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Depicting probabilistic context awareness knowledge in deliberative architectures

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Abstract

Facing long-term autonomy with a cognitive architecture raises several difficulties for processing symbolic and sub-symbolic information under different levels of uncertainty, and deals with complex decision-making scenarios. For reducing environment uncertainty and simplify the decision-making process, this paper establishes a method for translating robot knowledge to a conceptual graph to later extract probabilistic context information that allows to bound of the actions present at the deliberative layer. This research develops two ROS components, one for translating robot knowledge to the conceptual graphs and one for extracting context knowledge from this graph using Bayesian networks. We evaluate these components in a real-world scenario, performing a task where a robot notifies to a user a message of an event at home. Our results show an improvement in task completion when using our approach, decreasing the planning requests by 65% and doing the task in a third of the time.

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References

  • Arias M, Díez F, Palacios-Alonso M, Yebra M, Fernández J (2012) POMDPs in OpenMarkov and ProbModelXML. In: Seventh annual workshop on multiagent sequential decision-making under uncertainty, Valencia, Spain, pp 1–8

  • Arias M, Pérez-Martín J, Luque M, Díez FJ (2019) OpenMarkov, an open-source tool for probabilistic graphical models. In: Proceedings of the 28th international joint conference on artificial intelligence. AAAI Press, pp 6485–6487 (2019)

  • Bermejo Í, Oliva J, Dıez FJ, Arias M (2012) Interactive learning of Bayesian networks using OpenMarkov. Spain, CISIAD, UNED, Madrid

  • Cacciabue P, Decortis F, Drozdowicz B, Mancini G, Masson M, Nordvik J (1992) A cognitive model in a blackboard architecture: synergism of AI and psychology. Reliab Eng Syst Saf 36(3):187–197. https://doi.org/10.1016/0951-8320(92)90065-S

    Article  Google Scholar 

  • Cashmore M, Fox, M, Long D, Magazzeni D, Ridder B, Carrera A, Palomeras N, Hurtos N, Carreras M (2015) ROSPlan: planning in the robot operating system. In: Twenty-fifth international conference on automated planning and scheduling

  • Clavero JG, Rodriguez FJ, Rico FM, Guerrero ÁM, Matellán V (2019) Using probabilistic context awareness in a deliberative planner system. In: IWINAC (2), lecture notes in computer science, vol 11487. Springer, pp 157–166

  • Coles AJ, Coles AI, Fox M, Long D (2010) Forward-chaining partial-order planning. In: Twentieth international conference on automated planning and scheduling

  • De Graaf MM, Allouch SB (2013) Exploring influencing variables for the acceptance of social robots. Robot Auton Syst 61(12):1476–1486

    Article  Google Scholar 

  • Fox D, Burgard W, Thrun S (1997) The dynamic window approach to collision avoidance. IEEE Robot Autom Mag 4(1):23–33

    Article  Google Scholar 

  • Fox M, Long D (2011) PDDL2.1: an extension to PDDL for expressing temporal planning domains. CoRR http://arxiv.org/abs/1106.4561

  • Ghallab M, Howe A, Knoblock C, Mcdermott D, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL—the planning domain definition language. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.212

  • Gins J, Martn F, Vargas D, Rodrguez FJ, Matelln V (2019) Social navigation in a cognitive architecture using dynamic proxemic zones. Sensors 19(23):5189. https://doi.org/10.3390/s19235189

    Article  Google Scholar 

  • Gomez-Marin A, Paton JJ, Kampff AR, Costa RM, Mainen ZF (2014) Big behavioral data: psychology, ethology and the foundations of neuroscience. Nat Neurosci 17(11):1455–1462

    Article  Google Scholar 

  • International Organization for Standardization (2007) Information technology common logic (CL): a framework for a family of logic-based languages. Standard, International Organization for Standardization, ISO/IEC JTC 1/SC 32 Data management and interchange, Geneva, CH

  • Jaramillo CMZ, Gelbukh A, Isaza FA (2006) Pre-conceptual schema: a conceptual-graph-like knowledge representation for requirements elicitation. In: Mexican international conference on artificial intelligence. Springer, pp 27–37

  • Koenig N, Howard A (2004) Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS) (IEEE Cat. No. 04CH37566), vol 3, pp 2149–2154

  • Latombe JC, Lazanas A, Shekhar S (1991) Robot motion planning with uncertainty in control and sensing. Artif Intell 52(1):1–47

