preference handling in relational query languages
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The need for handling preferences arises, e.g., in design of autonomous systems that make choices generated by the environment where they act (context). This problem is addressed by representing the context as a database (DB) instance and a proposal of a fully declarative language capable of encoding various kinds of preferences studied in AI. Such preferences may order some pairs of choices nondeterministically, they may be extrinsic (when a dominance relationship between two choices depends also on other choices), and also context-dependent. The selection of most desirable choices can be augmented by other mandatory requirements encoded as a DB query that takes the DB instance as input. Semantics is well-defined even for conflicting preferences as it is based on the principle known in AI as minimal logic of preferences and on non-monotonic reasoning mechanism yielding a non-empty set of preference models. This set has a compact representation that can be encoded as a tractable disjunctive datalog program with optimal model semantics and exploited to denote most desirable choices as a DB query. The presented approach is flexible and promising in formulating policies to improve and automate preference based decision making in complex and dynamic contexts.TRANSCRIPT
Situation The Problem The Solution Contributions
Preference Handling in Relational QueryLanguages
Radim Nedbal
Czech Technical University in Prague,Fakulty of Nuclear Sciences and Physical Engineering
Prague, 7th October 2011
1,/,30
Situation The Problem The Solution Contributions
Preference Handling in Relational QueryLanguages
Radim Nedbal
Czech Technical University in Prague,Fakulty of Nuclear Sciences and Physical Engineering
Prague, 7th October 2011
1,/,30
Situation The Problem The Solution Contributions
Contents
1 SituationAutonomous systems should make desirable choicesDesirable choices can be intensionally denoted in a RQL
2 The ProblemDesirable feasible choices can’t be denoted in a RQLManual selection is opaque to the system
3 The SolutionA declarative language for preferences conditional on thecontext represented as a relational DB instanceSpecifying and interpreting preferencesRetrieving the most desirable choices
4 ContributionsSummary and conclusionsRelated work
2,/,30
Situation The Problem The Solution Contributions
Contents
1 SituationAutonomous systems should make desirable choicesDesirable choices can be intensionally denoted in a RQL
2 The ProblemDesirable feasible choices can’t be denoted in a RQLManual selection is opaque to the system
3 The SolutionA declarative language for preferences conditional on thecontext represented as a relational DB instanceSpecifying and interpreting preferencesRetrieving the most desirable choices
4 ContributionsSummary and conclusionsRelated work
2,/,30
Situation The Problem The Solution Contributions
Contents
1 SituationAutonomous systems should make desirable choicesDesirable choices can be intensionally denoted in a RQL
2 The ProblemDesirable feasible choices can’t be denoted in a RQLManual selection is opaque to the system
3 The SolutionA declarative language for preferences conditional on thecontext represented as a relational DB instanceSpecifying and interpreting preferencesRetrieving the most desirable choices
4 ContributionsSummary and conclusionsRelated work
2,/,30
Situation The Problem The Solution Contributions
Contents
1 SituationAutonomous systems should make desirable choicesDesirable choices can be intensionally denoted in a RQL
2 The ProblemDesirable feasible choices can’t be denoted in a RQLManual selection is opaque to the system
3 The SolutionA declarative language for preferences conditional on thecontext represented as a relational DB instanceSpecifying and interpreting preferencesRetrieving the most desirable choices
4 ContributionsSummary and conclusionsRelated work
2,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
Complex autonomous systems (CAS)
CAS
Environment
Perc
epts
CAS Decision most desirable
actions
A framework for selecting the most d e s i r a b l ef e a s i b l e c h o i c e s at run-time
declarative specification of (designer’s) d e s i r e s,amenable to customization
by allowing specification of additional d e s i r e s,by providing additional information about the c o n t e x t.
3,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
Complex autonomous systems (CAS)
CAS
Environment
Perc
epts
CAS Decision most desirable
actions
A framework for selecting the most d e s i r a b l ef e a s i b l e c h o i c e s at run-time
declarative specification of (designer’s) d e s i r e s,amenable to customization
by allowing specification of additional d e s i r e s,by providing additional information about the c o n t e x t.
3,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
Complex autonomous systems (CAS)
CAS
Environment
Perc
epts
CAS Decision most desirable
actions
A framework for selecting the most d e s i r a b l ef e a s i b l e c h o i c e s at run-time
declarative specification of (designer’s) d e s i r e s,amenable to customization
by allowing specification of additional d e s i r e s,by providing additional information about the c o n t e x t.
3,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
Complex autonomous systems (CAS)
CAS
Environment
Perc
epts
CAS Decision most desirable
actions
A framework for selecting the most d e s i r a b l ef e a s i b l e c h o i c e s at run-time
declarative specification of (designer’s) d e s i r e s,amenable to customization
by allowing specification of additional d e s i r e s,by providing additional information about the c o n t e x t.
3,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR. MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 0.2 0A2 A N 0.2 0A3 A N 1 1A4 A N 1 1A5 A Y 0.3 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR.mA s1
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 0.2 0A2 A N 0.2 0A3 A N 1 1A4 A N 1 1A5 A Y 0.3 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR.mA s1A3 s2A4 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 0.2 0A2 A N 0.2 0A3 A N 1 1A4 A N 1 1A5 A Y 0.3 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR.mA s1A3 s2A4 s2A5 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 0.2 0A2 A N 0.2 0A3 A N 1 1A4 A N 1 1A5 A Y 0.3 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR. MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 0 0A2 A N 0 0A3 A N 0 1A4 A N 0 1A5 A Y 0 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR.A5 s1A3 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 0 0A2 A N 0 0A3 A N 0 1A4 A N 0 1A5 A Y 0 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR. MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 1 0A2 A N 1 0A3 A N 1 1A4 A N 1 1A5 A Y 1 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR.mA s1
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 1 0A2 A N 1 0A3 A N 1 1A4 A N 1 1A5 A Y 1 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR.mA s1A3 s2A4 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 1 0A2 A N 1 0A3 A N 1 1A4 A N 1 1A5 A Y 1 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Autonomous systems should make choices most desirable at the current context
System configuration & design example
INPUT SCR.mA s1A3 s2A4 s2A1 s2A2 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 1 0A2 A N 1 0A3 A N 1 1A4 A N 1 1A5 A Y 1 0.04
MAP mA
A1 A2A3 A4
A5
4,/,30
Situation The Problem The Solution Contributions
Desirable choices can be intensionally denoted by their properties in a RQL
DB of feasible choices
INPUT SCR.mA s1
......
