Cristina Conati Department of Computer Science University of British Columbia
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Transcript of Cristina Conati Department of Computer Science University of British Columbia
Cristina ConatiDepartment of Computer ScienceUniversity of British Columbia
Plan Recognition for User-Adaptive Interaction
Research Context
User-Adaptive Interaction (UAI): interaction that can better support individual users by adapting to their specific needs
User Modeling: how to infer, represent and reason about user features relevant for adaptivity.
User Model
AdaptationKnowledge/Skills
Beliefs/Preferences
Goals/PlansActivities
Emotions
Meta-cognitive skills………
Overview
Brief examples of our plan/goal/activity recognition work in the context of UAI
Two research directions– Using eye-tracking information to facilitate plan
recognition– Explaining to the user the reasoning underlying the
adaptive intervensions
Adaptive Support To Problem Solving
• A tutoring agent monitors the student’s solution and intervenes when the student needs help.
• Example: Andes, tutoring system for Newtonian physics (Conati et al UMUAI 2002)
Fw = mc*g
Think about the direction of N…
N
Several sources of uncertainty Same action can belong to different solutions, or
different parts of the same solution Solutions steps can be skipped - reasoning behind the
student’s actions can be hidden hidden from the tutor Correct answers can be achieved through guessing.
Errors can be due to slips There can be flexible solution step order
Probabilistic Student Model
Bayesian network (automatically generated)– represents how solution steps derive from physic rules and
previous steps
Captures student interface actions to perform– on-line knowledge assessment, – plan recognition – prediction of students’ actions
Performs plan recognition by integrating information about the available solutions and the student’s knowledge
Example 2
Solution• Find the velocity by applying the
kinematics equation Vtx
2 = V0x2 + 2dx*ax
• Find the acceleration of the car by applying
Newton's 2nd law Fx = Wx + Nx = m*ax
If the student draws the axes and then gets stuck, is she trying to write the kinematics equations to find V? trying to find the car acceleration by applying Newton’s laws
RuleR
Fact/GoalF/G
RA Rule Application
R -try-Newton-2law
R-find-all-forces-on-body
R- choose-axis-for-Newton
F-N-is-normal-force-on-carG-find-axis-for-kinematics
G-try-Newton-2law
G_goal_car-acceleration?R -try-kinematics-for-velocity
R-find-kinematics quantities
R- choose-axis-for-kinematics
F-D-is-car-displacement
G-try-kinematics
G_goal_car-velocity?
F-A-is-car-acceleration
G-find-axis-for-newton
0.5 0.9
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0.8
0.7
0.5
1.0
F-axis-is 20
0.95
0.83
0.9
0.5
0.68
0.4
0.2 0.9 0.72
/ 0.9
/ 0.6
CPTs
Evaluation
Several studies showed effectiveness of Andes tutoring
Could not evaluate the plan recognition component directly, because of lack of ground truth values
(Conati et al UMUAI 2002)
Adaptive Support To Learning From Educational Games
Tricky problem- Help students learn- While maintaining fun
And engagement
Model of UserKnowledge
Model of UserAffect
Goal recognitoon for Modeling User Affect (via Cognitive Appraisal) (Conati Maclaren 2009)
GoalsSatisfied
Goals
Personality
InteractionPatterns
Emotion towardGame state
ti
UserAction
Outcome
EmotionsTowards Self
Agent ActionOutcome
GoalsSatisfied
Goals
ti+1
Emotion towardGame state
EmotionsTowards Agent
Personality
InteractionPatterns
Subnetwork for Goal Assessment
Goals
Extraversion
NeuroticismAgreeableness
Conscientiousness
HaveFun
AvoidFalling
BeatPartner
LearnMath
Succeed byMyself
Follow Advice FallOften
Ask AdviceOften
Move Quickly
Use Mag. GlassOften
Personality [Costa and McCrae, 1991]
Interaction Patterns
Links and probabilities derived from data (Zhou and Conati 2003)
Evaluation
DDN with goal assessment performs better than variation with goals initialized with population priors
Pretty good results on emotions recognition (~70%), but could be improved if we modeled goals as dynamic (changing priorities)
(Conati et al UMUAI 2002, Conati 2010)
Current work
See if we can improve goal recognition by including information on user attention
In previous research, we showed that including gaze information can improve a system’s prediction of user reflection/learning (Conati and Merten, Intelligent User Interfaces 2007)
We are now looking at whether eye-tracking can help recognize user goals and intentions (hints, no hints)
Adaptive Support To Interface Customization
MICA: Mixed-initiative support in creating a “personal interface”with tailored toolbar and menu entries (Bunt Conati Macgrenere IUI 2007)
Example: Adding Features
Example: Adding Features
Suggestions Generation
User Performancewith a given
Personal interface
Expertise
Expected Usages
Interface Layout
Personal interface with best expected performance
Overview
Brief examples of our plan/goal/activity recognition work in the context of UAI
Two research directions– Using eye-tracking information to facilitate plan
recognition– Explaining to the user the reasoning underlying the
adaptive interventions
How to provide effective adaptivity without violating the basic principles of HCI– Predictability, Controllability, Unobtrusiveness,
Transparency
One possible approach: – Enable the system to explain to the user the rationale
underlying its suggested adaptive interventions
One Challenge of UAI
Example: Adding Features
Rationale: How
Rationale: How
Formal Evaluation of Mica’s rationale
Compared versions of MICA with and without rationale [Bunt , Mcgrenere and Conati UM 2007]
Within subject laboratory study. – Participants performed guided tasks with MSWord, designed
to motivate customization User Model initialized with accurate information
– Expected usages frequencies obtained from guided tasks– Expertise obtained via detailed questionnaire
Study 2 (Rationale vs. No Rationale): Main Findings
No performance differences
94.2% (Rationale) vs 93.3% (No Rationale) recommendations followed
Preference Results
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Overall Agreement Trust SpecificUnderstanding
GeneralUnderstanding
Predictability
# of
par
ticip
ants
Rationale
No-Rationale
Equal
Majority of users prefer to have the rational present, but non-significant number don’t need or want it.
Identified aspects of this context that may make rationale unnecessary for some– Found the rational to be common sense– Unnecessary in a mixed-initiative interaction or
productivity application– Inherent trust
Design implications: rationale should be available but not intrusive
rationale
no rationale
Open Questions
When is it important to provide the rationale? How much information should be given? How to handle user feedback?
Conclusions
Plan/Goal/Activity recognition crucial in user-adaptive interaction
Important to explore new sources of information for accurate user modeling– E.g. eye tracking
Important to increase UAI acceptance via mixed-initiative approaches, that possibly include explanations of system behavior
Understanding user goals and limitations in interactive with information visualizations
How can gaze information help?
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