Tetsuo Yamabe

Distributed and Ubiquitous Computing Lab., Waseda University

A System Framework for Decision Support in Ambient Intelligence

12010年12月22日水曜日

Preface

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AugmentedTraditionalGames

MobilePedestrianNavigation

Activity-basedMicro-Pricing

Systems

Map Guideon

Public Displays

Case Study #1 Case Study #3

Case Study #4Case Study #2

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TEI’10, 11FNG’10IoT’10

MobiQuitous’08UbiComp’09Persuasive’10

ICPS’10

Case Study #1 Case Study #3

Case Study #4Case Study #2

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p. 5

Case studies

Case study #2Decision training with augmented traditional

games

Case study #3Decision inducement with

Activity-based Micro-Incentives

CitronA context acquisitionframework for mobile

sensor devices( )

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Outline

1. Introduction

2. Ambient Decision Support Systems

• ADSS System Framework

3. Case Studies

• Decision Training with Augmented Traditional Games

• Decision Inducement with Activity-based Micro-Incentives

4. Discussion

5. Conclusion and future work

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Introduction

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Decisions in everyday life

• Life is the outcome of decisions.

• “What’s for dinner tonight?” - Japanese, Chinese, Western-style...

• “How to go to office?” - by bus, train, walk...

• “Which university’s exam should I try?” - Waseda, Keio, Toudai...

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Limitations and fallacies in decision making

• Perceptual ability

• Skill, knowledge level

• Heuristics

• Bias

• Human error

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Decision Support Systems (DSS)

“A DSS is an interactive computer based system that helps decision makers utilize data and models to solve unstructured problems.” *

10

*Adapted from G. A. Gorry and M. S. S. Morton. A framework for management information systems. Sloan Management Review, 13(1):50–70, 1971.

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Decision support types

1. Decision aiding

• Capability enhancement

• Process automation

• Performance improvement

• Error correction and prevention

2. Decision training

• Evolution to the next expert level

11W. W. Zachary and J. M. Ryder. Decision support systems: Integrating decision aiding and decision training. Handbook of Human-Computer Interaction (Second Edition), pages 1235 – 1258, 1997.

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Organizational

Business

Stationary

PC

Personal

Personal use

Mobile

Environments

→

Traditional DSS ADSS

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Ambient Decision Support Systems

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Ambient Intelligence (AmI)

“AmI is a vision of the Information Society where the emphasis is on greater user-friendliness, more efficient services support, user-empowerment, and support for human interactions.

People are surrounded by intelligent intuitive interfaces that are embedded in all kinds of objects and an environment that is capable of recognizing and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way.” [Ducatel et. al, 2001]

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AmI key features and technologies

Key features

Embedded

Context aware

Personalized

Adaptive

Anticipatory

Enabling technologies

Very unobtrusive hardware

A seamless mobile/fixed communications infrastructure

Dynamic and massively distributed device networks

Natural feeling human interfaces

Dependability and security

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Decision support in AmI

•Ambient Decision Support Systems (ADSS) assist in an individual’s everyday decision making.

•Cognitive capability enhancement with sensors and actuators

•Sustainable decision support through implicit/explicit interaction

•Large-scale knowledge aggregation on cloud infrastructure

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AmI

DSS system architecture

17

User UserInterface

DialogMgmt System

DatabaseMgmt System

Model baseMgmt System

KnowledgeEngine

DSS

Surrounded by heterogeneoussmart objects

Augmented reality: seamlessly integrated

feedback

Context information acquired by sensors

Distributed over the cloud network

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Live activity logging and realtime multimodal feedbackVisual presentation of the record for further analysis

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ADSS key requirements

• Decision need to be made for not well structured problems while adapting to the contextual transition in social activities.

• Behavioral considerations of decision making have been ignored by those who create and build DSS.

• It is important to understand decision making process from cognitive perspective.

1. Endsley’s Situation Awareness model [Endsley, 1995]

2. Rasmussen’s SRK behavior model [Rasmussen, 1983]

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M. R. Endsley. Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1):32–64, 1995.

Endsley’s Situation Awareness model

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M. R. Endsley. Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1):32–64, 1995.

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M. R. Endsley. Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1):32–64, 1995.

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TaskSystem

EnvironmentsADSS

User(Situation Awareness model)

ADSS System Framework

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262010年12月22日水曜日

Case Studies

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Decision Training with Augmented Traditional GamesCS#2(Chapter 5)

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System concept

• Objective

• Support decision making in traditional games, by providing feedback/feedforward in a seamlessly integrated way.

29

• Decision support

• Visualize/sonify the information that novices cannot well recognize, in order to improve the situation awareness on current status.

ADSS

User

Visualization

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Ex. 1) Augmented Go

30

T. Iwata, T. Yamabe, M. Polojarvi, and T. Nakajima. 2010. Traditional games meet ICT: a case study on go game augmentation. In Proceedings of the fourth international conference on Tangible, embedded, and embodied interaction (TEI '10). ACM, , 237-240.

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Characteristics as an ADSS

Characteristics

Structured rules and restrictions

Stationary

Immersion

Emotional

Technologies

Augmented reality

Tangible interaction

Context awareness

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Ex. 2) EmoPoker

• Improve decision performance under uncertainty.

• “It is well established that intense drive states such as hunger, pain, sexual arousal, drug cravings, and sleep deprivation produce breakdowns in self-control and increase people’s willingness to take risks in order to alleviate the drive state.” *

• Biofeedback for improving self-control of emotional arousal.

• Auditory feedback which emulates heartbeat sound of the player

32

* Adapted from M. Pham. Emotion and rationality: A critical review and interpretation of empirical evidence. Review of General Psychology, Jan 2007.

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EmoPoker System

33

T. Yamabe, I. Kosunen, I. Ekman, LA. Liikkanen, K. Kuikkaniemi, and T. Nakajima, Biofeedback Training with EmoPoker: Controlling Emotional Arousalfor Better Poker Play. Fun and Games Conference 2010 (Fun and Games’10)

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EmoPoker System

34

Physiologicalresponse

Emotional arousaldetection

Biofeedback Mobile PC Player

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Biofeedback user study

fEMG (facial electromyography)• 6 nodes to face• Positive/negative emotions

EDA (electrodermal activity)• 2 nodes to fingers• Arousal level

RESP (respiration)!• 1 node around chest

HR (Heart rate)• 1 node around chest• Emotional arousal

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Biofeedback user study

• 5 min offline poker play x 8 rounds = 40 min in total

• 4 conditions x 2 sets

• Feedback {ON, OFF} x Tilt mode {ON, OFF}

• 8 subjects in Finland

• Volunteers gathered by mailing list

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Results

• 5 subjects data could be used for statistical analysis.

• Linear Mixed Model for repeated measurements.

• The tilt mode increased emotional arousal (p<.05).

• The biofeedback slightly decreased the arousal.

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Findings from the experiment

• Informativeness depends on the expertise level.

• “Tell as it is” is not useful especially for novice players.

• Preliminary knowledge helps to interpret the ambient message.

• Biofeedback design to induce autonomous response

• Breathing pattern indication for calm heated brain.

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Decision Inducement with Activity-based Micro-IncentivesCS#3(Chapter 6)

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System concept

• Objective

• Induce consumers’ decision behavior towards more desirable decision patterns with incentives.

40

• Decision support

• Induce desirable decisions by providing continuos economic incentives to activities.

ADSS

User

Incentives

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Pricing towards better decisions

41Adapted from the web page of The General Insurance Association of Japan (http://www.sonpo.or.jp/protection/insyu/)

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Activity-based Micro-Pricing system

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System architecture

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Characteristics as an ADSS

Characteristics

Semi-/non structured

Mobile

Economic incentives

Cognitive bias

Technologies

Context awareness

Mobile payment

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People subjectively frame economic transactions in their mind.• Loss-aversion decision making• Greater impact on trivial sums of money

45D. Kahneman and A. Tversky. Prospect theory: An analysis of decision under risk. Econometrica, 47(2):263–292, Mar 1979.

Kahneman’s Prospect theory

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UbiPayment and UbiRebate model

46

Small initial cost

Micro charge

cost

time

User

cost

time

Micro rebating

Large initial cost

User

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User study

1. Psychological test for asymmetric transaction effect evaluation

2. Simulation setup of the micro-pricing world

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User study #1

1. Payment: No initial cost (5 JPY payment per 2 seconds for using the tool)2. Rebate: 200 JPY as initial cost (5 JPY rebate per 2 seconds for NOT using the tool)

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Results

• 12 university students joined (male: 11, female:1, age: 22-25)

• 60 seconds examination for each round

• Participants used the tool longer time in the rebate case than the payment case.