MAP ROOM
mA AmB B
mC C...
...
CAMERA ROOM IR LIT GATE...
......
......
A4 A N 1 1A5 A Y 0.3 0.04B1 B N 0 1...
......
......
Maps of rooms where some non-IR cameras shoot a lit areaR(xmap, s1)←− S(xmap, xroom) ∧ T (xcamera, xroom, “N”,1, xgate)
R( mA, s1)←− S( mA , A ) ∧ T ( A4 , A , “N”,1, 1 )
A DB query1 is system interpretable specification of desirable choices,2 can be re-evaluated when DB changes.
5,/,30
Situation The Problem The Solution Contributions
Desirable choices can be intensionally denoted by their properties in a RQL
DB of feasible choices
INPUT SCR.mA s1
......
MAP ROOM
mA AmB B
mC C...
...
CAMERA ROOM IR LIT GATE...
......
......
A4 A N 1 1A5 A Y 0.3 0.04B1 B N 0 1...
......
......
Maps of rooms where some non-IR cameras shoot a lit areaR(xmap, s1)←− S(xmap, xroom) ∧ T (xcamera, xroom, “N”,1, xgate)
R( mA, s1)←− S( mA , A ) ∧ T ( A4 , A , “N”,1, 1 )
A DB query1 is system interpretable specification of desirable choices,2 can be re-evaluated when DB changes.
5,/,30
Situation The Problem The Solution Contributions
Desirable choices can be intensionally denoted by their properties in a RQL
A DB query specifies desirable characteristics
Non-IR cameras shooting a lit gate area.
ans(xcamera)←− T (xcamera, xroom, xIR, xlit, xgate) ∧xIR = “N” ∧ xlit = 1 ∧ xgate = 1 .
T : relation of installed cameras,x IR = “N” : non-IR cameras,
x lit = 1 : cameras shooting a lit area,xgate = 1 : cameras shooting a gate area.
(Most) desirable feasible choices are what matters (most)!
6,/,30
Situation The Problem The Solution Contributions
Desirable choices can be intensionally denoted by their properties in a RQL
A DB query specifies desirable characteristics
Non-IR cameras shooting a lit gate area.
ans(xcamera)←− T (xcamera, xroom, xIR, xlit, xgate) ∧xIR = “N” ∧ xlit = 1 ∧ xgate = 1 .
T : relation of installed cameras,x IR = “N” : non-IR cameras,
x lit = 1 : cameras shooting a lit area,xgate = 1 : cameras shooting a gate area.
(Most) desirable feasible choices are what matters (most)!
6,/,30
Situation The Problem The Solution Contributions
Desirable choices can be intensionally denoted by their properties in a RQL
A DB query specifies desirable characteristics
Non-IR cameras shooting a lit gate area.
ans(xcamera)←− T (xcamera, xroom, xIR, xlit, xgate) ∧xIR = “N” ∧ xlit = 1 ∧ xgate = 1 .
T : relation of installed cameras,x IR = “N” : non-IR cameras,
x lit = 1 : cameras shooting a lit area,xgate = 1 : cameras shooting a gate area.
(Most) desirable feasible choices are what matters (most)!
6,/,30
Situation The Problem The Solution Contributions
Desirable choices can be intensionally denoted by their properties in a RQL
A DB query specifies desirable characteristics
Non-IR cameras shooting a lit gate area.
ans(xcamera)←− T (xcamera, xroom, xIR, xlit, xgate) ∧xIR = “N” ∧ xlit = 1 ∧ xgate = 1 .
T : relation of installed cameras,x IR = “N” : non-IR cameras,
x lit = 1 : cameras shooting a lit area,xgate = 1 : cameras shooting a gate area.
(Most) desirable feasible choices are what matters (most)!
6,/,30
Situation The Problem The Solution Contributions
Desirable choices can be intensionally denoted by their properties in a RQL
A DB query specifies desirable characteristics
Non-IR cameras shooting a lit gate area.
ans(xcamera)←− T (xcamera, xroom, xIR, xlit, xgate) ∧xIR = “N” ∧ xlit = 1 ∧ xgate = 1 .
T : relation of installed cameras,x IR = “N” : non-IR cameras,
x lit = 1 : cameras shooting a lit area,xgate = 1 : cameras shooting a gate area.
(Most) desirable feasible choices are what matters (most)!
6,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Little knowledge to specify characteristics of feasible choices
Asking too specifically 99K empty result effect.(Satisfiability of DB queries is undecidable)
+ Adjust characteristics or give up!Asking for too little 99K flooding effect.
+ Manual selection!
Gradual adjusting original characteristics
+ Add or remove characteristics!
+ Relax or tighten up characteristics!
b expensive as space of characteristics is combinatorially huge!b infeasible in the case of automated decision making
(autonomous agents)!!7,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Little knowledge to specify characteristics of feasible choices
Asking too specifically 99K empty result effect.(Satisfiability of DB queries is undecidable)
+ Adjust characteristics or give up!Asking for too little 99K flooding effect.
+ Manual selection!
Gradual adjusting original characteristics
+ Add or remove characteristics!
