1
Introduction
1
Introduction
2
User-Adaptive Systems:
An Integrative Overview
Tutorial originally presented at
UM99 (June, 1999) and IJCAI99 (August, 1999)
Anthony Jameson
Department of Computer Science
Saarland University,Germany
[email protected]
http://www.cs.uni-sb.de/users/jameson
Overview
1. Introduction
2. Functions of Adaptation
3. Properties Modeled
4. Types of Input
5. Inference Techniques
6. Empirical Foundations
7. Concluding Remarks
2
3
Introduction
3
Contents
Introduction
Introduction
Overview
Contents
Previous Terminology
Proposal for an Integrative Term
Why Are User-Adaptive Systems Important?
Functions
Overview
Tailor Information Presentation
Recommend Products or Other Objects
Help U to Find Information
Support Learning
Give Help
Adapt an Interface
Take Over Routine Tasks
Support Collaboration
Other Functions
Properties
Overview
Personal Characteristics
General Interests and Preferences
Proficiencies
Noncognitive Abilities
Current Goal
Beliefs
Behavioral Regularities
Psychological States
Context of Interaction
Input Types
Overview
Self-Reports on Personal Characteristics
Self-Reports on Proficiencies and Interests
Evaluations of Specific Objects
Responses to Test Items
Naturally Occurring Actions
Low-Level Measures of Psychological States
Low-Level Measures of Context
Inference
Overview
Bayesian Networks
Machine Learning, Introduction
ML, Rule Learning
ML, Perceptron-Style Learning
ML, Probability and Instance-Based Learning
ML, Collaborative Filtering
ML, General Discussion
Logic
Stereotypes
Empirical Foundations
Overview
Results of Previous Research
Early Exploratory Studies
Knowledge Acquisition
Controlled Evaluations With Users
Experience With Real-World Use
Concluding Remarks
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For a collection of some of the early research, see Kobsa, A., & Wahlster, W.
(Eds.) (1989). User models in dialog systems. Berlin: Springer.
5
Introduction
5
Previous Terminology (1)
6
User modeling
• First research in the late 1970’s
• Research originally focused on natural language dialog systems
• Recently, considerable expansion of scope to include types of
adaptation listed below
See, e.g., the journal User Modeling and User-Adapted Interaction
• Emphasis on use of detailed, explicit models of the user (U) to
adapt the behavior of the system (S)
Student (or learner) modeling
• U uses an intelligent tutoring system or learning environment
• Typically very explicit and sophisticated models of U
Previous Terminology (2)
6
Learning about users, adaptive interfaces
• Terms sometimes used by machine-learning specialists
• Modeling of the user is typically less explicit
Personal learning assistants / apprentices
• Terms often used in the agents community
• Emphasis is on the role of the agent in supporting the individual user
Personalization
• The most popular term at the present time
• Used by many commercial firms, most of which have been founded
only recently
7
Introduction
8
7
Proposal for an Integrative Term
A user-adaptive system
• adapts its behavior to the individual user U
on the basis of nontrivial inferences from information about U
GENERALLY RELEVANT
PROPERTIES
UPWARD INFERENCE
INPUT
DOWNWARD INFERENCE
DECISIONRELEVANT PROPERTIES
General schema for processing in a user-adaptive system
Why Are User-Adaptive Systems Important?
Relevant technological developments
Recent technological developments have enabled the practical
deployment of user-adaptive systems:
• General increase in computing power
• New application domains where adaptivity is especially valuable
• New interaction techniques that facilitate adaptivity
• Advances in relevant AI techniques
Examples of the earliest widely used systems
• Recommendations provided by Amazon.com’s Web sites
• Help provided by Microsoft’s Office Assistant
Examples of companies that offer personalization
software
• NetPerceptions
• BroadVision
• ... (Go to Google and type "personalization")
8
9
Functions / Overview
10
Functions / Overview
Overview of Functions of Adaptation
9
1. Tailor information presentation
5. Give help
2. Recommend products or other
objects
6. Adapt an interface
3. Help U to find information
7. Take over routine tasks
8. Support collaboration
4. Support learning
9. Other functions
Questions About Functions of Adaptation (1)
Task
What is the task that U and/or S is performing?
Normal division of labor
In a nonadaptive system, which aspects of the task would be
performed by U and by S, respectively?
Division of labor with adaptation
How does adaptivity lead to a change in the division of labor − or in
other aspects of the performance of the task?
Adaptive decisions of S
What decisions does S typically make in adapting to U?
10
11
11
Functions / Overview
12
Questions About Functions of Adaptation (2)
Relevant properties of U
What are the main properties of U that S takes into account when
deciding how to adapt?
Potential benefits
What are the main potential benefits of this type of adaptation?
Limitations
What are the main difficulties or disadvantages?
Tailor Information Presentation
Figure 3 of Hirst, G., DiMarco, C., Hovy, E., & Parsons, K. (1997). Authoring
and generating health-education documents that are tailored to the needs of
the individual patient. In A. Jameson, C. Paris, & C. Tasso (Eds.), User
modeling: Proceedings of the Sixth International Conference, UM97
(pp. 107−118).Wien, New York: Springer Wien New York.
(http://www.cs.uni-sb.de/UM97/): the HealthDoc system
Tailoring Medical Information Presentation
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13
Hohl, H., Böcker, H., & Gunzenhauser, R. (1996). Hypadapter: An adaptive
hypertext system for exploratory learning and programming. User Modeling
and User-Adapted Interaction, 6, 131−156.
13
Functions / Tailor Information Presentation
Tailoring Educational Presentations (1)
Two different descriptions of the
same LISP function for a
beginner (left) and an expert
(right)
Tailoring Educational Presentations (2)
Figure 6 of Hohl et al. (1996)
14
The size and availability of the listed concepts depend on S’s
assessment of U’s knowledge
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Functions / Tailor Information Presentation
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Discussion (1)
15
Task
S presents information to U about some topic
Normal division of labor
S (generates and) presents the same information in the same form to
all users
Division of labor with adaptation
S may generate a different presentation for each user
Decisions of S
What information should S present?
In what form should S present it (e.g., with what media)?
Discussion (2)
16
Relevant properties of U
Interests
Knowledge
Influences U’s need to see particular information and her ability to
understand it
Presentation preferences
E.g., for media, for concrete vs. abstract forms of presentation
Potential benefits
Greater comprehensibility, relevance, and enjoyment for U
Limitations
Presentations become less consistent over occasions and different
users
Whether such inconsistency is a problem depends on the situation
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Functions / Recommend Products or Other Objects
18
Recommend Products or Other Objects
Adaptive Route Advisor
Figure 3 from Rogers, S., Fiechter, C., & Langley, P. (1999). An adaptive
interactive agent for route advice. Proceedings of the Third International
Conference on Autonomous Agents, Seattle, WA.
17
Discussion (1)
Task
Find objects suitable for use by U
Normal division of labor
S provides object descriptions
U searches through them, evaluating the objects
Division of labor with adaptation
U provides some indication of evaluation criteria
S recommends suitable objects
Decisions of S
What objects to recommend
18
P1: VTL/RKB
P2: PMR/TKL
Machine Learning
P3: PMR/TKL
KL459-02-Pazzani
19
QC: PMR/AGR
June 17, 1997
T1: PMR
17:23
Functions / Recommend Products or Other Objects
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Discussion (2)
19
Relevant properties of U
U’s evaluation criteria
Potential benefits
U saves time and effort searching
U covers a larger number of potential objects
Limitations
U’s evaluation criteria may be hard to assess adequately in the
limited time available
S can more easily be designed to offer biased presentations
Help U to Find Information
Figure 2 from Pazzani, M., & Billsus, D. (1997). Learning and revising user
profiles: The identification of interesting web sites. Machine Learning, 27,
313−331.
Recommendations of Syskill & Webert
Nonrecommended page
Recommended page
Pages U has
already rated
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Functions / Help U to Find Information
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Discussion (1)
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Task
Finding information relevant to U in a large collection of information
Normal division of labor
Browsing:
U navigates within a hypertext system
Query-based information retrieval (IR):
S retrieves items matching U’s query
U finds relevant items among those retrieved
Division of labor with adaptation
Browsing:
U navigates, but S gives advice about where to look
Query-based IR:
S’s choice and presentation of retrieved items take into account
information about U that goes beyond U’s query
Discussion (2)
Adaptive decisions of S
Browsing:
What links should S recommend?
Query-based IR:
What items should S present, in what order?