    Article  MathSciNet  MATH  Google Scholar 

  • Lemaignan S, Warnier M, Sisbot EA, Clodic A, Alami R (2017) Artificial cognition for social human–robot interaction: an implementation. Artif Intell 247:45–69. https://doi.org/10.1016/j.artint.2016.07.002

    Article  MathSciNet  Google Scholar 

  • Levesque HJ (1986) Knowledge representation and reasoning. Annu Rev Comput Sci 1(1):255–287

    Article  MathSciNet  Google Scholar 

  • Li X, Zhang S, Huang R, Huang B, Xu C, Zhang Y (2018) A survey of knowledge representation methods and applications in machining process planning. Int J Adv Manuf Technol 98(9–12):3041–3059

    Article  Google Scholar 

  • Liao L, Fox D, Kautz H (2006) Location-based activity recognition. Adv Neural Inf Process Syst 18:787

    Google Scholar 

  • Lima O, Ventura R, Awaad I (2018) Integrating classical planning and real robots in industrial and service robotics domains. In: Proceedings of the 6th workshop on planning and robotics (PlanRob) at ICAPS

  • Malfaz M, Castro-González Á, Barber R, Salichs MA (2011) A biologically inspired architecture for an autonomous and social robot. IEEE Trans Auton Ment Dev 3(3):232–246

    Article  Google Scholar 

  • Manso LJ, Bustos P, Bachiller P, Núñez P (2015) A perception-aware architecture for autonomous robots. Int J Adv Robot Syst 12(12):174

    Google Scholar 

  • Marder-Eppstein E, Berger E, Foote T, Gerkey B, Konolige K (2010) The office marathon: robust navigation in an indoor office environment. In: 2010 IEEE international conference on robotics and automation. IEEE, pp 300–307

  • Neumann B, Hotz L, Rost P, Lehmann J (2014) A robot waiter learning from experiences. In: International workshop on machine learning and data mining in pattern recognition. Springer, pp 285–299

  • Rodríguez-Lera FJ, Martín-Rico F, Matelián-Olivera V (2018a) Generating symbolic representation from sensor data: inferring knowledge in robotics competitions. In: 2018 IEEE international conference on autonomous robot systems and competitions (ICARSC). IEEE, pp 261–266

  • Rodríguez-Lera FJ, Matellán-Olivera V, Conde-González MÁ, Martín-Rico F (2018b) HiMoP: a three-component architecture to create more human-acceptable social-assistive robots. Cogn Process 19(2):233–244

    Article  Google Scholar 

  • Sowa JF (1999) Knowledge representation: logical, philosophical and computational foundations. Brooks/Cole Publishing Co, New York

    Google Scholar 

  • Sowa JF (2008) Conceptual graphs. Found Artif Intell 3:213–237

    Article  Google Scholar 

  • Wallace JW, Kambouris SJ (2016) A language, framework, and SDK for robotic communications, integration, and interoperability. In: 2016 international conference on computational science and computational intelligence (CSCI). IEEE, pp 472–477

  • Zhu C, Sheng W (2011) Motion-and location-based online human daily activity recognition. Pervas Mobile Comput 7(2):256–269

    Article  Google Scholar 

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Authors and Affiliations

  1. Escuela Internacional de Doctorado, Rey Juan Carlos University, Madrid, Spain

    Jonatan Ginés

  2. Intelligent Robotics Lab, Rey Juan Carlos University, Madrid, Spain

    Francisco J. Rodríguez-Lera

  3. Escuela de Ingenieras Industrial e Informática, University of León, León, Spain

    Francisco Martín & Ángel Manuel Guerrero

  4. Supercomputación Castilla y León, SCAYLE, León, Spain

    Vicente Matellán

Authors
  1. Jonatan Ginés
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  2. Francisco J. Rodríguez-Lera
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  3. Francisco Martín
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  4. Ángel Manuel Guerrero
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  5. Vicente Matellán
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Correspondence to Jonatan Ginés.

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This work has been partially funded by Ministerio de Economía and Competitividad of the Kingdom of Spain under Project RTI2018-100683-B-I00.

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Ginés, J., Rodríguez-Lera, F.J., Martín, F. et al. Depicting probabilistic context awareness knowledge in deliberative architectures. Nat Comput 21, 565–576 (2022). https://doi.org/10.1007/s11047-020-09798-z

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  • DOI: https://doi.org/10.1007/s11047-020-09798-z

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