+ Relax or tighten up characteristics!
b expensive as space of characteristics is combinatorially huge!b infeasible in the case of automated decision making
(autonomous agents)!!7,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Little knowledge to specify characteristics of feasible choices
Asking too specifically 99K empty result effect.(Satisfiability of DB queries is undecidable)
+ Adjust characteristics or give up!Asking for too little 99K flooding effect.
+ Manual selection!
Gradual adjusting original characteristics
+ Add or remove characteristics!
+ Relax or tighten up characteristics!
b expensive as space of characteristics is combinatorially huge!b infeasible in the case of automated decision making
(autonomous agents)!!7,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Little knowledge to specify characteristics of feasible choices
Asking too specifically 99K empty result effect.(Satisfiability of DB queries is undecidable)
+ Adjust characteristics or give up!Asking for too little 99K flooding effect.
+ Manual selection!
Gradual adjusting original characteristics
+ Add or remove characteristics!
+ Relax or tighten up characteristics!
b expensive as space of characteristics is combinatorially huge!b infeasible in the case of automated decision making
(autonomous agents)!!7,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Little knowledge to specify characteristics of feasible choices
Asking too specifically 99K empty result effect.(Satisfiability of DB queries is undecidable)
+ Adjust characteristics or give up!Asking for too little 99K flooding effect.
+ Manual selection!
Gradual adjusting original characteristics
+ Add or remove characteristics!
+ Relax or tighten up characteristics!
b expensive as space of characteristics is combinatorially huge!b infeasible in the case of automated decision making
(autonomous agents)!!7,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Little knowledge to specify characteristics of feasible choices
Asking too specifically 99K empty result effect.(Satisfiability of DB queries is undecidable)
+ Adjust characteristics or give up!Asking for too little 99K flooding effect.
+ Manual selection!
Gradual adjusting original characteristics
+ Add or remove characteristics!
+ Relax or tighten up characteristics!
b expensive as space of characteristics is combinatorially huge!b infeasible in the case of automated decision making
(autonomous agents)!!7,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Little knowledge to specify characteristics of feasible choices
Asking too specifically 99K empty result effect.(Satisfiability of DB queries is undecidable)
+ Adjust characteristics or give up!Asking for too little 99K flooding effect.
+ Manual selection!
Gradual adjusting original characteristics
+ Add or remove characteristics!
+ Relax or tighten up characteristics!
b expensive as space of characteristics is combinatorially huge!b infeasible in the case of automated decision making
(autonomous agents)!!7,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Little knowledge to specify characteristics of feasible choices
Asking too specifically 99K empty result effect.(Satisfiability of DB queries is undecidable)
+ Adjust characteristics or give up!Asking for too little 99K flooding effect.
+ Manual selection!
Gradual adjusting original characteristics
+ Add or remove characteristics!
+ Relax or tighten up characteristics!
b expensive as space of characteristics is combinatorially huge!b infeasible in the case of automated decision making
(autonomous agents)!!7,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Gradual adjusting characteristics
Non-IR cameras shooting a lit gate area.
x IR = “N” : non-IR cameras,x lit = 1 : cameras shooting a lit area,
xgate = 1 : cameras shooting a gate area.
x IR = “N” ∧ x lit = 1 ∧ xgate = 1
x IR = “N” ∧ x lit = 1x IR = “N” ∧ xgate = 1
x lit = 1 ∧ xgate = 1
x IR = “N” x lit = 1 xgate = 1
8,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Gradual adjusting characteristics
Non-IR cameras shooting a lit gate area.
x IR = “N” : non-IR cameras,x lit = 1 : cameras shooting a lit area,
xgate = 1 : cameras shooting a gate area.
x IR = “N” ∧ x lit = 1 ∧ xgate = 1
x IR = “N” ∧ x lit = 1x IR = “N” ∧ xgate = 1
x lit = 1 ∧ xgate = 1
x IR = “N” x lit = 1 xgate = 1
8,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Gradual adjusting characteristics
Non-IR cameras shooting a lit gate area.
x IR = “N” : non-IR cameras,x lit = 1 : cameras shooting a lit area,
xgate = 1 : cameras shooting a gate area.
x IR = “N” ∧ x lit = 1 ∧ xgate = 1
x IR = “N” ∧ x lit = 1x IR = “N” ∧ xgate = 1
x lit = 1 ∧ xgate = 1
x IR = “N” x lit = 1 xgate = 1
8,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Gradual adjusting characteristics
Non-IR cameras shooting a lit gate area.
x IR = “N” : non-IR cameras,x lit = 1 : cameras shooting a lit area,
xgate = 1 : cameras shooting a gate area.
x IR = “N” ∧ x lit = 1 ∧ xgate = 1
x IR = “N” ∧ x lit = 1x IR = “N” ∧ xgate = 1
x lit = 1 ∧ xgate = 1
x IR = “N” x lit = 1 xgate = 1
8,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Gradual adjusting characteristics
Non-IR cameras shooting a lit gate area.
x IR = “N” : non-IR cameras,x lit = 1 : cameras shooting a lit area,
xgate = 1 : cameras shooting a gate area.
x IR = “N” ∧ x lit = 1 ∧ xgate = 1
x IR = “N” ∧ x lit = 1x IR = “N” ∧ xgate = 1
x lit = 1 ∧ xgate = 1
x IR = “N” x lit = 1 xgate = 1
8,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Gradual adjusting characteristics
Non-IR cameras shooting a lit gate area.
x IR = “N” : non-IR cameras,x lit = 1 : cameras shooting a lit area,
xgate = 1 : cameras shooting a gate area.
x IR = “N” ∧ x lit = 1 ∧ xgate = 1
x IR = “N” ∧ x lit = 1x IR = “N” ∧ xgate = 1
x lit = 1 ∧ xgate = 1
x IR = “N” x lit = 1 xgate = 1
8,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Gradual adjusting characteristics
Non-IR cameras shooting a lit gate area.