Relevant properties of U
U’s current information need, goals, and general interests
Potential benefits
Browsing:
Better decisions by U about what links to pursue
Query-based IR:
Higher recall and precision
Limitations
If S’s support is of poor quality, U may miss information that she
otherwise would have found
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Functions / Support Learning
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Support Learning
The APT Lisp Tutor Interface
Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modelling the
acquisition of procedural knowledge. User Modeling and User-Adapted
Interaction, 4, 253−278.; Corbett, A. T., & Bhatnagar, A. (1997). Student
modeling in the ACT programming tutor: Adjusting a procedural learning
model with declarative knowledge. In A. Jameson, C. Paris, & C. Tasso
(Eds.), User modeling: Proceedings of the Sixth International Conference,
UM97 (pp. 243−254). Wien, New York: Springer Wien New York.
(http://www.cs.uni-sb.de/UM97/)
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Discussion (1)
24
Task
S acquires knowledge and/or skills in some topic area
Normal division of labor
S provides information, exercises, tests, hints, and feedback
U processes this material and learns
Division of labor with adaptation
S performs basically the same subtasks but more strongly dependent
on properties of the individual U
Adaptive decisions of S
What information, exercises or tests should S present or recommend
next?
What hints are appropriate at this point?
How should U’s state of knowledge be described to U?
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Functions / Support Learning
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Discussion (2)
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Relevant properties of U
Prior knowledge and knowledge acquired during interaction
Learning style, motivation, viewpoints, ...
Current specific goals and beliefs (e.g., misconceptions)
Potential benefits
Faster, better, and/or more enjoyable learning by U
Limitations
Decision-relevant information about the specific U may be too difficult
to obtain
Decisions by S may not need to depend on such information
E.g., S can teach a correct method without knowing what incorrect
method U is currently employing
Give Help
http://www.research.microsoft.com/research/dfg/horvitz/lum.htm. Cf. Horvitz,
E., Breese, J., Heckerman, D., Hovel, D., & Rommelse, K. (1998). The
Lumière project: Bayesian user modeling for inferring the goals and needs of
software users. In G. F. Cooper & S. Moral (Eds.), Uncertainty in Artificial
Intelligence: Proceedings of the Fourteenth Conference (pp. 256−265).San
Francisco: Morgan Kaufmann. (http://www.sis.pitt.edu/~dsl/UAI.html)
Active Help From the Office Assistant
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Functions / Give Help
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Discussion (1)
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Task
U finds out how to use an application successfully and efficiently
Normal division of labor
U searches through documentation and engages in trial and error
Division of labor with adaptation
U does basically the same things, but S gives hints to expedite the
process
Adaptive decisions of S
What hints should S offer?
Should S intervene now at all?
Relevant properties of U
Knowledge of the application
Current goal
General desire to be helped by S
Discussion (2)
28
Potential benefits
U finds relevant functions and documentation more quickly and with
less frustration
U discovers aspects of the application that U would otherwise never
have known about
Limitations
Reasonably accurate assessment by S can be very difficult
The relevant properties are usually reflected only indirectly in U’s
behavior
U’s current goal can change quickly
Interventions by S that aren’t helpful can be distracting and
misleading
Dey, A. K., Abowd, G. D., & Wood, A. (1998). Cyberdesk: A framework for
providing self-integrating context-aware services. Knowledge-Based
Systems, 11, 3−13.(http://www.cc.gatech.edu/fce/cyberdesk)
Figure 4 of Dey, A. K., Abowd, G. D., & Wood, A. (1998). Cyberdesk: A
framework for providing self-integrating context-aware services.
Knowledge-Based Systems, 11, 3−13.
(http://www.cc.gatech.edu/fce/cyberdesk)
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Functions / Adapt an Interface
A Context-Aware Interface (2)
U’s current work context is in itself a "property" of U that should be
taken into account when adaptation decisions are made
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Adapt an Interface
A Context-Aware Interface (1)
CyberDesk actively offers U different applications and specific
functions, depending on U’s current work context
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Functions / Adapt an Interface
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Discussion (1)
31
Task
U works with one or more applications through a given interface
Normal division of labor
U learns to deal with the given interface, perhaps adapting it explicitly
− if it is an adaptable interface (to be distinguished from an
adaptive one)
Division of labor with adaptation
S modifies the interface to make it more suitable for U, perhaps after
consulting U for approval
Adaptive decisions of S
How should input methods be adjusted?
E.g., keyboard properties
What functions should be made available or highlighted?
How should aspects of the display be adjusted?
E.g., colors, fonts
Discussion (2)
Relevant properties of U
Noncognitive skills (e.g., motor or visual disabilities)
Tendency or ability to use particular functions
Potential benefits
Saving of time, effort, errors, and frustration in using the interface
Avoidance of need to specify interface adaptations explicitly
U may not know what an appropriate specification would be
E.g., optimal delay before the keyboard starts repeating
Specifications might have to be made in each individual case, as
opposed to just once and for all
E.g., suitable choice of fonts and colors for each individual
graph
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Functions / Adapt an Interface
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Discussion (3)
33
Limitations
Adaptation of interface elements can decrease the consistency and
predictability of the interface
These properties are prerequisites for rapid automatic processing by
a skilled user
Take Over Routine Tasks
Mitchell, T., Caruana, R., Freitag, D., McDermott, J., & Zabowski, D. (1994).
Experience with a learning personal assistant. Communications of the ACM,
37(7), 81−91.
Advice from the Calendar Apprentice
Situation
• U is adding a planned meeting to her calendar
• U has already specified the meeting type, the attendees, and the
date
• U is about to specify the duration
Adaptation
• S proposes a duration of 60 minutes on the basis of previous
observations of U’s scheduling behavior
• U can accept or reject S’s recommendation
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Functions / Take Over Routine Tasks
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Discussion (1)
Task
U performs some regularly recurring task that requires many specific
decisions and/or operations
The task may involve devices other than traditional computers
(e.g., cars, home heating systems)
Normal division of labor
U makes the decisions and performs the operations individually
Division of labor with adaptation
Strong form:
S makes the decisions and performs the operations on U’s behalf
Weak form:
S recommends specific decisions and offers to perform particular
operations
Adaptive decisions of S
What action would U want to perform in this case?
Would U want me to perform this action without consultation?
Discussion (2)
36
Relevant properties of U
Preferences, interests, goals, etc.
Note: Most systems in this category don’t represent such properties
explicitly; instead, they learn behavioral regularities of U
Potential benefits
U saves time and effort
Limitations
Having S perform actions without consulting U can have serious
negative consequences in individual cases
Unlike a human assistant, S may not be able to recognize
exceptional cases that require U’s attention
When S does consult U, the savings of time and effort for U may be
limited
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Functions / Support Collaboration
38
Support Collaboration
Identification of Suitable Collaborators
Figure 2 of Collins, J. A., Greer, J. E., Kumar, V. S., McCalla, G. I., Meagher,
P., & Tkatch, R. (1997). Inspectable user models for just-in-time workplace
training. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling:
Proceedings of the Sixth International Conference, UM97 (pp. 327−337).
Wien, New York: Springer Wien New York. (http://www.cs.uni-sb.de/UM97/)
37
Discussion (1)
38
Task
U identifies persons who can work with U on a given task
Normal division of labor
U considers potential collaborators and assesses their suitability
Division of labor with adaptation
S compares a characterization of U with characterizations of potential
collaborators and suggests good matches
Adaptive decisions of S
Who would be a good collaborator?
Relevant properties of U
Knowledge, interests, ...
Willingness to collaborate, availability, ...
39
Functions / Support Collaboration
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Discussion (2)
39
Potential benefits
U can save time and find collaborators that couldn’t be identified
otherwise
Limitations
Because of the greater accessibility of models of users, threats to
privacy may be greater
Especially valuable collaborators may be requested too often
Other Functions
Figure 1 of Noh, S., & Gmytrasiewicz, P. J. (1997). Agent modeling in antiair
defense: A case study. In A. Jameson, C. Paris, & C. Tasso (Eds.), User
modeling: Proceedings of the Sixth International Conference, UM97
(pp. 389−400).Wien, New York: Springer Wien New York.
(http://www.cs.uni-sb.de/UM97/)
Agent Modeling for Coordination
C
A
Missile
B
E
D
Interceptor
F
Defense
Unit
1
2
1
2
When deciding which incoming missiles to try to intercept, Agent 1
should anticipate the corresponding decisions of Agent 2; and
vice-versa
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Properties / Overview
42
Properties / Overview
41
Overview of Properties Modeled
1. Personal characteristics
2. General interests and preferences
3. Proficiencies
4. Noncognitive abilities
5. Current goal
6. Beliefs
7. Behavioral regularities
8. Psychological states
9. Context of interaction
Typical Relationships Among Properties
Personal
Characteristic 1
Personal
Characteristic 2
Knowledgeability About
Topic
General
Interest
Long-Term
Goal
Specific Belief
Specific
Evaluation
Short-Term
Goal
User Action 1
"A −> B" = "A influences B"
User Action 2
Noncognitive
Ability
User Action 3
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Properties / Overview
44
Questions About Properties
43
Breadth of implications
Is the property P relevant to a variety of decisions of S or only to a
single type of decision?
Directness of decision-relevance
Can S take P into account directly when making a decision, or does S
first have to make (uncertain) inferences on the basis of P?
Ease of assessment
How difficult is it in general to arrive at a reliable assessment of P?
Personal Characteristics
Figure 3 from Hirst, G., DiMarco, C., Hovy, E., & Parsons, K. (1997).
Authoring and generating health-education documents that are tailored to
the needs of the individual patient. In A. Jameson, C. Paris, & C. Tasso
(Eds.), User modeling: Proceedings of the Sixth International Conference,
UM97 (pp. 107−118).Wien, New York: Springer Wien New York.
(http://www.cs.uni-sb.de/UM97/)
Personal Characteristics and Text Generation 44
References to
personal
characteristics
45
Properties / Personal Characteristics
46
Discussion
45
Breadth of implications
Often quite high (e.g., for age, gender)
Directness of decision-relevance
Personal characteristics sometimes have direct, important
consequences
E.g., what type of clothes a customer might want to buy
But inferences about properties like preferences and knowledge are
often unreliable
Ease of assessment
The necessary information is often already available or easily
supplied by U
Inferring personal characteristics on the basis of indirect evidence is
in general difficult
General Interests and Preferences
Figure 3 from Rogers, S., Fiechter, C., & Langley, P. (1999). An adaptive
interactive agent for route advice. Proceedings of the Third International
Conference on Autonomous Agents, Seattle, WA.
Modeling of Evaluation Criteria
In the Adaptive Route Advisor, the preferences of each user U are
represented as a vector of importance weights; this vector is used to
predict how U would evaluate any given route if she knew all of its
attributes
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Properties / General Interests and Preferences
48
Discussion
47
Breadth of implications
Generally high
Directness of decision-relevance
Generally moderate
Ease of assessment
Explicit self-reports can be fairly useful (see below)
Indirect inference is typically moderately difficult
Proficiencies
Figure 1 of Hohl, H., Böcker, H., & Gunzenhauser, R. (1996). Hypadapter:
An adaptive hypertext system for exploratory learning and programming.
User Modeling and User-Adapted Interaction, 6, 131−156.
Proficiencies in Hypadapter
Specific
proficiencies
General
proficiency
48
49
Properties / Proficiencies
50
Discussion
49
Breadth of implications
The more general the proficiency, the broader the implications
Directness of decision-relevance
The more general the proficiency, the less direct the implications
Ease of assessment
Self-reports can be of some use (see below)
The more general the proficiency, the greater the amount of available
indirect evidence
But this evidence may not be representative of the overall
proficiency
E.g., S wants to assess U’s knowledge of Linux, but the
available evidence concerns mainly the use of LaTeX and
Ghostview
Noncognitive Abilities
Figure 1 from Morales, R., & Pain, H. (1999). Modelling of novices’ control
skills with machine learning. In J. Kay (Ed.), UM99, User modeling:
Proceedings of the Seventh International Conference (pp. 159−168).Wien,
New York: Springer Wien New York. (http://www.cs.usask.ca/UM99/)
Modeling Pole-Balancing Skills
a
0
x
F
Morales and Pain use machine learning techniques to
model a learner’s ability to balance a pole on a cart
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Properties / Noncognitive Abilities
52
Discussion
51
Breadth of implications
Ranges from very high (e.g., generally poor eyesight) to very low
(e.g., specific motoric impairment)
Directness of decision-relevance
Typically quite high
Ease of assessment
Self-reports are in some cases possible, but often U wouldn’t know
how to characterize a noncognitive ability
Reliable objective tests are often available
Current Goal
Figure 1 from Heckerman, D., & Horvitz, E. (1998). Inferring informational
goals from free-text queries: A Bayesian approach. In G. F. Cooper & S.
Moral (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the
Fourteenth Conference (pp. 230−237).San Francisco: Morgan Kaufmann.
(http://www.sis.pitt.edu/~dsl/UAI.html)
Goal Identification With the Answer Wizard
52
p(Goal 1 | Query)
Query
p(Goal n | Query)
53
Properties / Current Goal
54
Discussion
53
Breadth of implications
Relevant only to decisions that S makes in the current situation
Directness of decision-relevance
Generally high
Ease of assessment
Explicit specification by U is usually too inconvenient
Indirect inference is typically difficult
Observable actions may be compatible with various goals
U may be pursuing more than one goal at a time
Beliefs
Figure 8 from Kobsa, A., & Pohl, W. (1995). The user modeling shell system
BGP-MS. User Modeling and User Adapted Interaction, 4, 59−106.
Representation of Beliefs in BGP-MS
54
55
56
Higher-Order Beliefs and Coordination
55
Figure 3 of Noh, S., & Gmytrasiewicz, P. J. (1997). Agent modeling in antiair
defense: A case study. In A. Jameson, C. Paris, & C. Tasso (Eds.), User
modeling: Proceedings of the Sixth International Conference, UM97
(pp. 389−400). Wien, New York: Springer Wien New York.
(http://www.cs.uni-sb.de/UM97/) Used with permission.
Properties / Beliefs
A
B
C
Battery1
D
E
F
Level 1:
A
26.8
47.4
38.6
46.7
45.4
50.0
B
46.0
28.7
40.0
48.1
46.7
51.3
Belief: 0.75
A
B
C
Level 2: Battery2
D
E
F
A
26.8
46.0
41.4
56.2
56.1
62.3
B
47.4
28.7
44.2
58.9
58.8
65.1
Battery1
C D
38.6 46.7
40.0 48.1
19.3 43.5
50.2 33.3
50.1 58.2
56.3 64.4
E
45.4
46.7
42.1
56.9
34.1
63.1
Battery2
C D
41.4 56.2
44.2 58.9
19.3 50.2
43.5 33.3
42.1 56.9
46.7 61.5
E
56.1
58.8
50.1
58.2
34.1
61.4
F
62.3
65.1
56.3
64.4
63.1
42.6
0.14
F
50.0
51.3
46.7
61.5
61.4
42.6
A
A 26.8
B 46.0
C 41.4
B
47.4
28.7
44.2
Battery1
C D
E F
38.6 46.7 45.4 50.0
40.0 48.1 46.7 51.3
19.3 43.5 42.1 46.7
0.11
No-info 1
[1/6, 1/6, 1/6, 1/6, 1/6, 1/6]
No-info 1
[1,0,0,0,0,0] . . . . . [0,0,0,0,0,1]
No-info 1
[1,0,0,0,0,0] . . . . . [0,0,0,0,0,1]
In the RMM approach, higher-order expectations about beliefs
and values are represented in terms of a hierarchy of payoff
matrices
Discussion
56
Breadth of implications
High or low, depending on the generality of the belief
Directness of decision-relevance
Can be high even for a general belief, if it has specific implications in
the current situation
E.g., "Ink jet printers can’t handle PostScript files"
Ease of assessment
Explicit self-reports would in general be unnatural and
time-consuming
Indirect assessment is often tricky
Observable actions seldom unambiguously reflect particular
beliefs
Particular beliefs are in general not easy to predict on the basis of
general proficiencies and personal characteristics
57
Mitchell, T., Caruana, R., Freitag, D., McDermott, J., & Zabowski, D. (1994).
Experience with a learning personal assistant. Communications of the ACM,
37(7), 81−91.
57
Properties / Behavioral Regularities
58
Behavioral Regularities
Rules Representing U’s Scheduling Habits
Example of a rule learned by the Calendar Apprentice
If
• Position-of-attendees is Grad-Student, and
• Single-attendee? is Yes, and
• Sponsor-of-attendees is Mitchell
Then
Duration is 60 minutes
Comments
There is no reference to the reasons why such a rule may be
applicable (or why it may be inapplicable in some cases), e.g.:
• the sort of topic that is typically discussed with graduate students
• Mitchell’s attitude toward graduate students
• ...
