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BLOG: Probabilistic Models with Unknown Objects

Brian Milch, Bhaskara Marthi, Stuart Russell,David Sontag, Daniel L. Ong, Andrey Kolobov

University of California at Berkeley

Basic Task

• Given observations, make inferences about underlying objects

• Difficulties:– Don’t know list of objects in advance– Don’t know when same object observed twice

(identity uncertainty / data association / record linkage)

Handling Unknown Objects

• Standard practice: special-purpose algorithms to resolve identity uncertainty

• Goal: Resolve identity uncertainty by inference in probabilistic model

• Bayesian LOGic (BLOG): representation language for models with – Unknown set of objects– Unknown map from observations to objects

Outline

• Motivating applications

• Bayesian Logic (BLOG)– Syntax– Semantics

• Proof-of-concept experimental results

Example 1: Aircraft Tracking

DetectionFailure

Example 1: Aircraft Tracking

FalseDetection

UnobservedObject

Example 2: Bibliographies

S. Russel and P. Norvig (1995). Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice Hall.

Russell, Stuart and Norvig, Peter. Articial Intelligence. Prentice-Hall, 1995.

Simple Example: Balls in an Urn

Draws(with replacement)

P(n balls in urn)

P(n balls in urn | draws)

1 2 3 4

Possible Worlds

……

… …

3.00 x 10-3 7.61 x 10-4 1.19 x 10-5

2.86 x 10-4 1.14 x 10-12

Draws Draws Draws

Draws Draws

Distributions over First-Order Structures

• Idea goes back to Gaifman [1964]• Halpern [1990] defines language for stating constraints

on such distributions– But not specifying a distribution uniquely

• Logic programming approaches [Poole 1993; Sato & Kameya 2001; Kersting & De Raedt 2001] define unique distributions, but assume unique names and domain closure

• PRMs [Koller & Pfeffer 1998] have special constructs for number uncertainty, existence uncertainty

• BLOG: Unified syntax for distributions over worlds with:– Varying sets of objects– Varying mappings from observations to objects

See also MEBN [Laskey and da Costa, UAI 2005]

Generative Process for Possible Worlds

Draws(with replacement)

1 2 3 4

BLOG Model for Urn and Balls

type Color; type Ball; type Draw;

random Color TrueColor(Ball);random Ball BallDrawn(Draw);random Color ObsColor(Draw);

guaranteed Color Blue, Green;guaranteed Draw Draw1, Draw2, Draw3, Draw4;

#Ball ~ Poisson[6]();

TrueColor(b) ~ TabularCPD[[0.5, 0.5]]();

BallDrawn(d) ~ UniformChoice({Ball b});

ObsColor(d) if (BallDrawn(d) != null) then ~ NoisyCopy(TrueColor(BallDrawn(d)));









header

number statement

dependencystatements









Identity uncertainty: BallDrawn(Draw1) = BallDrawn(Draw2)?









Arbitrary conditionalprobability distributions

CPD arguments









Context-specificdependence

Generative Process for Aircraft Tracking

Sky RadarExistence of radar blips depends on existence and locations of aircraft

BLOG Model for Aircraft Tracking

…

#Aircraft ~ NumAircraftDistrib();

State(a, t) if t = 0 then ~ InitState() else ~ StateTransition(State(a, Pred(t)));

#Blip: (Source, Time) -> (a, t) ~ NumDetectionsDistrib(State(a, t));

#Blip: (Time) -> (t) ~ NumFalseAlarmsDistrib();

ApparentPos(r)if (Source(r) = null) then ~ FalseAlarmDistrib()else ~ ObsDistrib(State(Source(r), Time(r)));

2

Source

Time

a

t

Blips

2

Time

t Blips

Declarative Semantics

• What is the set of possible worlds?

• What is the probability distribution over worlds?

What Exactly Are the Objects?

• Objects are tuples that encode generation history

• Aircraft: (Aircraft, 1), (Aircraft, 2), …• Blip from (Aircraft, 2) at time 8:

(Blip, (Source, (Aircraft, 2)), (Time, 8), 1)

Basic Random Variables (RVs)

• For each number statement and tuple of generating objects, have RV for number of objects generated

• For each function symbol and tuple of arguments, have RV for function value

• Lemma: Full instantiation of these RVs uniquely identifies a possible world

Another Look at a BLOG Model

…




ObsColor(d) if !(BallDrawn(d) = null) then ~ NoisyCopy(TrueColor(BallDrawn(d)));

Dependency and number statements define CPDs for basic RVs

Just a Bayes Net?

TrueColor(B1) TrueColor(B2) TrueColor(B3) …

ObsColor(D1)

BallDrawn(D1)

#Ball

Infinite parent set

Standard BN results no longer apply

Probability Distribution

• BLOG model specifies:– Conditional distributions for basic RVs– Factorization properties for certain finite

instantiations of basic RVs

• Theorem: Under certain conditions (analogous to BN acyclicity), every BLOG model defines unique distribution over possible worlds

Inference

• Does infinite set of basic RVs prevent inference?• No: Sampling algorithm only needs to instantiate

finite set of relevant variables• Algorithms:

– Rejection sampling [this paper]

– Guided likelihood weighting [Milch et al., AI/Stats 2005]

• Theorem: For large class of BLOG models, sampling algorithms converge to correct probability for any query, using finite time per sampling step

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 5 10 15 20 25

Number of Balls

Pro

ba

bili

tyProof-Of-Concept Experiment

prior

posterior

• Given 10 draws, all appearing blue

• 5 runs of 100,000 samples each

Conclusions

• Bayesian logic (BLOG) models define unique distributions over first-order model structures with– Varying sets of objects– Varying mappings from terms to objects

• Future work:– Practical inference algorithms– Applications to text understanding– Applications to situation awareness (DBLOG)

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