x IR = “N” : non-IR cameras,x lit = 1 : cameras shooting a lit area,
xgate = 1 : cameras shooting a gate area.
x IR = “N” ∧ x lit = 1 ∧ xgate = 1
x IR = “N” ∧ x lit = 1x IR = “N” ∧ xgate = 1
x lit = 1 ∧ xgate = 1
x IR = “N” x lit = 1 xgate = 1
8,/,30
Situation The Problem The Solution Contributions
(Most) desirable feasible choices can’t be intensionally denoted by their properties in a RQL
Gradual adjusting characteristics
Non-IR cameras shooting a lit gate area.
x IR = “N” : non-IR cameras,x lit = 1 : cameras shooting a lit area,
xgate = 1 : cameras shooting a gate area.
x IR = “N” ∧ x lit = 1 ∧ xgate = 1
x IR = “N” ∧ x lit = 1x IR = “N” ∧ xgate = 1
x lit = 1 ∧ xgate = 1
x IR = “N” x lit = 1 xgate = 1
8,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
Reasons behind manual selection of adjustingare opaque to the system,are someone’s “liking of one thing more than another,” i.e.,various desirability of respective answers,are what we term preferences.
Preferences are wishes!No perfect match?? 99K worse alternatives.A paradigm shift
from exact matches towards a best possible match-making,from h a r d c o n s t r a i n t s to s o f t c o n s t r a i n t s.
The main goal of the thesisa general framework for incorporating preferences in RQLto support the user-friendly design of autonomous systems thatcan act in dynamic environment.
9,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
Reasons behind manual selection of adjustingare opaque to the system,are someone’s “liking of one thing more than another,”i.e., various desirability of respective answers,are what we term preferences.
Preferences are wishes!No perfect match?? 99K worse alternatives.A paradigm shift
from exact matches towards a best possible match-making,from h a r d c o n s t r a i n t s to s o f t c o n s t r a i n t s.
The main goal of the thesisa general framework for incorporating preferences in RQLto support the user-friendly design of autonomous systems thatcan act in dynamic environment.
9,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
Reasons behind manual selection of adjustingare opaque to the system,are someone’s “liking of one thing more than another,”i.e., various desirability of respective answers,are what we term preferences.
Preferences are wishes!No perfect match?? 99K worse alternatives.A paradigm shift
from exact matches towards a best possible match-making,from h a r d c o n s t r a i n t s to s o f t c o n s t r a i n t s.
The main goal of the thesisa general framework for incorporating preferences in RQLto support the user-friendly design of autonomous systems thatcan act in dynamic environment.
9,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
þ
Requirements
J
qRQL
q(J)Preferences
P
Manual designation
q∗(J)
10,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
þ
Requirements
J
qRQL
q(J)Preferences
P
Manual designation
q∗(J)
10,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
þ
Requirements
J
qRQL
q(J)
PreferencesP
Manual designation
q∗(J)
10,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
þ
Requirements
J
qRQL
q(J)Preferences
Manual designation
q∗(J)
10,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
þ
Requirements
J
qRQL
q(J)Preferences
P
Manual designation
Requirements,preferences
q∗(J)
10,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
þ
Requirements
J
qRQL
q(J)Preferences
P
Manual designation
Requirements,preferences
q∗
RQL+
q∗(J)
10,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
þ
Requirements
J
qRQL
q(J)Preferences
P
Manual designation
Requirements,preferences
q∗
RQL+
q∗(J)
10,/,30
Situation The Problem The Solution Contributions
Manual selection or adjusting characteristics of desired choices is opaque to the system
Back to MM
Back to Representationþ
Requirements
J
qRQL
q(J)Preferences
P
Manual designation
q∗(J)
10,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
Partial pre-orders Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
Partial pre-orders Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
Partial pre-orders Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
Partial pre-orders Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
Partial pre-orders Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
Partial pre-orders Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
Partial pre-orders Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
A nonempty setof distinguished
preference models
Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
A nonempty setof distinguished
preference models
Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
A nonempty setof distinguished
preference models
Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
A nonempty setof distinguished
preference models
Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
A declarative language for preferences conditional on the current state of the world represented as a relational DB instance
Concretization of the basic concepts To J, q,P
Models Language Algorithms
Query
Interpretation Representation
Data model
RDM The most desirable choices
A nonempty setof distinguished
preference models
A nonempty setof distinguished
preference models
Heterogenous andpossibly conflicting
preference formulae of LP
Non-monotonic reasoningSubmodels of distinguished
preference models
DDP and DBS
11,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Models Back to the meta-model
are structures that capture properties of specified preferences
Preference model 〈Ω,〉is a partial pre-orderover a set Ω of a c c e p t a b l e f e a s i b l e choice.
reflexive, transitive, partial.
WALKING
SUBWAY
TAXI
WALKING
TAXI WALKING
SUBWAY
TAXI WALKING
SUBWAY
TAXI
?
?
?
b Ω is abstracted as q(J);b w w ′ (w w ′) reads: “w is (strictly) preferred to w ′.”
12,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Models Back to the meta-model
are structures that capture properties of specified preferences
Preference model 〈Ω,〉is a partial pre-orderover a set Ω of a c c e p t a b l e f e a s i b l e choice.
reflexive, transitive, partial.
WALKING
SUBWAY
TAXI
WALKING
TAXI WALKING
SUBWAY
TAXI WALKING
SUBWAY
TAXI
?
?
?
b Ω is abstracted as q(J);b w w ′ (w w ′) reads: “w is (strictly) preferred to w ′.”
12,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Models Back to the meta-model
are structures that capture properties of specified preferences
Preference model 〈Ω,〉is a partial pre-orderover a set Ω of a c c e p t a b l e f e a s i b l e choice.
reflexive, transitive, partial.