Excerpt from Table 1 of Linton, F., Joy, D., & Schaefer, H. (1999). Building
user and expert models by long-term observation of application usage. In J.
Kay (Ed.), UM99, User modeling: Proceedings of the Seventh International
Conference (pp. 129−138).Wien, New York: Springer Wien New York.
(http://www.cs.usask.ca/UM99/)
Profiles of Users’ Command Usage
Rank
1
2
3
4
5
6
7
...
18
19
20
Menu
Command
File
Edit
File
File
Edit
Edit
Format
Open
Paste
Save
DocClose
Clear
Copy
Bold
Tools
File
Spelling
View
PrintPreview
Header
Frequency
(%)
13.68
12.50
11.03
10.25
9.50
7.86
4.22
Cumulative
Frequency (%)
13.68
26.18
37.22
47.47
56.97
64.83
69.05
0.75
0.74
88.19
88.94
0.68
89.62
A simple frequency profile of U’s command usage can yield useful
information if it is compared with the profiles of more expert users
58
59
Properties / Behavioral Regularities
60
Discussion
59
Breadth of implications
Low: The implications typically concern the particular action that U
would perform in a given situation
Directness of decision-relevance
High, for the same reason
Ease of assessment
The inference methods are typically straightforward and reliable, but
they require a large amount of behavioral data
Psychological States
Affective-Computing homepage: http://www.media.mit.edu/affect/
Transitions Among Emotional States
Within the Affective Computing paradigm, a variety of
methods are used to recognize and predict a user’s
emotional state
60
61
Properties / Psychological States
62
Discussion
61
Breadth of implications
Relatively low, since the states are typically of limited duration
Directness of decision-relevance
Seldom very high
Even if S knows that U is frustrated, distracted, or rushed, it is
seldom obvious how S should adapt its behavior accordingly
Ease of assessment
Can be fairly high if suitable sensors are used (see below)
More tricky if behavioral symptoms must be interpreted
Context of Interaction
Figure 5 of Dey, A. K., Abowd, G. D., & Wood, A. (1998). Cyberdesk: A
framework for providing self-integrating context-aware services.
Knowledge-Based Systems, 11, 3−13.
(http://www.cc.gatech.edu/fce/cyberdesk)
Sensitivity to Other Applications Being Used 62
U’s current work context is in itself a "property" of U that should be
taken into account when adaptation decisions are made
63
Properties / Context of Interaction
64
Discussion
63
Breadth of implications
Implications are largely limited to the current situation
Directness of decision-relevance
Implications are often quite straightforward
Ease of assessment
Some aspects of context are more or less directly accessible to S,
since they concern the state of the computing device
If appropriate sensors are available (see below), other aspects can
be assessed straightforwardly
In other cases, difficult indirect inferences may be required
Input Types / Overview
Overview of Input Types
Explicit self-reports and -assessments
1. Self-reports on personal characteristics
2. Self-reports on proficiencies and interests
3. Evaluations of specific objects
Nonexplicit input
4. Responses to test items
5. Naturally occurring actions
6. Low-level measures of psychological states
7. Low-level measures of context
64
65
Input Types / Overview
66
Questions About Input Types
65
Frequency
How often is the input of the type I typically acquired?
Additional activity
Does I require extra activity by U that would not be required if I were
not needed?
Cognitive effort
How much thinking and memory retrieval does I require from U?
Motor effort
How much physical input activity (e.g., typing) does I require?
Reliability
To what extent does I allow S to derive precise, accurate
assessments of properties of U?
Self-Reports on Personal Characteristics
PC Self-Reports: Discussion
Frequency
Typically only at one or two
points in a dialog
Additional activity
Usually, there’s extra activity
But in some cases U might
have to supply much of the
same information anyway −
e.g., to place an order
Cognitive effort
Generally little
Motor effort
Often a lot − unless easy
copying from other information
sources is supported
66
Reliability
On the whole high
But there may be distortions if
sensitive issues are involved, or
if seldom-used information has
to be recalled from memory
67
Input Types / Self-Reports on Proficiencies and Interests
68
Self-Reports on Proficiencies and Interests
Examples from Hypadapter
Figure 1 from Hohl, H., Böcker, H., & Gunzenhauser, R. (1996). Hypadapter:
An adaptive hypertext system for exploratory learning and programming.
User Modeling and User-Adapted Interaction, 6, 131−156.
67
Discussion
Frequency
Typically only at one or two
points in a dialog
Additional activity
There’s almost always extra
activity
Cognitive effort
High, if U tries seriously to give
a reliable assessment (see
below)
Motor effort
Little
68
Reliability
Generally low:
U is unlikely to know the
exact meanings of the
reference points given
U may be motivated to give
an inaccurate
self-assessment
69
Input Types / Evaluations of Specific Objects
70
Evaluations of Specific Objects
Feedback to Syskill & Webert
Figure 1 from Pazzani, M., & Billsus, D. (1997). Learning and revising user
profiles: The identification of interesting web sites. Machine Learning, 27,
313−331.
69
U can click on the thumbs to indicate interest or disinterest in the page
being shown
Discussion
Frequency
Typically, many evaluations are
given
Additional activity
Usually, there’s some extra
activity but it’s not much of a
burden (see below)
Cognitive effort
Low; usually a spontaneous
reaction is expressed
Motor effort
Can be reduced to a single
mouse click
Reliability
Fairly high
The same problems as for
general interests may arise
to some extent, but the
concreteness of the
judgment makes it more
reliable
70
71
Input Types / Responses to Test Items
72
Responses to Test Items
http://www.c-lab.de/~gutkauf/um97.html; Gutkauf, B., Thies, S., & Domik, G.
(1997). A user-adaptive chart editing system based on user modeling and
critiquing. In A. Jameson, C. Paris, & C. Tasso (Eds.), User modeling:
Proceedings of the Sixth International Conference, UM97 (pp. 159−170).
Wien, New York: Springer Wien New York. (http://www.cs.uni-sb.de/UM97/)
71
Example: Color Discrimination Test
In the graph critiquing system of Gutkauf et al., users can take a color
discrimination test
S can then take any limitations into account when advising about graph
design
Discussion
Frequency
Tests can be administered at a
single point, or continually (e.g.,
in educational systems)
Additional activity
Usually there’s extra activity
If the test items also serve as
practice items, they may be
worth dealing with in any case
Cognitive and motor effort
Depends on the nature of the
test
Reliability
Generally high:
Test items can be selected,
administered, and
interpreted through
well-understood, reliable
procedures
72
73
Input Types / Naturally Occurring Actions
74
Naturally Occurring Actions
Discussion
73
Frequency
There may be a great number
of relevant natural actions − or
only a few if a very specific type
is involved
Additional activity
Reliability
Naturally occurring actions can
be reliable indicators of specific
beliefs and attitudes, but less
so of generally relevant
properties of U
Usually there’s no extra activity
Sometimes, ostensibly natural
actions are invoked by
disguised tests
Cognitive and motor effort
Can vary greatly; but not very
important, since the actions will
tend to be performed anyway
Low-Level Measures of Psychological States
Affective-Computing homepage: http://www.media.mit.edu/affect/
Example: Jewelry for Affective Computing
74
75 Input Types / Low-Level Measures of Psychological States 76
Discussion
75
Reliability
Frequency
Where the necessary sensors
are available, a large amount of
input data can be acquired
Additional activity
Since most people wouldn’t
wear such sensors otherwise,
any associated effort is usually
extra effort
Fairly high for some
psychological states that are
closely associated with
measurable symptoms
Cognitive effort
Typically none, since U doesn’t
have to give any explicit input
Motor effort
Sensors can be cumbersome,
but technological advances are
continually reducing the burden
Low-Level Measures of Context
Use of Position Information
CyberDesk can take into account a mobile user’s position, as sensed
via GPS
76
77
Input Types / Low-Level Measures of Context
78
Discussion
77
Reliability
Frequency
The amount of data that can be
acquired is limited only by the
technological possibilities and
the amount of available
information that is worth
collecting
Depends on the degree of
precision required and on the
available technology
Additional activity; cognitive
and motor effort
Some information about context
may be required from U, but
technological advances make it
increasingly possible to acquire
it without effort from U
Inference / Overview
Overview of Main Inference Techniques
1. Decision-theoretic methods
2. Machine learning
• Rule learning
• Perceptron-style learning
• Learning of probabilities
• Instance-based learning
3. Logic
4. Stereotypes
78
79
79
Inference / Overview
80
Questions About Inference Techniques
Knowledge acquisition effort
How much effort is required to provide S with the necessary
background knowledge about users in general?
