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Application-specific constraints for multimedia presentation generation. Joost Geurts, Jacco van Ossenbruggen and Lynda Hardman CWI Amsterdam email: Joost.Geurts @cwi.nl. Talk overview. Generating multimedia automatically Cuypers multimedia generation engine Multimedia and constraints

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application specific constraints for multimedia presentation generation

Application-specific constraints for multimedia presentation generation

Joost Geurts, Jacco van Ossenbruggen

and Lynda Hardman

CWI Amsterdam

email: Joost.Geurts@cwi.nl

talk overview
Talk overview
  • Generating multimedia automatically
  • Cuypers multimedia generation engine
  • Multimedia and constraints
    • Quantitative constraints
    • Qualitative constraints
  • Cuypers demo
  • Conclusion, future directions
generating adaptive multimedia
Generating adaptive multimedia
  • Content
    • Large multimedia database
  • System profile
    • PC, PDA, WAP
  • Network profile
    • Modem, Gigabit
  • User profile
    • Language, Interests, Abilities, Preferences

Too costly to author manually

automatic multimedia generation
Automatic multimedia generation
  • Designer does not specify complete presentation……but only specifies requirements
  • System automatically finds a solution which meets requirements
  • How should the requirements be specified?
    • Declarative constraints
traditional use of constraints
Traditional use of constraints
  • Constraint solving used for problems with:
    • Many variables
    • Large domains
  • Based on domain reduction paradigm
  • Quantitative constraints
    • Integer domain
    • Reduction by arithmetic relations
      • Greater than (>)
      • Less than (<)
      • Equals (=)
drawbacks of quantitative constraints
Drawbacks of quantitative constraints
  • Too many (trivial) solutions that differ by:
    • 1 pixel position, or
    • 1 milliseconds in timing
  • Not sufficiently expressive

e.g. cannot specify “no overlap” constraint

  • Too low level

e.g. A.X2  B.X1

solution qualitative constraints
Solution: qualitative constraints
  • Example “A not overlap B”, “B after C”
  • Advantages:
    • More intuitive
    • More expressive
    • Smaller domains
  • Qualitative solutions translate automatically to lower level quantitative constraints
  • New problem: What if constraints are insoluble?
solution constraint logic programming
Solution: Constraint Logic Programming
  • Combine Prolog unification and backtracking with constraint solving
  • Use Prolog rules to generate constraints
  • Backtrack when constraints are insoluble
cuypers generation engine
Cuypers generation engine
  • Multiple layers:
    • Prolog rules to generate constraints
    • Qualitative constraints translate to quantitative constraints
    • Solution of both constraints provides sufficient information for final presentation
cuypers demo scenario
Cuypers demo: scenario
  • Client:User is interested in Rembrandt and wants to know about about the “chiaroscuro” technique
  • Server: Query database
  • Server: Generate constraints according to:
    • System profile
    • User profile
    • Network profile
  • Server: Solve constraints / revise constraints
  • Server: Generate SMIL presentation
  • Client: Play presentation
conclusions
Conclusions
  • Quantitative constraintsare insufficient for automatic multimediapresentation generation. Also need
  • Qualitative constraintsto allow intuitive and effectivehigh level specification, and
  • Backtrackingfor revising specific constraintsthat cause the entire set to fail
future directions
Future directions
  • Best-first instead of depth-first
    • Choose “best” among possible solutions
    • Needs evaluation criteria
  • Improve knowledge management
    • Make design knowledge declarative and explicit
    • Preserve metadata in final presentation
    • Use standardized and reusable profiles
need to make trade offs
Need to make trade-offs
  • Semantics
    • Convey message
  • Aesthetics
    • Clear / nice layout
  • Resources
    • Screen size, bandwidth
  • Dimension may result in conflicting goals
quantitative constraints
Quantitative Constraints

% csp(+Ids, -Boxes)

csp([IdA,IdB],[box(IdA,[x1:AX1, …]), box(IdB,[x1:BX1,…])]) :-

% get values

maxX(MaxX), maxY(MaxY),

height(IdA,HeightA),

widtht(IdA,WidthA),

% define domains

[AX1,AX2,BX1,BX2]::[0..MaxX],

[AY1,AY2,BY1,BY2]::[0..MaxY],

% set width & height

AX2 – AX1 #= WidthA,

AY2 – AY1 #= HeightA,

% constraints

AX2 #< BX1, % A left-of B

AY1 #= BY1, % A top-align B,

true.

multimedia and constraints
Multimedia and Constraints
  • Constraint Logic Programming
    • Domain reduction
    • Backtracking
    • Unification (matching rules)
  • Qualitative Constraints
    • Non-integer domain
    • Allen’s 13 temporal interval relations in three dimensions
qualitative constraints
Qualitative Constraints
  • Example:
    • Two images, A,B
    • A left or right of B
    • A not above or below B
qualitative constraints1
Qualitative Constraints

% csp(+Ids, -Graph)

csp([IdA, Idb], [edge(IdA,IdB,x,NoOverlap),…]) :-

% define domains

NoOverlap :: [b,b-,m,m-],

Overlap :: [d,d-,s,s-,f,f-,e],

% constraints

edge(IdA,IdB,x,NoOverlap), % B not-overlap A

edge(IdA,IdB,y,Overlap), % B overlap A

true.

qualitative constraints2
Qualitative Constraints
  • Reasoning
    • Inverse:

edge(A,B,D,Value) <=> inverse(Value,RValue),edge(B,A,D,RValue).

    • Equality

edge(A,B,D,V1), edge(A,B,D,V2) => V1 #= V2

    • Transitive

edge(A,B,D,VAB), edge(B,C,D,VBC) =>

tr(VAB,VBC,VAC), % rule generation algorithm

edge(A,C,D,VAC).

  • Translation rules to quantitative domain

edge(A,B,D,b) => node(A,D/2,V2), node(B,D/1,V1)

V1 #< V2.

problems in generating multimedia
Problems in generating multimedia
  • Text documents are flexible
    • Add page, scrollbar,
    • Template models
    • Wrap text around images
  • Multimedia documents are less flexible
    • No pages or scrollbars, no line-breaking or hyphenation
    • Not based on text-flow
    • Feedback needed
  • Linear process model does not work for multimedia
quantitative constraints1
Quantitative Constraints
  • Example:
    • Two images, A,B
    • A left-of B
    • A top-align B
cuypers generation engine1
Cuypers generation engine
  • Rhetoric/Semantic
    • Sequence, Example
  • Communicative devices
    • Bookshelf, Slideshow
  • Qualitative Constraints
    • A before B
  • Quantitative Constraints
    • A.X2 < B.X1
  • Presentation
    • SMIL