WALKING
SUBWAY
TAXI
WALKING
TAXI WALKING
SUBWAY
TAXI WALKING
SUBWAY
TAXI
?
?
?
b Ω is abstracted as q(J);b w w ′ (w w ′) reads: “w is (strictly) preferred to w ′.”
12,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Models Back to the meta-model
are structures that capture properties of specified preferences
Preference model 〈Ω,〉is a partial pre-orderover a set Ω of a c c e p t a b l e f e a s i b l e choice.
reflexive, transitive, partial.
WALKING
SUBWAY
TAXI
WALKING
TAXI WALKING
SUBWAY
TAXI WALKING
SUBWAY
TAXI
?
?
?
b Ω is abstracted as q(J);b w w ′ (w w ′) reads: “w is (strictly) preferred to w ′.”
12,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Models Back to the meta-model
are structures that capture properties of specified preferences
Preference model 〈Ω,〉is a partial pre-orderover a set Ω of a c c e p t a b l e f e a s i b l e choice.
reflexive, transitive, partial.
WALKING
SUBWAY
TAXI
WALKING
TAXI WALKING
SUBWAY
TAXI WALKING
SUBWAY
TAXI
?
?
?
b Ω is abstracted as q(J);b w w ′ (w w ′) reads: “w is (strictly) preferred to w ′.”
12,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Models Back to the meta-model
are structures that capture properties of specified preferences
Preference model 〈Ω,〉is a partial pre-orderover a set Ω of a c c e p t a b l e f e a s i b l e choice.
reflexive, transitive, partial.
WALKING
SUBWAY
TAXI
WALKING
TAXI WALKING
SUBWAY
TAXI WALKING
SUBWAY
TAXI
?
?
?
b Ω is abstracted as q(J);b w w ′ (w w ′) reads: “w is (strictly) preferred to w ′.”
12,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
13,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)
ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
13,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
13,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
13,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
13,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
13,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
13,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Interpretation Back to the meta-model
gives exact meaning to preference formulae
P = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
14,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Interpretation Back to the meta-model
gives exact meaning to preference formulae
P = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
14,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Interpretation Back to the meta-model
gives exact meaning to preference formulae
P = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
14,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Interpretation Back to the meta-model
gives exact meaning to preference formulae
P = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
14,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Interpretation Back to the meta-model
gives exact meaning to preference formulae
P = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ¬ϕ ∧ ψ ∧ ω
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
14,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Interpretation Back to the meta-model
gives exact meaning to preference formulae
P = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ¬ϕ ∧ ψ ∧ ω
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
14,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Interpretation Back to the meta-model
gives exact meaning to preference formulae
P = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
14,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Interpretation Back to the meta-model
gives exact meaning to preference formulae
P = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
14,/,30
Situation The Problem The Solution Contributions
Specifying and interpreting preferences
Interpretation Back to the meta-model
gives exact meaning to preference formulae
P = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
14,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Representation Back to the meta-model
captures preference formulae in a framework suitable for algorithms
q(J)
q′(J)
ψ(J)
ϕ(J)
ω(J)
Due to Theorem 3, we can find q′(J),
q′(J) ⊆ q(J) ,
so thatthe set of DPMs with underlying set q′(J)determinesthe set of DPMs with underlying set q(J)
Any P can be represented compactly: To J, q,P
the set ofd i s t i n g u i s h e d p r e f e r e n c e m o d e l s,
+ defining the meaning of P
can be represented asthe set of t h e i r s u b m o d e l s.
15,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Representation Back to the meta-model
captures preference formulae in a framework suitable for algorithms
q(J)
q′(J)ψ(J)
ϕ(J)
ω(J)
Due to Theorem 3, we can find q′(J),
q′(J) ⊆ q(J) ,
so thatthe set of DPMs with underlying set q′(J)determinesthe set of DPMs with underlying set q(J)
Any P can be represented compactly: To J, q,P
the set ofd i s t i n g u i s h e d p r e f e r e n c e m o d e l s,
+ defining the meaning of P
can be represented asthe set of t h e i r s u b m o d e l s.
15,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Representation Back to the meta-model
captures preference formulae in a framework suitable for algorithms
q(J)
q′(J)
ψ(J)
ϕ(J)
ω(J)
Due to Theorem 3, we can find q′(J),
q′(J) ⊆ q(J) ,
so thatthe set of DPMs with underlying set q′(J)determinesthe set of DPMs with underlying set q(J)
Any P can be represented compactly: To J, q,P
the set ofd i s t i n g u i s h e d p r e f e r e n c e m o d e l s,
+ defining the meaning of P
can be represented asthe set of t h e i r s u b m o d e l s.
15,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Representation Back to the meta-model
captures preference formulae in a framework suitable for algorithms
q(J)
q′(J)ψ(J)
ϕ(J)
ω(J)
Due to Theorem 3, we can find q′(J),
q′(J) ⊆ q(J) ,
so thatthe set of DPMs with underlying set q′(J)determinesthe set of DPMs with underlying set q(J)
Any P can be represented compactly: To J, q,P
the set ofd i s t i n g u i s h e d p r e f e r e n c e m o d e l s,
+ defining the meaning of P
can be represented asthe set of t h e i r s u b m o d e l s.
15,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Representation Back to the meta-model
captures preference formulae in a framework suitable for algorithms
q(J)
q′(J)ψ(J)
ϕ(J)
ω(J)
Due to Theorem 3, we can find q′(J),
q′(J) ⊆ q(J) ,
so thatthe set of DPMs with underlying set q′(J)determinesthe set of DPMs with underlying set q(J)
Any P can be represented compactly: To J, q,P
the set ofd i s t i n g u i s h e d p r e f e r e n c e m o d e l s,
+ defining the meaning of P
can be represented asthe set of t h e i r s u b m o d e l s.
