More Web Proxy on the site http://driver.im/

research-article

Incorporating human intention into self-adaptive systems

Authors:

Pedro MirandaAuthors Info & Claims

ICSE '15: Proceedings of the 37th International Conference on Software Engineering - Volume 2

Pages 571 - 574

Published: 16 May 2015 Publication History

Abstract

Self-adaptive systems are fed with contextual information from the environments in which the systems operate, from within themselves, and from the users. Traditional self-adaptive systems research has focused on inputs of systems performance, resources, exception, and error recovery that drive systems' reaction to their environments. The intelligent ability of these self-adaptive systems is impoverished without knowledge of a user's covert attention (thoughts, emotions, feelings). As a result, it is difficult to build effective systems that anticipate and react to users' needs as projected by covert behavior. This paper presents the preliminary research results on capturing users' intention through neural input, and in reaction, commanding actions from software systems (e.g., load an application) based on human intention. Further, systems can self-adapt and refine their behaviors driven by such human covert behavior. The long-term research goal is to incorporate and synergize human neural input. Thus establishing software systems with a self-adaptive capability to "feel" and "anticipate" users intentions and put the human in the loop.

References

[1]

Cognionics 64-channel wireless EEG Headset, retrieved Nov. 2014. www.cognionics.com

[2]

D. Garlan (2013). A 10-year perspective on software engineering self-adaptive systems (Keynote), Proc. 8^th Int. Symp. on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2013), p. 2.

Digital Library

[3]

D. Garlan, et al. (2009). Architecture-based self-adaptation, in: Autonomic Comp and Networking, pp. 31--55.

[4]

E. Yin, Z. Zhou, J. Jiang, F. Chen, Y. Liu, D. Hu (2014). A Speedy Hybrid BCI Spelling Approach Combining P300 and SSVEP: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 61(2): 473--483

[5]

EPOC, retrieved Nov. 2014. emotiv.com/epoc/features.php

[6]

G. Schirner, et al. (2013). The Future of Human-in-the-Loop Cyber-Physical Systems, IEEE Computer 46(1):36--45.

Digital Library

[7]

H. Müller, M. Pezzè, M. Shaw (2008). Visibility of control in adaptive systems, 2nd Int. Workshop on Ultra-Large-Scale Software-Intensive Systems (ULSSIS 2008), pp. 23--26.

Digital Library

[8]

IBM Smarter Planet Initiative, retrieved Nov. 2014 http://www.ibm.com/smarterplanet/us/en/?ca=v_smarterplanet

[9]

J. A. S. Kelso, G. Dumas, E. Tognoli (2013). Outline of a general theory of behavior and brain coordination. Neural Networks 37:120--131.

Digital Library

[10]

J. O. Kephart, D. M. Chess (2003). The Vision of Autonomic Computing, in: IEEE Computer 36(1): 41--50.

Digital Library

[11]

L. Trejo, R. Rosipal, B. Matthews (2006) Brain--Computer Interfaces for 1-D and 2-D Cursor Control: Designs Using Volitional Control of the EEG Spectrum or Steady-State Visual Evoked Potentials: IEEE Trans. on neural systems and rehabilitation engineering, 14(2): 225--229

[12]

P. Tallgren, S. Vanhatalo, K. Kaila, J. Voipio (2005) Evaluation of commercially available electrodes and gels for recording of slow EEG potentials: Clinical Neurophysiology, 116: 799--806

[13]

R. Calderon (2007). Brain Computer Interface and Neuroprosthetics, M. Sc. Thesis, Florida Atlantic University.

[14]

R. de Lemos, et al. (2013). Software engineering for self-adaptive systems: A second research roadmap, in: Software Engineering for Self-Adaptive Systems II, LNCS 7475:1--32.