Computational complexity
How much time (and other computational resources) does T require?
Amount of input data required
How much input data does T require in order to be able to make
useful inferences?
Handling of noise and uncertainty
How adequately can T deal with the uncertainty that is inherent in
most inferences about a user?
Validity and justifiability
To what extent can the inferences made through T be counted on to
be accurate?
Bayesian Networks
Introduction (1)
80
Basic concepts
Each chance node in a Bayesian network (BN) corresponds to a
variable
In user-adaptive systems, most variables refer to properties of U
These may be observable or unobservable
S’s belief about each variable is represented as a probability
distribution over the possible values of the variable
Links between nodes typically (though not always) represent causal
influences
When the value of a given variable is observed, the corresponding
node is instantiated
The evaluation of the network then leads to revised beliefs about the
other variables
81
82
Introduction (2)
81
Horvitz, E., & Barry, M. (1995). Display of information for time-critical
decision making. In P. Besnard & S. Hanks (Eds.), Uncertainty in Artificial
Intelligence: Proceedings of the Eleventh Conference (pp. 296−305).San
Francisco: Morgan Kaufmann.
Inference / Bayesian Networks
Extension to Influence Diagrams
An influence diagram contains (in addition to chance nodes) decision
nodes and value nodes
Through the evaluation of an influence diagram, S can determine
which decision is likely to yield the highest value
Influence diagrams have so far seldom been employed in
user-adaptive systems (but see. e.g., Horvitz & Barry, 1995; Jameson
et al., 2000, cited below).
Introduction (3)
Learning of Bayesian networks from data
In contrast to the machine learning techniques discussed below, the
learning of Bayesian networks has so far been restricted almost
exclusively to networks that apply to users in general
Therefore, the learning does not in itself involve adaptation to a
particular user; it only sets the stage for such adaptation
Examples
Albrecht, D. W., Zukerman, I., & Nicholson, A. E. (1998). Bayesian
models for keyhole plan recognition in an adventure game. User
Modeling and User-Adapted Interaction, 8, 5−47.
Lau, T., & Horvitz, E. (1999). Patterns of search: Analyzing and
modeling Web query dynamics. In J. Kay (Ed.), UM99, User
modeling: Proceedings of the Seventh International Conference
(pp. 119−128).Wien, New York: Springer Wien New York.
(http://www.cs.usask.ca/UM99/)
82
83
Inference / Bayesian Networks
84
Introduction (4)
83
References
Overview of applications in user-adaptive systems
Jameson, A. (1996). Numerical uncertainty management in user and
student modeling: An overview of systems and issues. User Modeling
and User-Adapted Interaction, 5, 193−251.
General books
Pearl, J. (1988). Probabilistic reasoning in intelligent systems:
Networks of plausible inference. San Mateo, CA: Morgan Kaufmann.
Jensen, F. V. (1996). An introduction to Bayesian networks. New
York: Springer.
Castillo, E., Gutierrez, J. M., & Hadi, A. S. (1997). Expert systems
and probabilistic network models. Berlin: Springer.
Figure 7 of Heckerman, D., & Horvitz, E. (1998). Inferring informational goals
from free-text queries: A Bayesian approach. In G. F. Cooper & S. Moral
(Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth
Conference (pp. 230−237). San Francisco: Morgan Kaufmann.
(http://www.sis.pitt.edu/~dsl/UAI.html) Figures from this article reproduced
with permission.
Answer Wizard (1)
84
85
Inference / Bayesian Networks
Answer Wizard (2)
85
Figure 3 of Heckerman, D., & Horvitz, E. (1998). Inferring informational goals
from free-text queries: A Bayesian approach. In G. F. Cooper & S. Moral
(Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth
Conference (pp. 230−237).San Francisco: Morgan Kaufmann.
(http://www.sis.pitt.edu/~dsl/UAI.html)
86
p(Goal 1 | Query)
Query
p(Goal n | Query)
Overall goal of the Answer Wizard
Goal
Term 1
Term 2
Term 3
Term m
Simplified conception of relationships between
U’s current goal and terms in U’s query
Inference in the Answer Wizard
U’S CURRENT GOAL
BAYESIAN UPDATING
WORDS IN U’S QUERY
---
---
86
Figure 1 of Horvitz, E., Breese, J., Heckerman, D., Hovel, D., & Rommelse,
K. (1998). The Lumière project: Bayesian user modeling for inferring the
goals and needs of software users. In G. F. Cooper & S. Moral (Eds.),
Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth
Conference (pp. 256−265). San Francisco: Morgan Kaufmann.
(http://www.sis.pitt.edu/~dsl/UAI.html)
Figure 6 of Horvitz, E., Breese, J., Heckerman, D., Hovel, D., & Rommelse,
K. (1998). The Lumière project: Bayesian user modeling for inferring the
goals and needs of software users. In G. F. Cooper & S. Moral (Eds.),
Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth
Conference (pp. 256−265). San Francisco: Morgan Kaufmann.
(http://www.sis.pitt.edu/~dsl/UAI.html) Figures from this article reproduced
with permission.
87
Inference / Bayesian Networks
87
• Actions (t)
• Query
Assistance
history
User’s
background
Competency
profile
User's
acute needs
Automated
assistance
Utility
Explicit
query
88
Overview of Lumière
Event synthesis
Events
Inference
Words
Control system
Influence Diagram from Lumière
Task
history
Context
User's
goals
Documents &
data structures
Cost of
assistance
User’s
actions
88
89
Figure 2 of Horvitz, E., Breese, J., Heckerman, D., Hovel, D., & Rommelse,
K. (1998). The Lumière project: Bayesian user modeling for inferring the
goals and needs of software users. In G. F. Cooper & S. Moral (Eds.),
Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth
Conference (pp. 256−265). San Francisco: Morgan Kaufmann.
(http://www.sis.pitt.edu/~dsl/UAI.html)
89
Inference / Bayesian Networks
90
Bayesian Network from Lumière
Recent
menu surfing
Difficulty of
current task
User expertise
User needs
assistance
Pause after
activity
User distracted
Inference in Lumière
U’S CURRENT GOAL, OTHER
CURRENT STATES, AND
LONGER-TERM PROPERTIES
PROPAGATION IN BAYESIAN
NETWORK
U’S ACTIONS
BAYESIAN NET
PROPAGATION /
INFLUENCE DIAGRAM
EVALUATION
U’S DESIRE FOR HELP /
UTILITY OF OFFERING U
HELP
90
91
For more detailed discussion of these issues, see Jameson, A. (1996).
Numerical uncertainty management in user and student modeling: An
overview of systems and issues. User Modeling and User-Adapted
Interaction, 5, 193−251.
91
Inference / Bayesian Networks
92
Discussion (1)
Knowledge acquisition
Arriving at a suitable network structure and accurate probabilities can
require a substantial knowledge-acquisition effort
Recently, researchers in this area have begun applying general
techniques for the learning of Bayesian networks from data
Computational complexity
In general, evaluation of Bayesian networks is NP-hard
In practice, some designers of user-adaptive systems have
experienced considerable difficulty with these complexity problems,
investing a lot of effort in finding ways to overcome them
But in more favorable cases, the complexity does not pose a
significant problem
Discussion (2)
92
Amount of input data required
If the networks are learned from data − usually off-line − a large
amount of data is required
Inferences about a specific user can be made on the basis of a single
piece of unreliable evidence
Handling of noise and uncertainty
Bayesian networks are especially good at appropriately interpreting
individual pieces of evidence that permit only uncertain inference
Note that even an uncertain belief about a user may uniquely
determine an appropriate system action
93
Inference / Bayesian Networks
94
Discussion (3)
93
Validity and justifiability
If the qualitative and quantitative aspects of the networks are correct,
the inferences made will be valid (though typically they will yield
explicitly uncertain conclusions)
Shortcuts taken to simplify knowledge acquisition or to deal with
complexity problems may impair the validity of inferences in
unpredictable ways
Machine Learning, Introduction
Introduction (1)
Basic concepts
A machine learning (ML) system comprises a learning component
and a performance component
In the context of user-adaptive systems,
• the learning component, on the basis of data about a particular
user U (and/or about other users) infers the knowledge that is
required by the performance component;
• the performance component is typically used to make
decision-relevant predictions about a particular user U
The following subsections illustrate the ML methods that have been
used most in user-adaptive systems
94
95
95
Inference / Machine Learning, Introduction
96
Introduction (2)
General References
Overviews of use for user-adaptive systems
Langley, P. (1999). User modeling in adaptive interfaces. In J. Kay
(Ed.), UM99, User modeling: Proceedings of the Seventh
International Conference. Wien, New York: Springer Wien New York.
