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Meta-adaptivity and self-regulation: Towards the next generation of adaptive systems
Alexandros Paramythis
Johannes Kepler University
Linz, Austria
Paramythis, Meta-adaptivity and Self-regulation 2
Outline Introduction
» Problem space
Meta-adaptivity and self-regulation
» Definitions
» Examples
» Operational requirements
An example
» The system
» Evolution of adaptive behaviour
Adaptive system development revisited
» New possibilities
» New requirements
» Applicability
» Overhead
» Constraints
Discussion
Introduction
Paramythis, Meta-adaptivity and Self-regulation 4
Glossary! SeR Self-Regulation AS Adaptive System(s) SeRAS Self-Regulating Adaptive System(s)
Paramythis, Meta-adaptivity and Self-regulation 5
Problem space (1/2) Design / authoring of adaptive systems still hard,
despite recent progress in
» availability of frameworks and tools
» accumulation of validated design knowledge
Two major difficulties (among others):
» sometimes the design corpus is unavoidably poor, incomplete, or based on intuition
» “evolution” of an adaptive system is an exclusively “manual” task
Paramythis, Meta-adaptivity and Self-regulation 6
Problem space (2/2) A symmetric situation in the evaluation of adaptive
systems
Because of the complexity involved,
» people are often not willing to interfere with a working system; thus,
» deployed adaptive systems are even less likely to be evolved than their non-adaptive counterparts
Paramythis, Meta-adaptivity and Self-regulation 7
Meta-adaptivity to the rescue Let the system “participate”, by
» “trying out” alternative adaptive behaviours / strategies (e.g., when designers are unsure about their applicability)
» “building up” new pieces of adaptation design knowledge through the “findings” of such trials
» aggregating results across a large number of users, to derive knowledge even in the absence of trials
To achieve the above, use
» Meta-adaptivity, and, specifically, self-regulation
Meta-adaptivity and Self-regulation
Paramythis, Meta-adaptivity and Self-regulation 9
Meta-adaptivity and the second-level cycle
Environment Adaptor mechanism
Users
input
output
ADAPTIVEADAPTIVETHEORYTHEORY
low level theory
variant
Lower Level Adaptor
HigherLevel
Adaptor
User Interface Variants(flexibility)
Interaction Cues(evidence of user needs)
User / TaskModels(the needs)
Logical diagram for a two-level adaptation architecture for user interfaces;adapted from (Totterdel and Rautenbach, 1990)
Paramythis, Meta-adaptivity and Self-regulation 10
Self-regulation: Main points Self-regulating adaptive systems (SeRAS)
» are entry level “meta-adaptive” systems
» have “second-level” adaptation cycle
» “learn” dynamically how to modify their behaviour to accommodate different users, context of user, etc.
» typically “know” a priori the alternative adaptive behaviours / strategies
Paramythis, Meta-adaptivity and Self-regulation 11
Examples of SeRAS (1/2) Recommender system
» Recommends items to users
» Capable of multiple recommendation strategies• Item characteristics, User characteristics, Collaborative filtering,
combinations thereof
» Capable of switching between strategies
» The criteria for success may vary• E.g., user never shows interest in recommended items
Paramythis, Meta-adaptivity and Self-regulation 12
Examples of SeRAS (2/2) Adaptive Collaboration Support System
» Adaptively supports the establishment of groups that collaborate on a topic (e.g., learning task)
» Support is in the form of “neighbourhood” visualisations
» Capable of calculating / visualising neighbourhoods in multiple ways
» Criteria for success of a given neighbourhood algorithm / visualisation
• E.g., actual group establishment and longevity
Paramythis, Meta-adaptivity and Self-regulation 13
Operational Requirements for SeRAS Observing interaction
Observing adaptive behaviour
Self-evaluation
Modifying adaptive behaviour
Paramythis, Meta-adaptivity and Self-regulation 14
SeRAS Requirements: Observing interaction Already part of the first-level adaptation cycle for all
adaptive systems
» Prerequisite for adaptive behaviour in the first place!
No additional implications.
Paramythis, Meta-adaptivity and Self-regulation 15
SeRAS Requirements: Observing adaptive behaviour
Adaptive behaviour must be “broken down” to distinct constituents
» Granularity may vary
This requirement is possible to relax somewhat, but
It restricts the range of adaptive systems on which self-regulation can be applied
Paramythis, Meta-adaptivity and Self-regulation 16
SeRAS Requirements: Observing adaptive behaviour
Implications
» Notification when adaptations occur
» Identification of adaptations • Semantically interpretable, or, • Uniquely identifiable, or, at least,• Of a uniquely identifiable type
Paramythis, Meta-adaptivity and Self-regulation 17
SeRAS Requirements: Self-evaluation Most demanding of the requirements
Involves assessment of (degree of) success of adaptive behaviours
An overwhelming range of possibilities!
