Skip to main content
SpringerLink
Log in

A task manager using an ontological framework for a HARMS-based system

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Recently, conversational interaction with technology has made its way into popular commercial use. Advancements in natural language processing have made that possible. Now, imagine a future where the average person can have a team of robots and smart devices working together to accomplish daily tasks and all one has to do is interact with the system naturally. For this theoretical team to exist, an interaction system must be developed that can translate utterances and autonomously organize and direct the agents into completing the task. This paper presents a task manager that uses on ontology to divide tasks into subtasks and finds the most capable agent to complete the task. The task manager is an extension of a robust dialogue manager that users can communication naturally with. Two ontologies were developed as initial steps towards a task manager for a multi-agent system. An experiment was conducted to test the combination of the dialogue manager and the task manager.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Aly A, Tapus A (2013) A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human–robot interaction. In: 2013 8th ACM/IEEE international conference on human–robot interaction (HRI), pp 325–332. doi:10.1109/HRI.2013.6483606

  • Cantrell R, Talamadupula K, Schermerhorn P, Benton J, Kambhampati S, Scheutz M (2012) Tell me when and why to do it! run-time planner model updates via natural language instruction. In: 2012 7th ACM/IEEE international conference on human–robot interaction (HRI), pp 471–478

  • Chiu Y, Raj B, Stern R (2009) Towards fusion of feature extraction and acoustic model training: a top down process for robust speech recognition. INTERSPEECH, pp 1–4

  • CMU Sphinx: Open source speech recognition toolkit (2015) Website. Retrieved from http://cmusphinx.sourceforge.net/

  • De Marneffe M-C, MacCartney B, Manning CD (2006) Generating typed dependency parses from phrase structure parses. In: Proceedings of the 5th international conference on language resources and evaluation (LREC 2006), pp 449–454 (ISBN: 978-1-905593-50-7)

  • Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220. doi:10.1006/knac.1993.1008

    Article  Google Scholar 

  • Hong JH, Min B-C, Taylor JM, Raskin V, Matson ET (2012) NL-based communication with firefighting robots. In: IEEE 2012 international conference on systems, man, and cybernetics (SMC), pp 1461–1466. doi:10.1109/ICSMC.2012.6377941

  • Horrocks I (2005) Description logics in ontology applications. In: Beckert B (ed) Automated reasoning with analytic tableaux and related methods, vol 3702. Springer, Berlin, pp 2–13. doi:10.1007/11554554_2

  • Huwel S, Wrede B, Sagerer G (2006) Robust speech understanding for multi-modal human-robot communication. In: The 15th IEEE international symposium on robot and human interactive communication, 2006. ROMAN 2006, pp 45–50. doi:10.1109/ROMAN.2006.314393

  • Jena Ontology API (2013). https://jena.apache.org/. Accessed 28 Oct 2015

  • Klein D, Manning CD (2003) Fast extract inference with a factored model for natural language parsing. In: Advances in neural information processing systems, vol 15 (NIPS 2002), pp 3–10

  • Klein M, Schmidt A, Lauer R (2007) Ontology-centred design of an ambient middleware for assisted living: the case of soprano. In: Towards ambient intelligence: methods for cooperating ensembles in ubiquitous environments (AIM-CU), 30th annual German conference on artificial intelligence (KI 2007)

  • Lewis J, Matson ET, Wei S, Min B-C (2013) Implementing harms-based indistinguishability in ubiquitous robot organizations. Robot Auton Syst 61(11):1186–1192. doi:10.1016/j.robot.2013.04.001

    Article  Google Scholar 

  • Marneffe M-CD, Manning CD (2010) Stanford typed dependencies manual 40(September):1–22. http://nlp.stanford.edu/software/dependencies_manual.pdf. Accessed 28 Oct 2015

  • Matson E, Min B-C (2011) M2m infrastructure to integrate humans, agents and robots into collectives. In: 2011 IEEE instrumentation and measurement technology conference (I2MTC), pp 1–6. doi:10.1109/IMTC.2011.5944359

  • Mirnig N, Weiss A, Tscheligi M (2011) A communication structure for human-robot itinerary requests. In: 2011 6th ACM/IEEE international conference on human–robot interaction (HRI), pp 205–206