(http://www.cs.usask.ca/UM99/)
Sison, R., & Shimura, M. (1998). Student modeling and machine
learning. International Journal of Artificial Intelligence in Education, 9,
128−158.
Machine learning texts
Langley, P. (1996). Elements of machine learning. San Francisco:
Morgan Kaufmann.
Mitchell, T. M. (1997). Machine learning. Boston: McGraw-Hill.
ML, Rule Learning
"CAP" = "Calendar APprentice". Cf. Mitchell, T., Caruana, R., Freitag, D.,
McDermott, J., & Zabowski, D. (1994). Experience with a learning personal
assistant. Communications of the ACM, 37(7), 81−91.
Inference in CAP
96
RULES FOR PREDICTING U’S
DECISIONS
DECISION TREE LEARNING
OBSERVED SCHEDULING
DECISIONS OF U
RULE APPLICATION
PREDICTED DECISIONS OF
U
97
Inference / ML, Rule Learning
98
Learned Rules
97
Example of a learned rule:
If
• Position-of-attendees is Grad-Student, and
• Single-attendee? is Yes, and
Position
• Sponsor-of-attendees is Mitchell
Student
Programmer Faculty
Then
Lunchtime?
Mitchell et al. (1994)
Duration is 60
Applicability so far
Training data: 6/11
Yes
DiningHall
Department
W5309
S
No
W5309
EE
W5309
H301
Example of a learned decision tree
Test data: 51/86
ML, Perceptron-Style Learning
Cf. Rogers, S., Fiechter, C., & Langley, P. (1999). An adaptive interactive
agent for route advice. Proceedings of the Third International Conference on
Autonomous Agents, Seattle, WA.
Inference in the Adaptive Route Advisor
U’S IMPORTANCE WEIGHTS FOR 5
ATTRIBUTES OF ROUTES
PERCEPTRON-STYLE
LEARNING
U’S EXPRESSED
PREFERENCES FOR
SPECIFIC ROUTES
GRAPH SEARCH
PRESUMABLY DESIRABLE
ROUTES FOR U
98
99
Inference / ML, Probability and Instance-Based Learning 100
ML, Probability and Instance-Based Learning
NewsDude Interface
Billsus, D., & Pazzani, M. J. (1999). A hybrid user model for news story
classification. In J. Kay (Ed.), UM99, User modeling: Proceedings of the
Seventh International Conference (pp. 99−108). Wien, New York: Springer
Wien New York. (http://www.cs.usask.ca/UM99/)
99
Inference in NewsDude: Long-Term
CONDITIONAL PROBABILITIES
FOR WORDS
LEARNING OF CONDITIONAL
PROBABILITIES
RATINGS OF HEARD
STORIES
NAIVE BAYESIAN
CLASSIFICATION
INTEREST IN INDIVIDUAL
STORIES
100
101 Inference / ML, Probability and Instance-Based Learning 102
Modeling Long-Term Interests
101
STORY
INTERESTING?
WORD 1?
WORD 2?
...
WORD 3?
WORD 200?
Naive Bayesian classification in NewsDude;cf. Syskill & Webert
(Pazzani & Billsus, 1997);Answer Wizard (Heckerman & Horvitz, 1998)
Inference in NewsDude: Short-Term
PREVIOUSLY HEARD STORIES
AND THEIR RATINGS
STORAGE OF STORIES IN
VECTOR SPACE
RATINGS OF HEARD
STORIES
NEAREST-NEIGHBOR
PREDICTION
INTEREST IN INDIVIDUAL
STORIES
102
103 Inference / ML, Probability and Instance-Based Learning 104
103
Use of Nearest Neighbor Algorithm
+ +
+
-
-
+
Voting stories:
+
+
? New story
ML, Collaborative Filtering
Inference in Collaborative Filtering Systems 104
SIMILARITY TO PARTICULAR
OTHER USERS
COMPUTATION OF
CORRELATIONS WITH
OTHER USERS’ RATINGS
U’S RATINGS OF OBJECTS
WEIGHTED AVERAGING OF
RATINGS OF OTHERS
PREDICTED RATINGS OF U
105
Billsus, D., & Pazzani, M. (1998). Learning collaborative information filters. In
J. Shavlik (Ed.), Machine learning: Proceedings of the Fifteenth International
Conference. San Francisco, CA: Morgan Kaufmann.
105
Inference / ML, Collaborative Filtering
106
Progress With Collaborative Filtering
Problems with traditional methods
• Lack of sound theoretical basis
• Information loss because of limited overlap in ratings
Approach using better-understood machine learning
methods
• Treatment of problem as one of classification learning
• Reduction of dimensionality so that overlap in specific ratings is less
important
• Demonstrable improvement in precision over traditional methods
ML, General Discussion
Discussion (1)
106
Knowledge acquisition effort
In general relatively little
The systems typically start with very few assumptions about users in
general, learning everything they need to know from the input data
But it is important to frame the learning problem carefully
Computational complexity
Complexity ranges from negligible to considerable; it seems in
general not to pose a major problem
Since learning often takes place over a number of sessions, some
computation can often be performed off-line, between sessions
Amount of input data required
Typically, a large amount, preferably collected over a number of
sessions with the user in question
Exceptions
• instance-based learning, as in NewsDude
• methods which exploit data collected from other users
107
107
Inference / ML, General Discussion
108
Discussion (2)
Handling of noise and uncertainty
The methods applied so far to user-adaptive systems take into
account the noisiness of the data
The conclusions derived are often associated with an indication of S’s
confidence; but the representation of such uncertainty is less explicit
and sophisticated than for Bayesian networks
Validity and justifiability
Confidence in the applicability of the techniques is in general based
on empirical tests of their performance in the specific domain in
question
Machine learning researchers have conducted more thorough
empirical tests than other researchers into user-adaptive systems
There is in general no a priori reason to be confident that the
technique will achieve a particular level of performance in a given
setting
Inspection of the individual learned rules, probabilities, weights, etc.,
does not in general permit an assessment of their validity
Logic
Introduction (1)
108
The role of logic in user-adaptive systems
The most salient strength of logic is the possibility of using it to
• represent beliefs of U in a precise and differentiated way;
• make inferences on the basis of the content of these beliefs
Logic can also be used to represent and make inferences about other
aspects of a user
But its suitability, relative to other representation and inference
techniques, is less clear when U’s beliefs are not involved
Accordingly, logic is typically used in conjunction with other
approaches to representation and inference
109
Inference / Logic
110
Introduction (2)
109
General references
Overview of applications to user-adaptive systems
Part I of Pohl, W. (1998). Logic-based representation and reasoning
for user modeling shell systems. Sankt Augustin: infix.
Relevant books of broader scope
Ballim, A., & Wilks, Y. (1991). Artificial believers: The ascription of
belief. Hillsdale, NJ: Erlbaum.
Fagin, R., Halpern, J. Y., Moses, Y., & Vardi, M. Y. (1995).
Reasoning about knowledge. Cambridge, MA: MIT Press.
Genesereth, M. R., & Nilsson, N. J. (1987). Logical foundations of
artificial intelligence. Palo Alto, CA: Morgan Kaufmann.
Logical Inference in BGP-MS
110
GENERALLY RELEVANT
(POSSIBLY HIGHER-ORDER)
BELIEFS AND GOALS OF U
LOGICAL INFERENCE
ACTIONS AND
SELF-ASSESSMENTS OF U
LOGICAL INFERENCE
DECISION-RELEVANT
BELIEFS AND GOALS OF U
111
111
Inference / Logic
112
U’s Beliefs About the World (1)
U’s specific beliefs:
DOC1 is a PostScript document
PS-document(DOC1)
LPT1 is either a laser printer or an
inkjet printer
laser-printer LPT1 inkjet-printer LPT1 U’s general beliefs:
PostScript documents are printable
on PostScript printers
PostScript documents are printable
on inkjet printers
DOC1 is printable on LPT1
PS-document PS-printer printable-on PS-document injket-printer printable-on printable-on DOC1 LPT1
Jameson, A. (1995). Logic is not enough: Why reasoning about another
person’s beliefs is reasoning under uncertainty. In A. Laux & H. Wansing
(Eds.), Knowledge and belief in philosophy and artificial intelligence
(pp. 199−229).Berlin: Akademie Verlag.