Proposal: principled approach based on “expectations”
Paramythis, Meta-adaptivity and Self-regulation 18
SeRAS Requirements: Self-evaluation Proposed approach
» Define expectations (adaptation “theory”)
» Operationalise expectations with respect to• User interactive behaviour• Changes in the modelled interaction state
» Provide SeRAS with expectations expressed in computable form
» Computation models may vary as needed
Paramythis, Meta-adaptivity and Self-regulation 19
SeRAS Requirements: Self-evaluation Implications
» Quantifying changes in interaction state• Input: direct user input, current values from the static and dynamic
models of the system, “historical” values from the same models, as well as interim results from previous calculations
• Output: depends on computational approach• Computation: depends on system, but generality possible given
sufficient similarities in AS• Association with behaviours being evaluated: Understanding vs.
simple identification
Paramythis, Meta-adaptivity and Self-regulation 20
SeRAS Requirements: Modifying adaptive behaviour
Implies either (or both) of
» Changing first-level strategy / theory• Most straightforward of the alternatives
» Overriding adaptation outcomes
Plausibility and feasibility depend, mainly, on the decision making approach of the adaptive system
Paramythis, Meta-adaptivity and Self-regulation 21
SeRAS Requirements: Modifying adaptive behaviour
Implications of changing first-level strategy / theory
» The most readily attainable level of “intervention”
» Requires that alternative strategies are represented in a way that allows for:
• identifying them individually,• “knowing” whether they can be combined and in what ways, • (de-) activating them on demand
» Ideally, strategies would be conveyed to the system in a declarative manner
• alternatively, any approach which would result in a run-time model of adaptation would also suffice from a technical perspective
Paramythis, Meta-adaptivity and Self-regulation 22
SeRAS Requirements: Modifying adaptive behaviour
Implications of overriding adaptation outcomes
» To override the outcome of adaptations, the system must be able to understand and predict that outcome
» This, in turn, implies the need for a “model” of adaptive behaviour (again, granularity may vary)
• nevertheless, more fine-grained than the strategy level
» It also implies that systems will be able to have models of reasonable interventions, in response to prescribed adaptive behaviours
» All in all, a very powerful approach, but quite some progress required before it is more readily attainable
Paramythis, Meta-adaptivity and Self-regulation 23
The SeRAS spaceM
od
elli
ng
“black-box”
“wh
ite-
bo
x”
Decision Making
“bla
ck-b
ox”
“white-box”
•insufficient second-level inputs •self-evaluation possible only through global metrics and direct user feedback•interventions only at global scope and only in the form of disabling
•full-scale self-evaluation possible, but •difficult or impossible to associate self- evaluation context with adaptations•interventions only at global scope and only in the form of disabling
•self-evaluation possible only through global metrics and direct user feedback•fine-grained interventions possible, but•only external adaptation overriding feasible, and•impossible to associate interventions with adaptation logic
•full-scale self-evaluation possible •fine-grained interventions possible
self-evaluation capabilities
interventioncapabilities
The example
Paramythis, Meta-adaptivity and Self-regulation 25
The system Along the lines of NetCoach and AHA!
Main characteristics
» Domain model: small, course-specific, module- and concept- oriented ontology as the
» User model: overlay model over the domain
» Updates in the user model: through direct observation and interpretation of user actions
» Adaptation logic: rule-based
» Adaptive function: generation of recommendations / predictions about the suitability of modules in relation to the user’s current knowledge
Paramythis, Meta-adaptivity and Self-regulation 26
The design question What is the best way to convey system
recommendations / predictions to users?
» “Competing” adaptation strategies:
Paramythis, Meta-adaptivity and Self-regulation 27
Evolution of adaptive behaviour Assumptions
» Design goal 1
• Provide navigation assistance so that users do not encounter concepts they are not “ready” for
» Design goal 2
• Apply as few restrictions as possible on navigation
» However, no evidence as to what strategy to use when / for whom
Keep in mind
» Iterative approach
• Example goes through 3 iterations, but this is rather arbitrary
» Can use design “input”
• Again, example assumes none, but no reason why one cannot have a corpus to start with
Paramythis, Meta-adaptivity and Self-regulation 28
Iteration 1: “Tabula rasa”
A. No annotationA. No annotation
B. Colored linksB. Colored links
C. Colored bulletsC. Colored bullets
D. Custom iconsD. Custom icons
E. Link hidingE. Link hiding
“LA
_Str
ateg
ies”
Step 1: Define strategies
Strategies
A.
No
anno
tatio
n
B.
Col
ored
link
s
C. C
olor
ed b
ulle
ts
D. C
usto
m ic
ons
E.
Link
hid
ing
A. No annotation X X X X B. Colored links C. Colored bullets X D. Custom icons E. Link hiding
Step 2: Specify whether / how they can be combined
Use
r m
odel
M1: maximise (UC1 over UC2 ) using “LA_Strategies”
Step 3: Specify self-regulation metrics
UC1 UC2
# of links followed (total)# of links followed (total)UC2UC2
# of “ready” links followed# of “ready” links followedUC1UC1
Ready to start testing Ready to start testing
Paramythis, Meta-adaptivity and Self-regulation 29
Iteration 2: Selection, categories, priorities (1/3)
A. No annotationA. No annotation
B. Colored linksB. Colored links
C. Colored bulletsC. Colored bullets
D. Custom iconsD. Custom icons
E. Link hidingE. Link hiding
“LA
_Str
ateg
ies”
Step 1: (system) Eliminate “unnecessary” strategies
D. + E. D. + E.
C. + E. C. + E.
B. + C. B. + C. B. + D. B. + D. B. + E. B. + E.
Step 2: (system) Provide preliminary categorisation and rankingStep 3: (designer) Add semantics
Cat. I
Cat. II
Cat. III
absolute freedom, no support
absolute freedom, explicit support
restricted navigation, partially enforced path
Continue testing Continue testing
Paramythis, Meta-adaptivity and Self-regulation 30
Iteration 2: Selection, categories, priorities (2/3) Evidence from first round of testing
» Suggests that some strategies can be eliminated • e.g., because they did not satisfy the metric(s) for any user• in our case this will be B –link colour only– and all combinations
» Provides support for a tentative categorisation and “ranking” • on the basis of, e.g., how well strategies “performed”; more general
similarities in their effects; similarities in the user population on which they are most effective; etc.
» Semantics of findings “added” by the designers
Paramythis, Meta-adaptivity and Self-regulation 31
Iteration 2: Selection, categories, priorities (3/3) Pending issues
» In which “direction” is the ranking to be applied / tested?
• “liberal” to “restrictive” (i.e., from I to III) for this example
» What are the “default” and / or “fallback” strategies?
• “default” is category I, and “fallback” is category III for this example
Paramythis, Meta-adaptivity and Self-regulation 32
Iteration 3: Binding to concrete adaptation logic Evidence from second round of testing might result in
the following concrete adaptation logic
» Novices and students unfamiliar with the knowledge domain / material category III
» Within category III, apply new ranking: (a) Strategy E
(b) Combined D and E
(c) Combined C and E
» Reserve category II for users sufficiently familiar with the system and the recommendation mechanism
» Only use category I for experienced users that are also familiar with the knowledge domain
» …
Adaptive system development revisited
Paramythis, Meta-adaptivity and Self-regulation 34
New possibilities and requirements The good – self-evolution
» Categorisation or “clustering” of strategies and “ranking”
» Derivation of concrete adaptation knowledge / logic
The bad – non-trivial
» Several technical requirements• Quite a few, but most importantly, the system must be capable of
self-evaluation• A bonus requirement for the user modelling community: the type of
analysis that takes place as part of self-evaluation requires that the system has access to “historical” states of a user’s model
» Potentially more involved design process
Paramythis, Meta-adaptivity and Self-regulation 35
New possibilities (1/2) Derivation of new adaptation knowledge / logic
» Analysis of similarities between the user models of users for whom adaptation strategies have resulted in comparable ouput from the self-regulation metrics
» Identification of “discriminating” user model attributes / values
» Human-assisted integration of new knowledge (enrichment with semantics also desirable in the process)
Paramythis, Meta-adaptivity and Self-regulation 36
New possibilities (2/2) Categorisation or “clustering” of strategies and
“ranking”
» Identification of strategies that have similar effects (with respect to metrics) given sufficiently similar user models
• provisional “clustering”, as well as preliminary “cause and effect” patterns
» Identification of differentiating subsets of models that render certain strategies more effective than others in a given context
• combined with semantic meta-data (e.g., level of navigation freedom a strategy affords) this can be turned into a ranking
Paramythis, Meta-adaptivity and Self-regulation 37
New requirements (1/2) Emerging technical requirements
» Adaptation strategies must be represented independently from the “driving” adaptation logic
• strong requirement
» Adaptation strategies (expressed potentially as sets of actions) must be applicable in combination
• weak requirement; can be simulated, albeit with extra work
» There must exist a representation of one or more adaptation “goals” that drive self-evaluation and selection / application of strategies
• again, a strong requirement, which, additionally precludes trivial approaches to the first two
Paramythis, Meta-adaptivity and Self-regulation 38
New requirements (2/2) Emerging technical requirements (cont.)
