR34 573_CR3 J de Kleer (573_CR16) 1987; 32 573_CR35 573_CR10 573_CR32 573_CR1 573_CR33 573_CR30 E Kontopoulos (573_CR18) 2011; 24 573_CR31 P Menzies (573_CR27) 2020 G Ferrand (573_CR9) 2006; 25 Y LeCun (573_CR24) 1998; 86 N Katzouris (573_CR15) 2016; 16 M Gelfond (573_CR12) 2013; 23 M Sridharan (573_CR42) 2019; 33 573_CR26 T Miller (573_CR29) 2019; 267 PH Winston (573_CR43) 2018 E Erdem (573_CR7) 2018; 32 P Langley (573_CR22) 2017 W Samek (573_CR38) 2017; 1 M Friedman (573_CR11) 1974; 71 GA Miller (573_CR28) 1995; 38 M Sridharan (573_CR41) 2018; 7 S Lewandowsky (573_CR25) 2000; 6 G Antoniou (573_CR2) 2008; 64 573_CR23 573_CR45 573_CR44 SJ Read (573_CR36) 1993; 65 JE Laird (573_CR20) 2012 573_CR19 573_CR17 573_CR39 573_CR14 M Gelfond (573_CR13) 2014 573_CR37 M Sridharan (573_CR40) 2019; 65 JE Laird (573_CR21) 2017; 32
References_xml	– reference: LewandowskySMundyMTanGThe dynamics of trust: comparing humans to automationJ Exp Psychol Appl.20006210410.1037/1076-898X.6.2.104 – reference: Koh PW, Liang P. Understanding black-box predictions via influence functions. In: International conference on machine learning. pp. 1885–1894. 2017. – reference: LairdJEThe soar cognitive architecture2012CambridgeThe MIT Press10.7551/mitpress/7688.001.0001 – reference: Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Neural information processing systems, pp. 1097–1105. 2012. – reference: ReadSJMarcus-NewhallAExplanatory coherence in social explanations: a parallel distributed processing accountPers Soc Psychol.199365342910.1037/0022-3514.65.3.429 – reference: FriedmanMExplanation and scientific understandingPhilosophy1974711519 – reference: Mota T, Sridharan M. Commonsense reasoning and knowledge acquisition to guide deep learning on robots. In: Robotics science and systems. 2019. – reference: LairdJEGluckKAndersonJForbusKDJenkinsOCLebiereCSalvucciDScheutzMThomazATraftonGWrayREMohanSKirkJRInteractive task learningIEEE Intell Syst.201732462110.1109/MIS.2017.3121552 – reference: Mota T, Sridharan M. Incrementally grounding expressions for spatial relations between objects. In: International joint conference on artificial intelligence, pp. 1928–1934. 2018. – reference: Ribeiro M, Singh S, Guestrin C. Why should I trust you? Explaining the predictions of any classifier. In: International conference on knowledge discovery and data mining, pp. 1135–1144. 2016. – reference: SridharanMMeadowsBKnowledge representation and interactive learning of domain knowledge for human-robot collaborationAdv Cogn Syst.20187120 – reference: FerrandGLessaintWTessierAExplanations and proof treesComput. Inform.2006251001102122476961132.68677 – reference: Mota T, Sridharan M. Commonsense reasoning and deep learning for transparent decision making in robotics. In: European conference on multiagent systems. 2020. – reference: Zhang Y, Sreedharan S, Kulkarni A, Chakraborti T, Zhuo HH, Kambhampati S. Plan explicability and predictability for robot task planning. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R editors. International conference on robotics and automation, vol. 31, pp. 313–1320. 2017. – reference: de KleerJWilliamsBCDiagnosing multiple faultsArtif Intell.1987329713010.1016/0004-3702(87)90063-4 – reference: FandinnoJSchulzCAnswering the “Why” in answer set programming: a survey of explanation approachesTheory and Practice of Logic Programming2019192114203391236910.1017/S1471068418000534 – reference: Bercher P, Biundo S, Geier T, Hoernle T, Nothdurft F, Richter F, Schattenberg B. Plan, repair, execute, explain - how planning helps to assemble your home theater. In: Twenty-fourth international conference on automated planning and scheduling. 2014 – reference: LangleyPMeadowsBSridharanMChoiDExplainable agency for intelligent autonomous systemsInnovative applications of artificial intelligence2017CambridgeAAAI Press – reference: MillerGAWordNet: a lexical database for EnglishCommun ACM.19953811394110.1145/219717.219748 – reference: Chai JY, Gao Q, She L, Yang S, Saba-Sadiya S, Xu G. Language to action: towards interactive task learning with physical agents. In: International joint conference on artificial intelligence. 2018. – reference: ErdemEPatogluVApplications of ASP in roboticsKunstliche Intelligenz2018322–314314910.1007/s13218-018-0544-x – reference: KatzourisNArtikisAPaliourasGOnline learning of event definitionsTheory Pract Logic Programm.2016165–6817833356935110.1017/S1471068416000260 – reference: SridharanMMeadowsBTowards a theory of explanations for human-robot collaborationKunstliche Intelligenz201933433134210.1007/s13218-019-00616-y – reference: Fox M, Long D, Magazzeni D. Explainable planning. In: IJCAI workshop on explainable AI. 2017. – reference: KontopoulosEBassiliadesNAntoniouGVisualizing semantic web proofs of defeasible logic in the dr-device systemKnowl Based Syst.201124340641910.1016/j.knosys.2010.12.001 – reference: Gil Y. Learning by experimentation: incremental refinement of incomplete planning domains. In: International conference on machine learning, pp. 87–95. 