U’s Beliefs About the World (2)
112
Strengths and limitations of this approach
+ Complex beliefs can be represented (e.g., in first-order predicate
calculus)
+ The use of modal operators can often be avoided if the statements
are maintained in a partition of beliefs ascribed to U
+ The logical inferences that U can make on the basis of her beliefs
can be computed straightforwardly
− The partition approach can’t handle S’s uncertain beliefs about U’s
beliefs
• Either U thinks LPT1 is a laser printer or U thinks LPT1 is an
inkjet printer
• U probably knows that PostScript documents are printable on
inkjet printers
− Inferences presuppose that the relevant background knowledge can
be ascribed to U
There is often no direct evidence about U’s background
knowledge (cf. Jameson, 1995)
113
113
Inference / Logic
114
More Complex Intensional Ascriptions (1)
U’s goals and desires
U wants to print DOC1 on LPT1
If U wants to print a document on
a printer, then U must believe that
the document is printable on that
printer
Therefore, U believes that DOC1
is printable on LPT1
General inference schemas
If U wants to do something and
!#"$&% printed-on ' DOC1 ( LPT1 )
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consequence, then U wants that
consequence
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More Complex Intensional Ascriptions (2)
U’s higher-order beliefs
U believes that S believes that
U believes that DOC1 is
printable on LPT1
U believes that it is mutually
believed that DOC1 is printable
on LPT1
U#VU9WTU#VU9W
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114
printable–on X DOC1 Y LPT1 Z
printable–on ` DOC1 a LPT1 b
Strenghts and limitations of this approach
+ Sophisticated representation and reasoning involving important
concepts are possible
- Reasoning with modal logic can be computationally very complex
- Here again, the required premises are often unknown or uncertain
115
115
Inference / Logic
116
Discussion (1)
Knowledge acquisition effort
Systems that use logic sometimes presuppose many assumptions
about users in general that actually require verification
Computational complexity
Complexity is mainly a problem in connection with reasoning about
higher-order beliefs with modal logic
A good deal of effort has been devoted to finding ways of keeping
complexity within reasonable limits
Amount of input data required
A logic-based system can make an inference on the basis of a single
specific fact about U
Discussion (2)
116
Fagin, R., & Halpern, J. Y. (1994). Reasoning about knowledge and
probability. Journal of the Association for Computing Machinery, 41,
340−367.
Handling of noise and uncertainty
Logic-based systems in this area have in general treated uncertainty
management as a matter of secondary priority
A typical approach is to view inferences as being defeasible and to
withdraw them when contradictory evidence is obtained, using
truth-maintenance methods
Research integrating logic and probabilistic methods (see, e.g., Fagin
& Halpern, 1994) has so far had little impact in the area of
user-adaptive systems
Validity and justifiability
The inference methods used are normally known to be valid
The propositions represented and the inferences made are in general
understandable
Questions about validity arise when uncertainty about the
propositions being used in inference is not taken into account
117
Inference / Stereotypes
118
Stereotypes
117
Introduction (1)
What is a stereotype?
A stereotype comprises two main components (Rich, 1989, cited
below):
• a body, which contains information that is typically true of users to
whom the stereotype applies
• a set of triggers, which are used to determine whether the
stereotype applies to a given user
Upward inference occurs when the condition corresponding to a
trigger is satisfied and the stereotype is activated
Downward inference occurs when one or more of the facts in the
body of an activated stereotype is assumed to apply to the user in
question
Introduction (2)
118
Hierarchies and multiple inheritance
Stereotypes may be arranged in hierarchies
More than one stereotype may be active for a given U
Conflicts arising through multiple inheritance are typically resolved
through the application of heuristic rules
Classic general references
Rich, E. (1979). User modeling via stereotypes. Cognitive Science, 3,
329−354.
Rich, E. (1989). Stereotypes and user modeling. In A. Kobsa & W.
Wahlster (Eds.), User models in dialog systems (pp. 35−51). Berlin:
Springer.
Chin, D. N. (1989). KNOME: Modeling what the user knows in UC. In
A. Kobsa & W. Wahlster (Eds.), User models in dialog systems
(pp. 74−107). Berlin: Springer.
Figure 9 of Kobsa and Pohl (1995)
Figure 8 of Kobsa, A., & Pohl, W. (1995). The user modeling shell system
BGP-MS. User Modeling and User Adapted Interaction, 4, 59−106.
119
119
Inference / Stereotypes
Stereotypes in BGP-MS (2)
120
Stereotypes in BGP-MS (1)
120
Figure 1 of Hohl, H., Böcker, H., & Gunzenhauser, R. (1996). Hypadapter:
An adaptive hypertext system for exploratory learning and programming.
User Modeling and User-Adapted Interaction, 6, 131−156. Figures from this
article reproduced with permission.
121
121
Inference / Stereotypes
TRIGGERING OF
STEREOTYPES
ACTIONS AND
SELF-ASSESSMENTS OF U
Personal Questionnaires (1)
122
Inference in Hypadapter
MEMBERSHIP IN ONE OR MORE
STEREOTYPES
(POSSIBLY MULTIPLE)
INHERITANCE
SPECIFIC
DECISION-RELEVANT FACTS
ABOUT U
122
123
124
Personal Questionnaires (2)
Figure 2 of Hohl et al. (1996).
123
Inference / Stereotypes
Figure 8 of Hohl et al. (1996)
Content of User Model
124
125
Inference / Stereotypes
126
Stereotype Triggering Rules
125
Rule "Specified-explicitly"
Condition
U has specified her level of qualification as "Expert"
Triggered stereotype
Based on Figure 10 of Hohl et al. (1996)
Expert
Rule "Sufficient-topic-knowledge"
Condition
There are at least 15 topics T such that U’s knowledge of T equals or
exceeds the knowledge of T expected for an Expert
Triggered stereotype
Expert
Evaluating Topics for Presentation
Rule "Easy-Topic-for-Beginners"
Conditions
U is a Beginner
The level of difficulty of Topic T is "Mundane" or "Simple"
Based on Figure 13 of Hohl et al. (1996)
Scoring
Add 10 points to the appropriateness score for T
Rule "Avoid-Known-Topic"
Condition
According to the user model, U knows Topic T
Scoring
Subtract 20 points from the appropriateness score for T
126
127
Inference / Stereotypes
128
Discussion (1)
127
Knowledge acquisition effort
In principle, the same sort of effort is required as for Bayesian
networks:
What stereotypes should be defined?
What triggering rules are appropriate?
What properties can be inherited from each stereotype?
...
In practice, systematic knowledge acquisition for stereotype-based
systems has been rare
Computational complexity
Low
Amount of input data required
Stereotypes are typically triggered on the basis of only a few
observations
Discussion (2)
128
Handling of noise and uncertainty
Various calculi for representing S’s confidence in its inferences have
been employed
• uncertainty about the assignment of U to a given stereotype
• uncertainty about the inheritance of particular properties from that
stereotype
These methods are less well understood than probabilistic methods
Validity and justifiability
Stereotypes don’t explicitly represent propositions that can be judged
to be true or false
The inference methods don’t guarantee the truth of the conclusions
given the truth of the premises
The various aspects of a given system can be judged to be more or
less plausible, and the performance of the system can be evaluated
129
Empirical Foundations / Overview
130
Empirical Foundations / Overview
129
Overview of Types of Empirical Studies
Studies that don’t require a running system
1. Results of previous research
2. Early exploratory studies
3. Knowledge acquisition from experts
Studies with a system
4. Controlled evaluations with users
5. Experience with real-world use
Questions Addressed by Empirical Studies (1)130
1. Correctness of assumptions about users relied on by
inference techniques
Each system typically presupposes some general assumptions
about users that are not logically necessarily correct. Can these
assumptions be shown empirically to be correct?
Bayesian networks: network structure, probabilities
Logic: general propositions about users that S assumes to be
true; simplifying assumptions that are made to make inference
easier
Stereotypes: specific triggering rules, assumptions underlying
inheritance mechanisms
131
Empirical Foundations / Overview
132
Questions Addressed by Empirical Studies (2)
131
2. Appropriateness of the inference techniques used
Are the techniques used well suited to dealing with the inference
tasks faced by S?
E.g., can they deal with the amount of noise actually present in
the input data?
3. Adequacy of available data
Is there typically enough data available about each user U to
enable S to make useful inferences about U?
4. Adequacy of coverage
Does S take into account enough of the relevant input data and
user properties to be able to make a useful number of appropriate
adaptation decisions?
