dtproblog - ucla starai lab - newsstarai.cs.ucla.edu/slides/aaai10.pdf · 2019. 1. 13. · 07/14/10...
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![Page 1: DTProbLog - UCLA StarAI Lab - Newsstarai.cs.ucla.edu/slides/AAAI10.pdf · 2019. 1. 13. · 07/14/10 DTProbLog 3 Motivation Combination of relations, uncertainty and decision theory](https://reader036.vdocuments.mx/reader036/viewer/2022071605/6141f0a42035ff3bc76259d6/html5/thumbnails/1.jpg)
DTProbLogA Decision-Theoretic Probabilistic Prolog
Guy Van den BroeckIngo Thon
Martijn van OtterloLuc De Raedt
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07/14/10 DTProbLog 2
Motivation
● Many real-world decision problems are relational and probabilistic
● Wildfire control● Who to vaccinate for swine flu● Viral marketing● ...
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07/14/10 DTProbLog 3
Motivation
● Combination of relations, uncertainty and decision theory largely ignored
some exceptions are MLDNs, DTLPs and FOMDPs
● Relations with uncertainty
= Statistical Relational Learning
● ProbLog is a simple probabilistic Prolog● DTProbLog is a decision-theoretic ProbLog
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07/14/10 DTProbLog 4
Outline
● DTProbLog: the Language● DTProbLog: the Algorithms
● Exact Solution Algorithm● Approximate Solution Algorithms
● Experiments: Viral Marketing● Related Work & Conclusions
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07/14/10 DTProbLog 5
DTProbLog: the Language
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07/14/10 DTProbLog 6
HomerMarge
Bart Lisa
Lenny
Apu
Moe
SeymourRalph
Maggie
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07/14/10 DTProbLog 7
Homer1 Marge
Bart Lisa
Lenny
Apu
Moe
SeymourRalph
Maggie
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07/14/10 DTProbLog 8
Homer1 Marge
0.3
Bart0.3
Lisa
Lenny
Apu
Moe0.3
SeymourRalph
Maggie
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07/14/10 DTProbLog 9
Homer1 Marge
0.39
Bart0.41
Lisa0.25
Lenny0.14
Apu0.17
Moe0.34
Seymour0.25 Ralph
0.21
Maggie0.17
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07/14/10 DTProbLog 10
Homer1 Marge
0.39
Bart0.41
Lisa0.25
Lenny0.14
Apu0.17
Moe0.34
Seymour0.25 Ralph
0.21
Maggie0.17
ProbLogProbabilistic Facts0.3 :: buy_trust(_,_).
Background Knowledgebuys(X) : trusts(X,Y), buys(Y), buy_trust(X,Y).
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07/14/10 DTProbLog 11
HomerMarge
Bart Lisa
Lenny
Apu
Moe
SeymourRalph
Maggie
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07/14/10 DTProbLog 12
Homer0.20 Marge
Bart Lisa
Lenny
Apu
Moe
SeymourRalph
Maggie
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07/14/10 DTProbLog 13
Homer0.20 Marge
0.08
Bart0.08
Lisa0.05
Lenny0.03
Apu0.03
Moe0.07
Seymour0.05 Ralph
0.04
Maggie0.03
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07/14/10 DTProbLog 14
Homer0.20 Marge
0.08
Bart0.08
Lisa0.05
Lenny0.03
Apu0.03
Moe0.07
Seymour0.05 Ralph
0.04
Maggie0.03
Probabilistic Facts0.3 :: buy_trust(_,_).0.2 :: buy_marketing(_).
Background Knowledgebuys(X) : buys(X) : trusts(X,Y), marketed(X), buys(Y), buy_marketing(X). buy_trust(X,Y).
ProbLog
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07/14/10 DTProbLog 15
HomerMarge
Bart Lisa
Lenny
Apu
Moe
SeymourRalph
Maggie
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07/14/10 DTProbLog 16
Homer0.25 Marge
0.11
Bart0.12
Lisa0.08
Lenny0.10
Apu0.11
Moe0.25
Seymour0.12 Ralph
0.08
Maggie0.05
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07/14/10 DTProbLog 17
HomerMarge
Bart Lisa
Lenny
Apu
Moe
SeymourRalph
Maggie
????
??
?? ????
??
??
??
??
+$5
-$3
Which strategy gives the maximum expected utility?
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07/14/10 DTProbLog 18
$1.1
2
Homer0.12 Marge
0.13
Bart0.27
Lisa0.13
Lenny0.06
Apu0.11
Moe0.12
Seymour0.27 Ralph
0.16
Maggie0.07
+$5
-$3
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07/14/10 DTProbLog 19
$1.1
2
Homer0.12 Marge
0.13
Bart0.27
Lisa0.13
Lenny0.06
Apu0.11
Moe0.12
Seymour0.27 Ralph
0.16
Maggie0.07
Probabilistic Facts ...
Background Knowledge...
Decisions ? :: marketed(P) : person(P).
Utility Factsbuys(P) => 5 : person(P). marketed(P) => 3 : person(P).