1994. – reference: McGuinness DL, Glass A, Wolverton M, Da Silva PP. Explaining task processing in cognitive assistants that learn. In: AAAI spring symposium: interaction challenges for intelligent assistants, pp. 80–87. 2007. – reference: SridharanMGelfondMZhangSWyattJREBA: a refinement-based architecture for knowledge representation and reasoning in roboticsJ Artif Intell Res20196587180399043610.1613/jair.1.11524 – reference: SamekWWiegandTMüllerKRExplainable artificial intelligence: understanding, visualizing and interpreting deep learning ModelsITU J ICT Discov Impact Artif Intell Commun Netw Serv20171110 – reference: Norcliffe-Brown W, Vafeais E, Parisot S. Learning conditioned graph structures for interpretable visual question answering. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R. editors. Advances in neural information processing systems, vol. 31. Montreal, Canada. 2018. – reference: GelfondMKahlYKnowledge representation, reasoning and the design of intelligent agents2014CambridgeCambridge University Press10.1017/CBO9781139342124 – reference: AntoniouGBikakisADimaresisNGenetzakisMGeorgalisGGovernatoriGKarouzakiEKazepisNKosmadakisDKritsotakisMProof explanation for a nonmonotonic semantic web rules languageData Knowl. Eng.200864366268710.1016/j.datak.2007.10.006 – reference: Anjomshoae S, Najjar A, Calvaresi D, Framling K. Explainable agents and robots: results from a systematic literature review. In: International conference on autonomous agents and multiagent systems. Montreal, Canada. 2019. – reference: Borgo R, Cashmore M, Magazzeni D. Towards providing explanations for AI planner decisions. In: IJCAI workshop on explainable artificial intelligence, pp. 11–17. 2018. – reference: MillerTExplanations in artificial intelligence: insights from the social sciencesArtif Intell.2019267138387451110.1016/j.artint.2018.07.007 – reference: Assaf R, Schumann A. Explainable deep neural networks for multivariate time series predictions. In: International joint conference on artificial intelligence, Macao, China, pp. 6488–6490. 2019. – reference: LeCunYBottouLBengioYHaffnerPGradient-based learning applied to document recognitionProc IEEE.199886112278232410.1109/5.726791 – reference: Someya Y. Lemma list for English language. 1998. – reference: Yi K, Wu J, Gan C, Torralba A, Kohli P, Tenenbaum JB. Neural-symbolic VQA: disentangling reasoning from vision and language understanding. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, and Garnett R. editors. Advances in neural information processing systems. Montreal, Canada. 2018. – reference: GelfondMInclezanDSome properties of system descriptions of ALd\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$AL_d$$\end{document}J Appl Non Class Logics Spec Issue Equilib Logic Answ Set Programm.2013231–210512010.1080/11663081.2013.798954 – reference: Mota T, Sridharan M. Answer me this: constructing disambiguation queries for explanation generation in robotics. In: Workshop of the UK planning and scheduling special interest group. 2020. – reference: WinstonPHHolmesDThe genesis enterprise: taking artificial intelligence to another level via a computational account of human story understandingComputational models of human intelligence report 12018CambridgeMassachusetts Institute of Technology – reference: Law M, Russo A, Broda K. The ILASP system for inductive learning of answer set program. Technical report on arXiV. 2020. https://arxiv.org/abs/2005.00904. – reference: MenziesPBeebeeHZaltaENCounterfactual theories of causationThe Stanford encyclopedia of philosophy20202020StanfordStanford University – reference: Mota T, Sridharan M. 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SubjectTerms	Actuation Advances in Multi-Agent Systems Research: EUMAS 2020 Extended Selected Papers Algorithms Classification Cognition & reasoning Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Decision making Deep learning Descriptions Image manipulation Information Systems and Communication Service Knowledge representation Logic Original Research Pattern Recognition and Graphics Planning Reasoning Robot learning Robotics Robots Scene analysis Semantic web Software Engineering/Programming and Operating Systems Vision
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Title	Integrated Commonsense Reasoning and Deep Learning for Transparent Decision Making in Robotics
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