meta-adaptivity and self-regulation: towards the next generation of adaptive systems alexandros...

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


Glossary! SeR Self-Regulation AS Adaptive System(s) SeRAS Self-Regulating Adaptive System(s)


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


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


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


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)


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


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


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


Operational Requirements for SeRAS Observing interaction

Observing adaptive behaviour

Self-evaluation

Modifying adaptive behaviour


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.


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


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


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”


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


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


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



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



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


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


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


The design question What is the best way to convey system

recommendations / predictions to users?

» “Competing” adaptation strategies:


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


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


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


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


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


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


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


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)


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


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


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


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


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


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


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


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.


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”)


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

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