  • Noy NF, Mcguinness DL (2001) Ontology development 101: a guide to creating your first ontology (Technical Report). Stanford University

  • Quindere M, Lopes LS, Teixeira AJ (2013) Evaluation of a dialogue manager for a mobile robot. In: Ro-man, 2013 IEEE, pp 126–132. doi:10.1109/ROMAN.2013.6628466

  • Russell S, Norvig P (2009) Artificial intelligence: a modern approach. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Ryker A, Matson E, Kim S, Lee S, Jang I (2015) Implementing a harms-based software system for use in collective robotics applications. In: 2015 6th international conference on automation, robotics and applications (ICARA), pp 416–420. doi:10.1109/ICARA.2015.7081184

  • Scherp A, Franz T, Saathoff C, Staab S (2012) A core ontology on events for representing occurrences in the real world. Multimedia Tools Appl 58(2):293–331. doi:10.1007/s11042-010-0667-z

    Article  Google Scholar 

  • Seabra-Lopes L, Teixeira A (2000) Human–robot interaction through spoken language dialogue. In: 2000 IEEE/RSJ international conference on intelligent robots and systems, 2000 (IROS 2000). Proceedings, vol 1, pp 528–534. doi:10.1109/IROS.2000.894658

  • Seedorf S, Informatik FF, Mannheim U (2006) Applications of ontologies in software engineering. In: 2nd international workshop on semantic web enabled software engineering (SWESE 2006), held at the 5th international semantic web conference (ISWC 2006)

  • Shin S, Matson ET, Park J, Yang B, Lee J, Jung J-W (2015) Speech-to-speech translation humanoid robot in doctor’s office. In: 2015 6th international conference on automation, robotics and applications (ICARA), pp 484–489. doi:10.1109/ICARA.2015.7081196

  • Skubic M, Shultz D, Adams W (2002) Using spatial language in a human–robot dialogue. In: Proceedings of the international conference on robotics and automation, pp 4143–4148

  • Spiliotpoulos D, Androutspolulos I, Spyropoulos C (2001) Human–robot interaction based on spoken natural language dialogue. In: Proceedings of the European workshop on service and humanoid robots, pp 25–27

  • Stanford Dependencies (2015). http://nlp.stanford.edu/software/stanford-dependencies.shtml. Accessed 28 Oct 2015

  • The Stanford Parser: a statistical parser (2015). http://nlp.stanford.edu/software/lex-parser.shtml. Accessed 28 Oct 2015

  • W3c Semantic Web Activity (Technical Report) (2013). W3C

  • Wagoner AR, Matson ET (2015) A robust human–robot communication system using natural language for HARMS. Procedia Comput Sci 56:119–126. doi:10.1016/j.procs.2015.07.178

    Article  Google Scholar 

  • Ye J, Stevenson G, Dobson S (2011) A top-level ontology for smart environments. Pervasive Mob Comput 7(3):359–378. doi:10.1016/j.pmcj.2011.02.002 (Knowledge-driven activity recognition in intelligent environments)

    Article  Google Scholar 

  • Yuan X, Liu G (2012) A task ontology model for domain independent dialogue management. In: 2012 IEEE international conference on virtual environments human–computer interfaces and measurement systems (VECIMS) proceedings. IEEE, pp 148–153. doi:10.1109/VECIMS.2012.6273227

  • Zhou X, Zhang X, Hu X (2007) Dragon toolkit: incorporating auto-learned semantic knowledge into large-scale text retrieval and mining. In: 19th IEEE international conference on tools with artificial intelligence (ICTAI 2007), vol 2. IEEE, pp 197–201. doi:10.1109/IC-TAI.2007.117

Download references

Author information

Authors and Affiliations

  1. M2M Lab/Department of Computer and Information Technology, Purdue University, West Lafayette, IN, USA

    Amy R. Wagoner & Eric T. Matson

Authors
  1. Amy R. Wagoner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric T. Matson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eric T. Matson.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wagoner, A.R., Matson, E.T. A task manager using an ontological framework for a HARMS-based system. J Ambient Intell Human Comput 7, 457–463 (2016). https://doi.org/10.1007/s12652-016-0378-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-016-0378-z

Keywords

Search

Navigation