15,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Representation Back to the meta-model
captures preference formulae in a framework suitable for algorithms
q(J)
q′(J)
ψ(J)
ϕ(J)
ω(J)
Due to Theorem 3, we can find q′(J),
q′(J) ⊆ q(J) ,
so thatthe set of DPMs with underlying set q′(J)determinesthe set of DPMs with underlying set q(J)
Any P can be represented compactly: To J, q,P
the set ofd i s t i n g u i s h e d p r e f e r e n c e m o d e l s,
+ defining the meaning of P
can be represented asthe set of t h e i r s u b m o d e l s.
15,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Representation Back to the meta-model
captures preference formulae in a framework suitable for algorithms
q(J)
q′(J)
ψ(J)
ϕ(J)
ω(J)
Due to Theorem 3, we can find q′(J),
q′(J) ⊆ q(J) ,
so thatthe set of DPMs with underlying set q′(J)determinesthe set of DPMs with underlying set q(J)
Any P can be represented compactly: To J, q,P
the set ofd i s t i n g u i s h e d p r e f e r e n c e m o d e l s,
+ defining the meaning of P
can be represented asthe set of t h e i r s u b m o d e l s.
15,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
16,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
16,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
16,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)
ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
16,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)
ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
16,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)
ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
16,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
16,/,30
Situation The Problem The Solution Contributions
Retrieving the most desirable choices
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)
ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
16,/,30
Situation The Problem The Solution Contributions
Summary and conclusions
The proposed frameworkis general enough to have wide applicability
A novel, flexible approach based on the language capableof encoding qualitative comparative preference statements
1 that may be of various kinds2 that may be nondeterministic;3 that may be context sensitive4 that may be augmented by mandatory requirements.
is suitable for control of dynamic systems, where both thestate and number of objects changes.
A camera stream of a gate area is always desirable.+ .. an arbitrary such a camera.
Streams from non-IR cameras shooting a lit area are moredesirable than streams from IR cameras.+ .. currently lit areas wrt. the updated DB.
17,/,30
Situation The Problem The Solution Contributions
Summary and conclusions
The proposed frameworkis general enough to have wide applicability
A novel, flexible approach based on the language capableof encoding qualitative comparative preference statements
1 that may be of various kinds2 that may be nondeterministic;3 that may be context sensitive4 that may be augmented by mandatory requirements.
is suitable for control of dynamic systems, where both thestate and number of objects changes.
A camera stream of a gate area is always desirable.+ .. an arbitrary such a camera.
Streams from non-IR cameras shooting a lit area are moredesirable than streams from IR cameras.+ .. currently lit areas wrt. the updated DB.
17,/,30
Situation The Problem The Solution Contributions
Summary and conclusions
The proposed frameworkis general enough to have wide applicability
A novel, flexible approach based on the language capableof encoding qualitative comparative preference statements
1 that may be of various kinds2 that may be nondeterministic;3 that may be context sensitive4 that may be augmented by mandatory requirements.
is suitable for control of dynamic systems, where both thestate and number of objects changes.
A camera stream of a gate area is always desirable.+ .. an arbitrary such a camera.
Streams from non-IR cameras shooting a lit area are moredesirable than streams from IR cameras.+ .. currently lit areas wrt. the updated DB.
17,/,30
Situation The Problem The Solution Contributions
Summary and conclusions
The proposed frameworkis general enough to have wide applicability
A novel, flexible approach based on the language capableof encoding qualitative comparative preference statements
1 that may be of various kinds2 that may be nondeterministic;3 that may be context sensitive4 that may be augmented by mandatory requirements.
is suitable for control of dynamic systems, where both thestate and number of objects changes.
A camera stream of a gate area is always desirable.+ .. an arbitrary such a camera.
Streams from non-IR cameras shooting a lit area are moredesirable than streams from IR cameras.+ .. currently lit areas wrt. the updated DB.
17,/,30
Situation The Problem The Solution Contributions
Summary and conclusions
The proposed frameworkis general enough to have wide applicability
A novel, flexible approach based on the language capableof encoding qualitative comparative preference statements
1 that may be of various kinds2 that may be nondeterministic;3 that may be context sensitive4 that may be augmented by mandatory requirements.
is suitable for control of dynamic systems, where both thestate and number of objects changes.
A camera stream of a gate area is always desirable.+ .. an arbitrary such a camera.
Streams from non-IR cameras shooting a lit area are moredesirable than streams from IR cameras.+ .. currently lit areas wrt. the updated DB.
17,/,30
Situation The Problem The Solution Contributions
Summary and conclusions
The proposed frameworkis formal enough to support automated decision making
1 Preferences are embedded in RQLs.2 The empty result effect is eliminated:
+ any preference specification has a DPMTheorem 1(totality of interpretation).
3 Constructive semantics is based on a compactrepresentation (Theorem 3).
from which DPMs can be inferred;which can be encoded as a DDP.
+ We exploit DDP machinery (Algorithm 1, Theorem 4)to compute DPMs.
4 MDC are denoted as a DB query (Theorem 5)and retrieved from the DB, exploiting standard DBoptimization strategies.
18,/,30
Situation The Problem The Solution Contributions
Summary and conclusions
The proposed frameworkis formal enough to support automated decision making
1 Preferences are embedded in RQLs.2 The empty result effect is eliminated:
+ any preference specification has a DPMTheorem 1(totality of interpretation).
3 Constructive semantics is based on a compactrepresentation (Theorem 3).
from which DPMs can be inferred;which can be encoded as a DDP.
+ We exploit DDP machinery (Algorithm 1, Theorem 4)to compute DPMs.
4 MDC are denoted as a DB query (Theorem 5)and retrieved from the DB, exploiting standard DBoptimization strategies.
18,/,30
Situation The Problem The Solution Contributions
Summary and conclusions
The proposed frameworkis formal enough to support automated decision making
1 Preferences are embedded in RQLs.2 The empty result effect is eliminated:
+ any preference specification has a DPMTheorem 1(totality of interpretation).