[15]

R. P. N. Rao (2013). Brain-Computer Interfacing: An Introduction. Cambridge University Press.

[16]

ReThink Medical Next Generation Wireless EEG, retrieved Feb. 2014 www.rethinkmedical.com

[17]

S. Amiri, A. Rabbi, L. Azinfar, R. Fazel-Rezai (2013). A Review of P300, SSVEP, and Hybrid P300/SSVEP Brain-Computer Interface Systems: Brain-Computer Interface Systems -- Recent Progress and Future Prospects. pp. 197--213.

[18]

S. Debener, et al. (2012). How about taking a low-cost, small, and wireless EEG for a walk? Psychophysiology 49(11):1617--1621.

[19]

S. Huang, E. Tognoli, (2014). Brainware: Synergizing software systems and neural inputs, in: Proc. 36^th International conference on software engineering (ICSE 2014), pp. 444--447.

Digital Library

[20]

Wearable Sensing, retrieved Nov. 2013 wearablesensing.com

[21]

Z. Zhang, X. Li, Z. Deng, (2010) A CWT-based SSVEP Classification Method for Brain-Computer Interface System: International Conference on Intelligent Control and Information Processing: 43--48.

Cited By

Condori-Fernandez NLopez FNovielli NBegel AMaalej W(2017)Using emotions to empower the self-adaptation capability of software servicesProceedings of the 2nd International Workshop on Emotion Awareness in Software Engineering10.5555/3105556.3105560(15-21)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.5555/3105556.3105560
Lloyd EHuang STognoli EGarlan DNuseibeh B(2017)Improving human-in-the-loop adaptive systems using brain-computer interactionProceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1109/SEAMS.2017.1(163-174)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.1109/SEAMS.2017.1

Index Terms

Incorporating human intention into self-adaptive systems
1. Human-centered computing
  1. Interaction design
    1. Interaction design process and methods
      1. User centered design
2. Software and its engineering

Recommendations

Brainware: synergizing software systems and neural inputs
ICSE Companion 2014: Companion Proceedings of the 36th International Conference on Software Engineering

The rapid advances in the field of Brain Computer Interfaces (BCI) are expected to enrich the quality of people’s lives. BCI connects computer actions with neural inputs—signals indicating the user’s intentions, desired actions, attention, thoughts, ...
Towards Understanding Trust in Self-adaptive Systems
SEAMS '24: Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

Self-adaptive systems (SASs) can change their structures autonomously and dynamically adapt their behaviors aiming at (i) attaining longer-term system goals and (ii) coping with inevitable dynamics and changes in their operational environments that are ...
Adaptive Knowledge Bases in Self-Adaptive System Design
SEAA '15: Proceedings of the 2015 41st Euromicro Conference on Software Engineering and Advanced Applications

Self-adaptive systems allow for flexible solutions in changing environments. Usually, a fixed set of predefined rules is used to define the adaptation possibilities of a system. The main problem of such systems is to cope with environment behaviours ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '15: Proceedings of the 37th International Conference on Software Engineering - Volume 2

May 2015

1058 pages

General Chair:
Antonia Bertolino
ISTI-CNR, Italy
,
Program Chairs:
Gerardo Canfora
University of Sannio, Italy
,
Sebastian Elbaum
University of Nebraska-Lincoln

Sponsors

ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\DATC: IEEE Computer Society
TCSE: IEEE Computer Society's Tech. Council on Software Engin.

Publisher

IEEE Press

Publication History

Published: 16 May 2015

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICSE '15

Sponsor:

ACM
SIGSOFT
IEEE-CS\DATC
TCSE

ICSE '15: 37th International Conference on Software Engineering

May 16 - 24, 2015

Florence, Italy

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
133
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Condori-Fernandez NLopez FNovielli NBegel AMaalej W(2017)Using emotions to empower the self-adaptation capability of software servicesProceedings of the 2nd International Workshop on Emotion Awareness in Software Engineering10.5555/3105556.3105560(15-21)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.5555/3105556.3105560
Lloyd EHuang STognoli EGarlan DNuseibeh B(2017)Improving human-in-the-loop adaptive systems using brain-computer interactionProceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1109/SEAMS.2017.1(163-174)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.1109/SEAMS.2017.1

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents