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NUR Intelligent UI
NUR – Intelligent UI
2
User interface design - big picture
User Research• user needs and
requirements
step 0
Problem
Description• sources: marketing
research
Application
Domain
step 1
User Modeling• personas
• profiles
• mental models
System
modeling• scenarios
• storyboards
• HTA, CTT
step 2
UI Design• app. models (GOMS,
KLM, etc.)
• prototypes
• final design
• 3 parts
• visual design
• interaction design
• UX design
UI Testing• KLM
• CW
• EE
• user testing (UX)
step 3
Documentation• visual design
• interaction design
• UX design
step 4
Implementation
step 5
NUR – Intelligent UI
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Intelligent system
Reasoning
Learning
Adaptability
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4
Intelligent system - reasoning
Derivation of a new knowledge– not previously contained in the system
New knowledge is based on the existing knowledge
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Reasoning Example
r1 r2 r3
r5r4 r6r7
t1
t2
contains
connected
t3
g1
Legend
ROI: (r2 , connected, *)
ROI
r7
r4
r1 r2 r3
r6r5
ROI
ROI
ROI
t1
t2t3g1
ROI definition: (object1 , semantic_relation, object2)
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Reasoning Example
r1 r2 r3
r5r4 r6r7
d1
d2 d3
d4 d5d6
contains (explicit relation)
connected (implicit relation)
t2
t1 d7
t3
g1
Legend
ROI
ROI
ROI ROI
r7
r4
r1 r2 r3
r6r5
t1
t2t3g1
connected: R(X,Y) door(A), R(X,A), R(Y,A).rule template:
ROI: (r2 , connected, *)
connected: contains(r1,r4)?query:
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Reasoning Example
saferoom(X) room(X), safe(X).
safe(X) contains(X,g).
safe(X) not:dangerous(X).
dangerous(X) contains(X,t).
safeconn(X,Y) saferoom(X), saferoom(Y),
connected: contains(X,Y).
rule template:
r7
r4
r1 r2 r3
r6r5
t1
t2t3g1
ROI: (r3 , safeconn, r7)
safepath:safeconn(r3,r7)?
safepath:safeconn(r3,r7,P)?
query:
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Intelligent Interactive System
Systems that provide interactive support based on
embedded AI mechanisms
Provides interface to AI functionality and knowledge
representations
Examples
Cooperative problem-solving systems
Operation center of autonomous system
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Intelligent User Interfaces (IUI)
A bridge between user and intelligent interactive system
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Reasons for introduction of IUI
Make the communication more natural– natural language conversation
– talking head
Lower cognitive load– personalization of UI and the content to user needs
– efficient UI control and content presentation
Accessibility – adaptation of UI and content to user capabilities
Increase the flexibility– automatic UI generation based on the context (tasks, environment, user
model)
Allow autonomous work
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Intelligent versus Intuitive Interfaces
Intuitive - having immediate mental perception or
understanding (natural, no training)
Intelligent - capable of communicating and reasoning (user,
task, dialogue, information, media)
Interface - a device that bridges different systems, people,
ideas, etc. (interpretation and generation)
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Models used in IUI design
Environment model
Conversation model
Domain model
User model
Behavior model
Physical model
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user context
ontology
Example: Conversation system
physical
locationend-device
capabilities
network
status
conversation
history
environment
tasks
user
preferences
domain
conversation
context
conversation
state
application
knowledge
physical
location
forg
ett
ing
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user context
Example: Conversation system
physical
locationend-device
capabilities
network
status
environment
tasks
user
preferencesontology
NL
annotation
linguistic
ontology
conversation
history
conversation
context
domain
application
knowledge
domain
application
knowledge
conversation
state
conversation
context
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Example: Conversation system
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Example: Domain model enriched by NL
warehouse
store office
contains contains
adjacentTo
subject
predicate
object
transitive
symmetricstoreoffice
warehouse
Domain RangeProperty Type
Role
name
Auxiliary
VerbVerb
Natural language attributes of a property type
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How to discover Intelligence in
an interactive system
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Turing test
Test whether we discover that our partner is a computer
Behavioral view of intelligence
– A behavior capable of fooling a human interrogator
Acting humanly is sufficient to pass the test
– Is it necessary the AI system also thinks humanly?
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Agents and intelligence in user
interfaces
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Agent definition
Agent is a theoretical concept from AI
There is no single universal definition of an agent
Agent in Webster's New World Dictionary: A person or thing
that acts or is capable of acting or is empowered to act, for
another.
Agent
Environment
Action
output
Sensor
Input
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What is an intelligent agent?
Intelligent agent is a computer system located in certain
environment and is capable to react in a flexible way on
events in its environment
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Intelligent Agent Types
Intelligent
Agents
Human agents
(Travel agent)
Software
agents
Hardware
agents (robots)
Information
agents
Cooperation
agents
Transaction
agents
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Characteristics of SW agents
EnvironmentAgent
Source: W. Brenner, et al, Intelligent Software Agents
Cooperation
Coordination
Character
Autonomy
MobilityLearning
Proactivity
Reactivity
Goal-oriented
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Software Agents
One view:
Software processes that have non-trivial tasks delegated to
them which require independent action and a report on the
results.
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SW Agents: Autonomy
Agent’s activities are autonomous (no statements from the
user)
Properties of an agent: autonomy, mobility, ability to
communicate, ability to learn, …
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SW Agents: Intelligence (necessary attributes)
Reasoning: agent monitors environment and takes
decisions (based on changes in the environment)
Learning: agent’s behavior is improving (based on previous
experience)
Adaptability: agent is able of adaptation to changes in its
environment (robustness)
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SW Agents: Mobility
Agent mobility = “traveling” from one computer to another
one
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SW Agents: Cooperation with other agents
Cooperation between agents makes possible to solve the
problems much faster (usually the solution is better)
Language for cooperation description
This is NOT an essential attribute of SW Agent
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SW Agents: Emotions
It may be desirable to humanize the agents
Problem with so called “uncanny valley” effect
Fam
ilia
rity
Similarity0 %
100 %
100 %
Human
Toy robot
Moving corps
UNCANNY
VALLEY
Humanoid
robot
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Issues for Software Agents
Personification– Should agents be represented as a living or animated character?
– Does it improve adoption of software?
– Does it create inflated expectations?
– Is it just too annoying?
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Talking Head: Real-time generation
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Talking Head: Interpolation between expressions
Surprised Sad Worried
Actual Actual Interpolated
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Issues for Software Agents
Trust and Competence– How does user develop an informed level of trust?
– Can agent give self-assessment on likely outcome of task?
Delegation– How can user delegate tasks?
– How can user check on status of delegated tasks?
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Issues for Software Agents
Control– How does user set limits on the
agent’s activity?
– When does the agent get to
interrupt the user (mixed-
initiative dialog)?
Dealing with multiple
agents– How can the user manage
many agents?
– How can interactions between
agents be predicted?
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Issues for Software Agents: Multiple agents
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Automation and human control
Users can avoid:– Routine, tedious, and error prone tasks
Users can concentrate on:– Making critical decisions, coping with unexpected situations, and planning
future actions
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Agents in user interfaces
Agents learn– monitoring user’s behavior (and they use behavioral patterns)
– feedback from the user
– question to other agents
Example– e-mail filter
– purchase of a ticket
Thank you for attention