ComPlexUs 2003Spread of Crime

Traffic jams

Marriage

Complex Systems Theory and MethodsAgent-based modeling (including cell automata)

Chaos, Fractals, & Dynamical systems theoryComplex Network science

Game TheoryMulti-scale modelingDistributed control

Nonlinear pattern recognition and data miningEvolutionary, Adaptive, Developmental approaches

Bio-inspired Computing (ANNs, EC)Etc.

Data Collection TechnologyHigh-throughput Microarrays

Molecular ImagingDistributed Sensor Networks

Web mining, Etc.

Computational CapabilitiesMassive Data Storage

Rapid Data AccessProcessor speed

Distributed and parallel computingEtc.

Increasing Recognition of

the Limits of Reductionism

Complex Systems Science is at an exciting time of Maturation

712.1.2011Älytekniikan ja konenäön uusia mahdollisuuksia / Pekka

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Emergenssi

Applications

9

• In the security sector affective behavioural cues play a crucial role in establishing or detracting from credibility

• In the medical sector, affective behavioural cues are a direct means to identify when specific mental processes are occurring

• Neurology (in studies on dependence between emotion dysfunction or impairment and brain lesions)

• Psychiatry (in studies on schizophrenia and mood disorders)• Dialog/Automatic call center Environment – to reduce

user/customer frustration• Academic learning• Human Computer Interaction (HCI)

Zhihong Zeng; Pantic, M.; Roisman, G.I.; Huang, T.S.; , "A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions," Pattern Analysis and Machine Intelligence

Future Research Directions

10

• So far Context has been overlooked in most Affection Computing researches

• Collaboration among Affection researchers from different disciplines

• Fast real-time processing• Multimodal detection and recognition to achieve

higher accuracy• On/Off focus• Systems that can model conscious and subconscious

user behaviour

Rafael A. Calvo, Sidney D'Mello, "Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications

Context Aware Multimodal Affection Analysis Based Smart Learning Environment

11

12Application System Architecture

Face

Analysis

Voice

Analysis

Posture

Analysis

Physiology

Analysis

System Controller

Hardw

are Calibration M

anager

Decision Support System

Multimedia Note

Reading Behavior Report

Lesson Length Suggestion

Class Efficiency Report

Multimodal Affect Input Parameter Adjustment

Output

13

Driver Emotion Aware Multiple Agent Controlled Automatic Vehicle

1414

Navigation Agent

Safety Agent

Driving Aid Agent

Affective Multimedia Agent

AudioLinguistic / Non-linguistic

Facial Expression

Seat Pressure

Actions •Steering Movement•Interaction with Gas / Break Paddle

Bio-signals

Stress LevelBasic Emotions

Feature Detector

Feature Detector

Feature Detector

…………...

Feature Estimator

Complex Emotions

……Route Selection

Inter agent communication to aid decision making

Notify in case of Emergency

Speed, ABS,Traction Control

Music, ClimateControl

Alert the Driver

Affective ComputingConcerned Issues

15

Privacy concerns [4] [5] I do not want the outside world to know

what goes through my mind…Twitter is the limit

Ethical concerns [5] Robot nurse or caregivers capable of

effective feedback Risk of misuse of the technology

In the hand of impostors Computers start to make emotionally

distorted, harmful decisions [18] Complex technology

Effectiveness is still questionable, risk of false interpretation

Conclusion

16

Strategic Business Insight (SBI) –

“Ultimately, affective-computing technology could eliminate the need for devices that today stymie and frustrate users…

Affective computing is an important development in computing, because as pervasive or ubiquitous computing becomes mainstream, computers will be far more invisible and natural in their interactions with humans.” [4]

Toyota’s thought controlled wheelchair [19]

17Picard, R. 1995. Affective Computing. M.I.T Media Laboratory Perceptual Computing Section Technical Report Picard, R. 1995. Affective Computing. The MIT Press

18

References

19

[1] Picard, R. 1995. Affective Computing. M.I.T Media Laboratory Perceptual Computing Section Technical Report No. 321[2] Picard, R. 1995. Affective Computing. The MIT Press. ISBN-10: 0-262-66115-2.[3] Picard, R., & Klein, J. (2002). Computers that recognize and respond to user emotion: Theoretical and practical implications. Interacting

With Computers, 14, 141-169. [4] http://www.sric-bi.com/[5] Bullington, J. 2005. ‘Affective’ computing and emotion recognition systems: The future of biometric surveillance? Information Security

Curriculum Development (InfoSecCD) Conference '05, September 23-24, 2005, Kennesaw, GA, USA. [6] Boehner, K., DePaula, R., Dourish, P. & Sengers, P. 2005. Affect: From Information to Interaction. AARHUS’05 8/21-8/25/05 Århus,

Denmark.[7] Zeng, Z. et al. 2004. Bimodal HCI-related Affect Recognition. ICMI’04, October 13–15, 2004, State College, Pennsylvania, USA.[8] Taleb, T.; Bottazzi, D.; Nasser, N.; , "A Novel Middleware Solution to Improve Ubiquitous Healthcare Systems Aided by Affective

Information," Information Technology in Biomedicine, IEEE Transactions on , vol.14, no.2, pp.335-349, March 2010[9] Khosrowabadi, R. et al. 2010. EEG-based emotion recognition using self-organizing map for boundary detection. International

Conference on Pattern Recognition, 2010.[10] R. Cowie, E. Douglas, N. Tsapatsoulis, G. Vostis, S. Kollias, w. Fellenz and J. G. Taylor, Emotion Recognition in Human-computer