If not, S’s adaptation may be of little use, even if it meets the other
criteria
Questions Addressed by Empirical Studies (3)132
5. Appropriateness of adaptation decisions
Do the adaptation decisions that S makes on the basis of
decision-relevant properties actually improve the quality of U’s
interaction with S?
Even if S fulfills all other criteria, its adaptation decisions may
be useless or harmful if they don’t take into account important
factors
E.g., even if S correctly identifies the menu options that U
needs most, it may be a bad idea to move these to the top of
the respective menus
The resulting unpredictability of the menus could outweigh any
advantage of the adaptation
133
Empirical Foundations / Results of Previous Research
134
Results of Previous Research
Figure 1 of Corbett, A. T., & Bhatnagar, A. (1997). Student modeling in the
ACT programming tutor: Adjusting a procedural learning model with
declarative knowledge. In A. Jameson, C. Paris, & C. Tasso (Eds.), User
modeling: Proceedings of the Sixth International Conference, UM97
(pp. 243−254). Wien, New York: Springer Wien New York.
(http://www.cs.uni-sb.de/UM97/)
133
A Research-Based Tutor
The ACT tutors are based on a large body of theoretical and empirical
research into cognitive psychology and cognitive modeling
Discussion
134
Previous research can in principle yield valuable information about
almost any of the questions listed above
But it does not in general refer to a system like S or even to a similar
setting
Consequently, a good deal of extrapolation is typically required
It may be necessary to conduct studies in the domain of S in order to
determine the applicability of previous research results
135
Empirical Foundations / Early Exploratory Studies
136
Early Exploratory Studies
Horvitz, E., Breese, J., Heckerman, D., Hovel, D., & Rommelse, K. (1998).
The Lumière project: Bayesian user modeling for inferring the goals and
needs of software users. In G. F. Cooper & S. Moral (Eds.), Uncertainty in
Artificial Intelligence: Proceedings of the Fourteenth Conference
(pp. 256−265). San Francisco: Morgan Kaufmann.
(http://www.sis.pitt.edu/~dsl/UAI.html)
135
Wizard of Oz Study: Method
The studies were based on a "Wizard of Oz" design. In the studies,
naive subjects with different levels of competence in using the
Microsoft Excel spreadsheet application were given a set of
spreadsheet tasks. Subjects were informed that an experimental help
system would be tracking their activity and would be occasionally
making guesses about the best way to help them. Assistance would
appear on an adjacent computer monitor. Experts were positioned in
a separate room that was isolated acoustically from the users. The
experts were given a monitor to view the subjects’ screen, including
their mouse and typing activity, and could type advice to users.
Experts were not informed about the nature of the set of spreadsheet
tasks given to the user. Both subjects and experts were told to
verbalize their thoughts and the experts, subjects, and the subjects’
display were videotaped.
Wizard of Oz Study: Results (1)
136
Difficulty of the system’s task
We found that experts had the ability to identify user goals and needs
through observation. However, recognizing the goals of users was a
challenging task. Experts were typically uncertain about a user’s
goals and about the value of providing different kinds of assistance.
At times, goals would be recognized with an "Aha!" reaction after a
period of confusion.
Consequences of poor advice
We learned that poor advice could be quite costly to users. Even
though subjects were primed with a description of the help system as
being "experimental", advice appearing on their displays was typically
examined carefully and often taken seriously; even when expert
advice was off the mark, subjects would often become distracted by
the advice and begin to experiment with features described by the
wizard. This would give experts false confirmation of successful goal
recognition, and would bolster their continuing to give advice pushing
the user down a distracting path.
137
137
Empirical Foundations / Early Exploratory Studies
138
Wizard of Oz Study: Results (2)
Suggestions for desirable strategies
Experts would become better over time with this and other problems,
learning, for example, to become conservative with offering advice,
using conditional statements (i.e.,"if you are trying to do X, then..."),
and decomposing advice into a set of small, easily understandable
steps.
Discussion
Other methods in this category include interviews with − or
observation of − users of a nonadaptive version of S
These methods can yield valuable information about all of the
questions listed above
Getting this information at an early stage can help to avoid poor
design decisions that may later be difficult or impossible to change
138
139
Empirical Foundations / Knowledge Acquisition
140
Knowledge Acquisition
Figure 4 of Heckerman, D., & Horvitz, E. (1998). Inferring informational goals
from free-text queries: A Bayesian approach. In G. F. Cooper & S. Moral
(Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth
Conference (pp. 230−237).San Francisco: Morgan Kaufmann.
(http://www.sis.pitt.edu/~dsl/UAI.html)
139
Elicitation of Probabilities from Experts
Early in the design of the Answer Wizard, usability experts identified key
terms employed by users and assessed the conditional probabilities of
their being used in a query, given that U had a particular goal.
p(term | goalj)
Modifying chart
Formatting document
• chart
• graph
• cells
• row
• column
• insert
• modify
• edit
• format
• plot
• file
•
Discussion
140
Knowledge acquisition from domain experts has a special role in that
adaptation to a given person has so far been something that humans
are much better at than computers
For some types of system, there are persons who have extensive
experience with the type of adaptation required
The knowledge acquired can concern all of the questions listed
above
The general comments about early exploratory studies apply here as
well
141 Empirical Foundations / Controlled Evaluations With Users 142
Controlled Evaluations With Users
141
Value of Adaptivity in the Route Advisor
75
Percent Correct
Figure 7 of Rogers, S., Fiechter, C., & Langley, P. (1999). An adaptive
interactive agent for route advice. Proceedings of the Third International
Conference on Autonomous Agents, Seattle, WA.
100
50
25
Individualized model
Aggregate model
0
1
3
5
7
9
11
13
15
17
19
21
23
Subject Number
The Adaptive Route Advisor’s personalized models consistently
outpredicted an aggregate model, even though each personalized
model was based on only a small fraction of the data
Pazzani, M., & Billsus, D. (1997). Learning and revising user profiles: The
identification of interesting web sites. Machine Learning, 27, 313−331.
Discussion
142
Controlled evaluations differ in the extent to which they
• focus on specific aspects of S (e.g., the inferences made) or on
S’s overall effectiveness
• are conducted in a setting that approximates that of the real use
of S
The more focused studies mainly yield insight into
• the correctness of assumptions about users in general and
• the appropriateness of the inference techniques used
A frequent type of study of this sort compares the effectiveness of
several variants of a given modeling or inference technique
E.g., naive Bayesian classification vs. more complex classification
techniques (see, e.g., Pazzani & Billsus, 1997)
143 Empirical Foundations / Experience With Real-World Use 144
Experience With Real-World Use
143
Evaluation of Calendar Apprentice in Use (1)
PREDICTION ACCURACY
USER B: LOCATION
100
80
80
60
60
40
40
20
20
0
0
Mar
Jun
Sep
Jan
Jun
Sep Dec
Jan
USER A: DURATION
PREDICTION ACCURACY
Top half of Figure 2 of Mitchell, T., Caruana, R., Freitag, D., McDermott, J.,
& Zabowski, D. (1994). Experience with a learning personal assistant.
Communications of the ACM, 37(7), 81−91. Explanation on next slide
USER A: LOCATION
100
Jun
Sep
Dec
USER B: DURATION
100
100
80
80
60
60
40
40
20
20
0
0
Mar
Jun
Sep
Jan
Jun
Sep Dec
Jan
Jun
Sep
Dec
Evaluation of Calendar Apprentice in Use (2) 144
The previous figure shows CAP’s accuracy over time in predicting, for
each of the users A and B, two aspects of their scheduling behavior:
• choice of location for a meeting
• choice of duration
Note the drops in accuracy at times in the year when scheduling
habits typically change
The authors also reported a number of more specific analyses
145 Empirical Foundations / Experience With Real-World Use 146
145
Discussion
Studies of real use can potentially yield highly specific answers to all
of the questions listed above
Because of the lack of control, it may be hard to pinpoint the reasons
for particular (positive or negative) results
Building a version of the system that can be tested in real use may be
impractical at an early stage; and at a late stage the insights gained
may be hard to take into account
Concluding Remarks
Concluding Remarks
146
• In the past, research on user-adaptive systems has been
unnecessarily fragmented
• Specialized communities focused on particular types of application
or particular implementation techniques
• Design decisions were often taken by default, rather than on the
basis of a consideration of all available options
• Recent conferences and publications have seen an increasing
tendency for researchers to adopt ideas and results that originated
in other research communities
• If this trend is strengthened, we may expect an acceleration of
progress in this already fast-moving field