DTProbLog
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07/14/10 DTProbLog 20
DTProbLog: the Algorithms
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DTProbLog: the Algorithms
● DTProbLog solves decision problems in complex relational and uncertain environments.
● Exact solution algorithm● Extends ProbLog's BDD-based inference● Efficient datastructures: BDD and ADD
● Approximate algorithms● Local search● K-best proofs
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Example:Dressing for unpredictable weather
Decision Facts Probabilistic Facts? :: umbrella. 0.3 :: rainy.? :: raincoat. 0.5 :: windy.
Background Knowledgedry : rainy, umbrella, not(broken_umbrella).dry : rainy, raincoat.dry : not(rainy).
broken_umbrella : umbrella, rainy, windy.
Utility Factsumbrella => 2. dry => 60.raincoat => 20. broken_umbrella => 40.
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Exact Solution Algorithm
1 Find all proofs for each utility attribute (Prolog)
2
3
4
5
Binary decision diagrams from the proofs
Algebraic decision diagrams for the probability of each attribute
Algebraic decision diagrams for the utility of each attribute
Algebraic decision diagram for the total utility
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1 Find all proofs for each utility attribute (Prolog)
2 Binary decision diagrams from the proofs
dry => 60.broken_umbrella => 40.
umbrella => 2.raincoat => 20.
● Leafs indicate attribute true or false ● Nodes are probabilistic facts or decisions
false true
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3 Algebraic decision diagrams for the probability of each attribute
dry => 60.
broken_umbrella => 40.
umbrella => 2.
raincoat => 20.
● Probabilistic facts are marginalized out● Nodes are decisions only● Leafs are probabilities
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4 Algebraic decision diagrams for the utility of each attribute
dry => 60.
broken_umbrella => 40.
umbrella => 2.
raincoat => 20.
● Leafs are expected utilities
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5 Algebraic decision diagram for the total utility
+ sound pruning (ADD not built entirely in memory)
∑
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Approximate Solution: Local Search
1 Find all proofs for each utility attribute (Prolog)
2
3
4
5
Binary decision diagrams from the proofs
Algebraic decision diagrams for the probability of each attribute
Algebraic decision diagrams for the utility of each attribute
Algebraic decision diagram for the total utility
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Approximate Solution: Local Search
1 Find all proofs for each utility attribute (Prolog)
2
3
4
5
Binary decision diagrams from the proofs
Algebraic decision diagrams for the probability of each attributeAlgebraic decision diagrams for the utility of each attributeAlgebraic decision diagram for the total utility
3 Greedy hillclimber search
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07/14/10 DTProbLog 30
Approximate Solution: K-best Proofs
1 Find all proofs for each utility attribute (Prolog)
2
3
4
5
Binary decision diagrams from the proofs
Algebraic decision diagrams for the probability of each attribute
Algebraic decision diagrams for the utility of each attribute
Algebraic decision diagram for the total utility
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07/14/10 DTProbLog 31
Approximate Solution: K-best Proofs
1 Find all proofs for each utility attribute (Prolog)
2
3
4
5
Binary decision diagrams from the proofs
Algebraic decision diagrams for the probability of each attribute
Algebraic decision diagrams for the utility of each attribute
Algebraic decision diagram for the total utility
1 Find the k most likely proofs for each utility attribute
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Experiments: Viral Marketing
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Experiments: Viral Marketing
● Synthetic dataset
Random power law graphs of increasing size
● Real-world Epinions dataset [Domingos02]
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(Q1) Does the exact solution algorithm perform better than naively calculating the utility of all strategies?
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(Q2) What is the difference in runtime and solution quality between exact and local search?
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(Q3) What is the difference in runtime and solution quality between for different values of k in k-best.
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(Q4) Do the algorithms scale?
Epinions social network● 75,000 people● 500,000 trust relations
YES● Local search● 17-best proofs● Solved in 16 hours
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Related Work & Conclusions
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Related Work
Representation Solution Evaluation
Relational Probabilities Global optimum
Local optimum
Exact inference
Approximate inference
Influence Diagrams
MLDNs[Nath]
ICL[Poole]
DTLPs[Chen]
DTProbLog
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Summary
● DTProbLog, the programming language● Probabilistic Prolog● Decisions● Utilities: rewards or costs attached to goals
● Solution algorithms● Exactly● Approximately
● Experiments● Effective● Scale well
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Ongoing and Future Work
● Sequential decision problems● Easy to represented in DTProbLog● Bad fit for solution algorithms
● Solvers● Integer linear programming● Bounded approximation● Monte-Carlo
● Lifting (many BDDs have same structure)● Learning DTProbLog programs● Inverse reinforcement learning
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Thank You!
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Viral Marketing
Decisions ? :: marketed(P) : person(P).
Probabilistic Facts0.3 :: buy_trust(_,_). 0.2 :: buy_marketing(_).
Background Knowledgebuys(X) : buys(X) : trusts(X,Y), marketed(X), buys(Y), buy_marketing(X). buy_trust(X,Y).
Utility Factsbuys(P) => 5 : person(P). marketed(P) => 3 : person(P).