Interaction. In: IEEE Signal Processing Magazine, Band 18 p.32 - 80, 2001.[11] Rafael A. Calvo, Sidney D'Mello, "Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications," IEEE

Transactions on Affective Computing, pp. 18-37, January-June, 2010 [12] Zhihong Zeng; Pantic, M.; Roisman, G.I.; Huang, T.S.; , "A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous

Expressions," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.31, no.1, pp.39-58, Jan. 2009[13] Norman, D.A. (1981). ‘Twelve issues for cognitive science’, Perspectives on Cognitive Science, Hillsdale, NJ: Erlbaum, pp.265–295.[14] R. Sharma, V. Pavlovic, and T. Huang. Toward multimodal human-computer interface. In Proceedings of the IEEE, 1998.[15] Vesterinen, E. (2001). Affective Computing. Digital media research seminar, spring 2001: “Space Odyssey 2001”.

References[16] Burkhardt, F.; van Ballegooy, M.; Engelbrecht, K.-P.; Polzehl, T.; Stegmann, J.; , "Emotion detection in dialog systems: Applications,

strategies and challenges," Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on , vol., no., pp.1-6, 10-12 Sept. 2009

[17] Leon, E.; Clarke, G.; Sepulveda, F.; Callaghan, V.; , "Optimised attribute selection for emotion classification using physiological signals," Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE , vol.1, no., pp.184-187, 1-5 Sept. 2004

[19] http://www.engadget.com/2009/06/30/toyotas-mind-controlled-wheelchair-boast-fastest-brainwave-anal/[20] http://www.w3.org/TR/2009/WD-emotionml-20091029/[21] M. Pantic, N. Sebe, J. F. Cohn, and T. Huang. Affective multimodal human-computer interaction. In ACM International Conference

on Multimedia (MM), 2005.[22] Gong, L., Wang, T., Wang, C., Liu, F., Zhang, F., and Yu, X. 2010. Recognizing affect from non-stylized body motion using shape of

Gaussian descriptors. In Proceedings of the 2010 ACM Symposium on Applied Computing (Sierre, Switzerland, March 22 - 26, 2010). SAC '10. ACM, New York, NY, 1203-1206.

[23] Khalili, Z.; Moradi, M.H.; , "Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG," Neural Networks, 2009. IJCNN 2009. International Joint Conference on , vol., no., pp.1571-1575, 14-19 June 2009

[24] Huaming Li and Jindong Tan. 2007. Heartbeat driven medium access control for body sensor networks. In Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments (HealthNet '07). ACM, New York, NY, USA, 25-30.

[25] Ghandi, B.M.; Nagarajan, R.; Desa, H.; , "Facial emotion detection using GPSO and Lucas-Kanade algorithms," Computer and Communication Engineering (ICCCE), 2010 International Conference on , vol., no., pp.1-6, 11-12 May 2010

[26] Lucey, P.; Cohn, J.F.; Kanade, T.; Saragih, J.; Ambadar, Z.; Matthews, I.; , "The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression," Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on , vol., no., pp.94-101, 13-18 June 2010

[27] Ruihu Wang; Bin Fang; , "Affective Computing and Biometrics Based HCI Surveillance System," Information Science and Engineering, 2008. ISISE '08. International Symposium on , vol.1, no., pp.192-195, 20-22 Dec. 2008

20

24

• Pepper is a humanoid robot by Aldebaran Robotics and SoftBank Mobile designed with the ability to read emotions.

• It was introduced in a conference on 5 June 2014, and has been showcased to the public at Softbank mobile stores in Japan since 6 June.

• It will be available on February 2015 at a base price of JPY 198,000 ($1,931) at Softbank Mobile stores.

• Pepper's emotion comes from the ability to analyze expressions and voice tones.

• The robot’s head has • 4 microphones, • 2 HD cameras (in the mouth and forehead), and • a 3-D depth sensor (behind the eyes).

• There is a gyroscope in the torso and touch sensors in the head and hands.

• The mobile base has two sonars, six lasers, three bumper sensors, and a gyroscope.

https://www.youtube.com/watch?v=kCFYw8mIqcc

RoboCup

• Robots must cooperate in…– Strategy acquisition– Real-time reasoning– Multi-agent collaboration– Competition against another

team of robots

RoboCup is an international research effort to promote autonomous robots.

RoboCup

• Each robot has…– Pentium 233MHz– Linux OS– Video camera and

frame grabber– Sensor System– Kicker

How to the robots make decisions?

• Control is based on a set of behaviors• Each behavior has a set of preconditions that either…

– Must be satisfied– Are desired

• A behavior is selected when all of the “musts” become true

• A behavior is selected from several behaviors based on how many desired conditions are true

Applications of AI and Robotics

• Industrial Automation• Services for the Disabled• Vision Systems• Planetary Exploration• Mine Site Clearing• Law Enforcement• And Many Others…

48Älytekniikan ja konenäön uusia mahdollisuuksia / Pekka

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49Älytekniikan ja konenäön uusia mahdollisuuksia / Pekka

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https://www.youtube.com/watch?v=3IFuv1AVouM

https://www.youtube.com/watch?v=hlHrvQ7D5OU

http://time.com/2917394/japan-human-robots/#2917394/japan-human-robots/

https://wtvox.com/robotics/top-10-humanoid-robots/

https://www.youtube.com/watch?v=IhVu2hxm07E