» The system must be capable of maintaining and employing a ranking amongst (combinations of) strategies
• strong requirement if ranking is desirable; manually attempting that typically prohibitive because of overhead
» Most importantly, the system must be capable of self-evaluation itself, which requires
• that the system “knows” about alternatives, and• that the system has a way of assessing said alternatives with
respect to the degree they satisfy design requirements
» And a bonus requirement for the user modelling community• the type of analysis that takes place as part of self-evaluation
requires that the system has access to “historical” states of a user’s model
Paramythis, Meta-adaptivity and Self-regulation 39
The role of meta-adaptivity Why do we need meta-adaptivity at all? What does it
bring to the design table?
» Capacity to test with end users large numbers of alternative behaviours
» (Semi-) automatic derivation of adaptation knowledge within the system
• although not discussed today, this approach can also be used to validate existing adaptation logic
» In short: it helps us design in ways that would be too expensive to apply manually
Paramythis, Meta-adaptivity and Self-regulation 40
The role of self-regulation Why self-regulation as a specific meta-form?
» Although not an exceptionally sophisticated form of meta-adaptivity, self-regulation suffices for the scenaria discussed today
» It is easier to implement than other forms, because it does not presuppose the generation of new strategies by the system
» In cases where the first-level adaptation cycle uses a declarative form of specification of adaptive behaviours, self-regulation can be implemented orthogonally to the primary adaptation mechanism
Paramythis, Meta-adaptivity and Self-regulation 41
Discussion (1/2) Applicability of the proposed approach?
» Of course, not universal
» Requires a “running system”
» Better suited to cases with a limited design corpus to boot, and / or with several competing design alternatives
Design / authoring overhead?
» On the one had, several additional tasks• formulation of strategies; formulation of metrics; review and validate
system results and propositions; incorporate improvements
» On the other hand though, major overlap with tasks that would need to be performed anyway in iterative design
• even though strategies and metrics may not need to be expressed in formal / computable forms
Paramythis, Meta-adaptivity and Self-regulation 42
Discussion (2/2) Additional constraints:
» Not a replacement for user studies!• but possibly a tool to facilitate aspects of such studies
» A tool to be used with care • by nature, self-regulation can pose an even greater threat to
usability qualities if used carelessly, either at design time, or in deployed systems
And, of course, new roles for humans!
Alexandros Paramythis
Johannes Kepler University
Linz, Austria
Thank you! . Questions?
F.A.Q.s – Extra slides
Paramythis, Meta-adaptivity and Self-regulation 45
F.A.Q.s Can self-regulation be implemented generically?
» Open-source framework with scheduled release in October is under active development
• Applicable to any XML “pipeline”; support for HTML as well• Pluggable user modelling components, and pluggable adaptation
logic (reasoning) components• Support for adaptation actions and strategies; independent from
either of the above through configurable “bindings”• Self-regulation features
– Integrated second-level cycle– Built-in expression language for formulating metrics – Analysis uses variations of existing data mining algorithms (mainly
different forms of multivariate analysis, reverse derivation of associations from clustering, etc.)
– Accumulated observations can be output as reports, or using the same expression language as metrics
– Too many more details to list here; ask for more info.
Paramythis, Meta-adaptivity and Self-regulation 46
F.A.Q.s What are some examples of a self-regulation metrics?
» One approach, using absolute thresholds:(count of followed links when in state “ready” / count of followed links when in state “not-ready”) > 0.5
» Alternative approach with relative ordering: maximise (count of followed links when in state “ready” / count of followed links when in state “not-ready”)
Paramythis, Meta-adaptivity and Self-regulation 47
F.A.Q.s Isn’t link annotation too simple an example?
» What happens when we address more complex issues?
» Or, what happens when we want to test several design aspects in parallel?
In fact, this is exactly why this approach is being proposed.
» It can “scale” well to more complex design issues• for instance, it can very easily be applied recursively; it can be extended to
accommodate for potentially competing design goals
» When applied properly, the discriminatory capacity of the approach is only limited by the make-up of the user sample participating in tests
• the more potentially interacting design variables one works with, the more care one must take in deciding the number and characteristics of participating users – just like in any user study