3 Constructive semantics is based on a compactrepresentation (Theorem 3).
from which DPMs can be inferred;which can be encoded as a DDP.
+ We exploit DDP machinery (Algorithm 1, Theorem 4)to compute DPMs.
4 MDC are denoted as a DB query (Theorem 5)and retrieved from the DB, exploiting standard DBoptimization strategies.
18,/,30
Situation The Problem The Solution Contributions
Summary and conclusions
The proposed frameworkis formal enough to support automated decision making
1 Preferences are embedded in RQLs.2 The empty result effect is eliminated:
+ any preference specification has a DPMTheorem 1(totality of interpretation).
3 Constructive semantics is based on a compactrepresentation (Theorem 3).
from which DPMs can be inferred;which can be encoded as a DDP.
+ We exploit DDP machinery (Algorithm 1, Theorem 4)to compute DPMs.
4 MDC are denoted as a DB query (Theorem 5)and retrieved from the DB, exploiting standard DBoptimization strategies.
18,/,30
Situation The Problem The Solution Contributions
Related work
Influential paper, projects, and figures
M. Lacroix and Pierre Lavency.Preferences: Putting More Knowledge into Queries.VLDB, 1987.
1999 –(8 projects)
It’s a Preference WorldUniversity of Augsburg
Germany
WernerKießling
2003 –Preference QueriesUniversity at Buffalo
USA
JanChomicki
? –Command & ControlBen-Gurion University
Beer-Sheva, Israel
Ronen I.Brafman
19,/,30
Situation The Problem The Solution Contributions
Related work
Comparison to related work
Preference model Language Interpretation
Pre
-ord
er
Tota
lpre
-ord
er
Str
icto
rder
Tota
lord
er
Granularity
Ext
rinsi
city
Context
Tota
litar
ian
Cet
eris
parib
us
Non
dete
rmin
ist.
Tupl
e
Set
oftu
ples
Attr
ibut
e
Con
text
free
Inte
rnal
Ext
erna
l
Lacroix and Lavency 1987 X X X X XKießling 2002 X X X XChomicki 2002; 2003 X X (X) X X XHolland and Kießling 2004 X X X XBrafman and Domshlak 2004 X X X X XAgrawal et al. 2006 X X X XEndres and Kießling 2006 X X X X XCiaccia 2007 X X X XMindolin and Chomicki 2007 X X X X XGeorgiadis et al. 2008 X X X X XKaci and Neves 2010 X X X X X XZhang and Chomicki 2011 X X X XThe presented approach X X X X X X X X
20,/,30
Appendix
System configuration & design example
INPUT SCR.mA s1A3 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 0.2 0A2 A N 0.2 0A3 A N 1 1A4 A N 1 1A5 A Y 0.3 0.04
MAP mA
A1 A2A3 A4
A5
23,/,30
Appendix
System configuration & design example
INPUT SCR.mA s1A3 s2A4 s2A1 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 1 0A2 A N 1 0A3 A N 1 1A4 A N 1 1A5 A Y 1 0.04
MAP mA
A1 A2A3 A4
A5
23,/,30
Appendix
System configuration & design example
INPUT SCR.mA s1A3 s2A4 s2A2 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 1 0A2 A N 1 0A3 A N 1 1A4 A N 1 1A5 A Y 1 0.04
MAP mA
A1 A2A3 A4
A5
23,/,30
Appendix
System configuration & design example
INPUT SCR.mA s1A4 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 0.2 0A2 A N 0.2 0A3 A N 1 1A4 A N 1 1A5 A Y 0.3 0.04
MAP mA
A1 A2A3 A4
A5
23,/,30
Appendix
System configuration & design example
INPUT SCR.mA s1A3 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 1 0A2 A N 1 0A3 A N 1 1A4 A N 1 1A5 A Y 1 0.04
MAP mA
A1 A2A3 A4
A5
23,/,30
Appendix
System configuration & design example
INPUT SCR.mA s1A4 s2
MAP ROOM
mA A
mB B
mC C...
...
CAMERA ROOM IR LIT GATE
A1 A N 1 0A2 A N 1 0A3 A N 1 1A4 A N 1 1A5 A Y 1 0.04
MAP mA
A1 A2A3 A4
A5
23,/,30
Appendix
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
24,/,30
Appendix
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
24,/,30
Appendix
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
24,/,30
Appendix
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
24,/,30
Appendix
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
24,/,30
Appendix
Language Back to the meta-model
encodes preferences by specifying models
Language of preference formulae LP
ϕB ψ is a preference formula (of LP) iffϕ,ψ are DB queries “of the same type,”B is represents a recognized kind of a preference.
ϕ1m>M ψ ,
ϕ2M>M ψ ,
ϕ3m>m ψ ,
ϕ4M>m ψ ,
P .
q(J)ψ(J)
ϕ1(J)
ϕ2(J)
ϕ3(J)
ϕ4(J)
24,/,30
Appendix
Interpretation Back to the meta-model
gives exact meaning to preference formulaeP = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
25,/,30
Appendix
Interpretation Back to the meta-model
gives exact meaning to preference formulaeP = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
25,/,30
Appendix
Interpretation Back to the meta-model
gives exact meaning to preference formulaeP = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
25,/,30
Appendix
Interpretation Back to the meta-model
gives exact meaning to preference formulaeP = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
25,/,30
Appendix
Interpretation Back to the meta-model
gives exact meaning to preference formulaeP = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
25,/,30
Appendix
Interpretation Back to the meta-model
gives exact meaning to preference formulaeP = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
25,/,30
Appendix
Interpretation Back to the meta-model
gives exact meaning to preference formulaeP = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
25,/,30
Appendix
Interpretation Back to the meta-model
gives exact meaning to preference formulaeP = ϕ m>M ψ ,ψ m>M ω ϕ ∧ ψ ∧ ¬ω ? ϕ ∧ ¬ψ
q(J)
ψ(J)
ϕ(J)
ω(J)
q(J)
ψ(J)
ϕ(J)
ω(J)
1 Minimal logic of preference:+ w is as good as w ′ iff allowed by P
+ each P is satisfied by one or more models!2 Non-monotonic reasoning mechanism: yields DPMs.