• Rebates might strongly encourage the participants to use the tool, even though a bigger initial cost was withdrawn.

Averaged experimental result of the flash application test.

Payment Rebate

Transferred circles (num) 60.91 61.33

Time taken to use the tool (sec) 17.00 (28%) 38.33 (63%)

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User study #2

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User study #2

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Findings from the experiment

• Feedback design amplifies psychological impact.

• Frequent notification increases cognitive load and obtrusiveness.

• Automatic payment transaction elicits anxiety and reluctance.

• Need to affect attitude to achieve sustainable behavior change.

• Persuasive messages induce reasoning and higher elaboration.

• Hybrid incentive design with social incentives.

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Discussion

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Attention

Emotion

Motivation

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Irrational decision making

Negative behavior

Human error

Arousal

Attitude

Cognitive load

55

Emotion

Motivation

Attention

The sense of immersion enables to perceive more decision informationand elicit emotional response by the experience.

Stronger motivation directs more attention to the decision problem.

In anticipation of better emotional feeling, motivation is reinforced towards particular behavior.

552010年12月22日水曜日

p.

• [User] Simplified ambient media decreases cognitive load, but requires skill and knowledge for interpretation.

• [System] Automaticity beyond a user’s consciousness and control elicits negative emotional arousal.

• [Developer] ADSS developers could lack of expertise for satisfying a variety of user’s requirement.

• [Ethic] Decision control could ethically be a problem.

Other design issues

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Conclusion and future work

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p.

Conclusion

• As the main contribution of this work, we identified practical design guidelines for ADSS development, from the four case studies designed upon our system framework.

• Whereas decision support is an essential aspect for most AmI services, human decision making mechanism is often discussed apart from the system design.

• The ADSS framework will assist in service developers to design a system with directing polite attention to psychological issues.

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p.

Future work

• Next iteration in more practical application domains.

1. Automobile - highly dynamic - attention

2. Financial activities - highly uncertain - emotion

3. Social infrastructure - highly complicated - motivation

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p.60

Seam-full vs Seamless

Crowd vs Individual

Control vs Support

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p.

Selected publications• T. Yamabe, I. Kosunen, I. Ekman, LA. Liikkanen, K. Kuikkaniemi, and T. Nakajima, Biofeedback Training with EmoPoker:

Controlling Emotional Arousalfor Better Poker Play. Fun and Games Conference 2010 (Fun and Games’10, WIP paper)

• T. Yamabe, Y. Washio, S. Matsuzawa, T. Nakajima, Empowering End-users to Find Point-of-interests with a Public Display, In Proc. of the 2010 International Conference on Pervasive Services (ICPS’10, full paper)

• T. Yamabe, V. Lehdonvirta, H. Ito, H. Soma, H. Kimura, and T. Nakajima, Activity-Based Micro-Pricing: Realizing Sustainable Behavior Changes Through Economic Incentives. In Proc. of The Fifth International Conference on Persuasive Technology (Persuasive’10, full paper)

• T. Yamabe, V. Lehdonvirta, H. Ito, H. Soma, H. Kimura, and T. Nakajima, Applying Pervasive Technologies to Create Economic Incentives that Alter Consumer Behavior. In Proc. of The 11th International Conference on Ubiquitous Computing (UbiComp’09, full paper, acceptance rate: 12.35%)

• T. Yamabe and T. Nakajima, Possibilities and Limitations of Context Extraction in Mobile Devices: Experiments with a Multi-sensory Personal Device, International Journal of Multimedia and Ubiquitous Engineering. 2009. vol.4(4)

• T. Yamabe, K. Takahashi, and T. Nakajima, Towards Mobility Oriented Interaction Design: Experiments in Pedestrian Navigation on Mobile Devices, In Proc. of The Fifth Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous’08, full paper, acceptance rate: 17.14%)

• T. Yamabe, A. Takagi, and T. Nakajima. 2005. Citron: A Context Information Acquisition Framework for Personal Devices, In Proc. of the 11th IEEE international Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’05, full paper)

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Thank you!

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