25,/,30
Appendix
Representation Back to the meta-model
captures preference formulae in a framework suitable for algorithms
q(J)
q′(J)ψ(J)
ϕ(J)
ω(J)
Due to Theorem 3, we can find q′(J),
q′(J) ⊆ q(J) ,
so thatthe set of DPMs with underlying set q′(J)determinesthe set of DPMs with underlying set q(J)
Any P can be represented compactly: To J, q,P
the set ofd i s t i n g u i s h e d p r e f e r e n c e m o d e l s,
+ defining the meaning of P
can be represented asthe set of t h e i r s u b m o d e l s.
26,/,30
Appendix
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)
ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
27,/,30
Appendix
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)
ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
27,/,30
Appendix
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)
ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
27,/,30
Appendix
Algorithms To Algorithm 2 Back to the meta-model
MDC w.r.t. J, q, P = ϕ m>M ψ ,ψ m>M ω
ψ(J)
ϕ(J)
ω(J)
q′(J)
ϕ
ψ
ω
ab
c
d e
fg
ab
c
d e
fg
ab
q(J)
ϕ m>M ψ : g b ∧ e b
ψ m>M ω : d a ∧ d g
transitivity:
x z ∧ y ∈ a, . . . ,g → x y ∨ y z
J,q,P DPMsDecl. sem.Theorems 1,2
// DPMs MDC//
Repres.
OO
Theorem 3
DDP
ILORU
Alg.1, Theo.4//
Constr. sem.
II
R-MDC//
XX
RQL
Theorem 5
a 99K
[q∧ϕ∧ψ∧¬ω](J)
b 99K
[q∧ϕ∧¬ψ∧¬ω](J)
27,/,30
Appendix
Algorithm 2 for computation of most preferred matches To algorithms
Require: P,q,J.Ensure: The most preferred tuples wrt. P that fulfill q.
1: Construct UP . . Step I. (see page 68)2: Construct rules of P. . Step II. (see page 72)3: Add rules ensuring transitivity. . Step III. (see page 74)4: Compute O. . Algorithm 1 on page 775: Determine O<.6: Compute SMJ
P
(O<).
7: Compute MX(SMJ
P
(O<))
.8: Translate q together with elements from MX
(SMJ
P
(O<))
into a RQL formula q′.9: Evaluate q′(J) – the most preferred matches. . DBMS
28,/,30
Appendix
P,Ω,J IP(Ω,J)declarativesemantics
//_______ IP(Ω,J) = UEΩP
(⋃αΩ αΩ= UEΩ
P
(⋃αΩ αΩ
(IP
(Ω,J
)))IP(Ω,J)
MX (IP (Ω,J)) =
MX (IP (Ω,J)) =⋃
wk∈MX(IP(UP ,J)
)Dom(wk )
P,Ω,J
P
1−3
OOOOOOOOOOO
?OOOOOOOOOOO
P O<4,5
constructivesemantics
// O< EMJP
(MODP(UP ,J)
)EMJ
P
(MODP(UP ,J)
)SMJ
P
(EMJ
P
(MODP(UP ,J)
))6
OOSMJ
P
(EMJ
P
(MODP(UP ,J)
))IP (UP ,J)IP (UP ,J)
IP
(Ω,J
)
f
NN
IP (UP ,J)
MX (IP (UP ,J))
7
YYMX (IP (UP ,J))
⋃wk∈MX
(IP(UP ,J)
)Dom(wk )
8,9
OO IP
(Ω,J
)
(IP
(Ω,J
)))]]
29,/,30
Appendix
Influential paper, projects, and figures
M. Lacroix and Pierre Lavency.Preferences: Putting More Knowledge into Queries.VLDB, 1987.
1999 –(8 projects)
It’s a Preference WorldUniversity of Augsburg
Germany
Werner
Kießling
2003 –Preference QueriesUniversity at Buffalo
USA
Jan
Chomicki
? –Command & ControlBen-Gurion University
Beer-Sheva, Israel
Ronen I.
Brafman30,/,30
Appendix
Influential paper, projects, and figures
M. Lacroix and Pierre Lavency.Preferences: Putting More Knowledge into Queries.VLDB, 1987.
1999 –(8 projects)
It’s a Preference WorldUniversity of Augsburg
Germany
Werner
Kießling
2003 –Preference QueriesUniversity at Buffalo
USA
Jan
Chomicki
? –Command & ControlBen-Gurion University
Beer-Sheva, Israel
Ronen I.
Brafman
30,/,30
Appendix
Influential paper, projects, and figures
M. Lacroix and Pierre Lavency.Preferences: Putting More Knowledge into Queries.VLDB, 1987.
1999 –(8 projects)
It’s a Preference WorldUniversity of Augsburg
Germany
Werner
Kießling
2003 –Preference QueriesUniversity at Buffalo
USA
Jan
Chomicki
? –Command & ControlBen-Gurion University
Beer-Sheva, Israel
Ronen I.
Brafman
30,/,30
Appendix
Influential paper, projects, and figures
M. Lacroix and Pierre Lavency.Preferences: Putting More Knowledge into Queries.VLDB, 1987.
1999 –(8 projects)
It’s a Preference WorldUniversity of Augsburg
Germany
Werner
Kießling
2003 –Preference QueriesUniversity at Buffalo
USA
Jan
Chomicki
? –Command & ControlBen-Gurion University
Beer-Sheva, Israel
Ronen I.
Brafman30,/,30