learning models of relational stochastic processes

Learning Models of Relational Stochastic Processes

Sumit Sanghai

Motivation

• Features of real-world domains – Multiple classes, objects, relations

P2P1

P3

A2A1

V1

Motivation

• Features of real-world domains – Multiple classes, objects, relations– Uncertainty

P2P1

P3

A2A1

V1

?

??

Motivation

• Features of real-world domains – Multiple classes, objects, relations– Uncertainty– Changes with time

P2P1

P3

A2A1

V1 P4A3

P5

P6

Relational Stochastic Processes

• Features– Multiple classes, objects, relations– Uncertainty– Change over time

• Examples: Social networks, molecular biology, user activity modeling, web, plan recognition, …

• Growth inherent or due to explicit actions • Most large datasets are gathered over time

– Explore dependencies over time– Predict future

Manufacturing Process

Manufacturing Process

Paint(A, blue)

Manufacturing Process

Paint(A, blue) Bolt(B, C)

Manufacturing Process

Paint(A, blue) Bolt(B, C)

A.colt t + 1A.col Predblue

redredblue blue

0.10.9

0.95redblue 0.05

Why are they different?

• Modeling object, relationships modification, creation and deletion

• Modeling actions (preconditions/effects), activities, plans

• Can’t just throw ``time’’ into the mix• Summarizing object information• Learning can be made easier by concentrating

on temporal dependencies• Sophisticated inference techniques like particle

filtering may be applicable

Outline

• Background: Dynamic Bayes Nets

• Dynamic Probabilistic Relational Models

• Inference in DPRMs

• Learning with Dynamic Markov Logic Nets

• Future Work

Dynamic Bayesian Networks

• DBNs model change in uncertain variables over time

• Each time slice consists of state/observation variables

• Bayesian network models dependency of current on previous time slice(s)

• At each node a conditional model (CPT, logistic regression, etc.)

YtYt+1

t t+1

X1t

X2t X2

t+1

X1t+1

Inference and learning in DBNs

• Inference– All techniques from BNs are used– Special techniques like Particle Filtering,

Boyen-Koller, Factored Frontier, etc. can be used for state monitoring

• Learning– Problem exactly similar to BNs– Structural EM used in case of missing data

• Needs a fast inference algorithm

Particle Filtering in DBNs

• Task: State monitoring• Particle Filter

– Samples represent state distribution at time t– Generate samples for t+1 based on model– Reweight according to observations– Resample

• Particles stay in most probable regions• Performs poorly in hi-dimensional spaces

Incorporating time in First Order Probabilistic Models

• Simple approach: Time is one of the arguments in first order logic– Year(p100, 1996), Hot (SVM, 2004)

• But time is special– World is growing in the direction of time

• Hot (SVM, 2005) dependent on Hot (SVM, 2004)

– Hard to discover rules that help in state monitoring, future prediction, etc.

– Blowup by incorporating time explicitly– Special inference algorithms no longer applicable

Dynamic Probabilistic Relational Models

• DPRM is a PRM replicated over time slices– DBN is a Bayes Net replicated over time slices

• In a DPRM attributes for each class dependent on attributes of same/related class – Related class from current/previous time slice– Previous relation

• “Unrolled” DPRM = DBN

DPRMs: Example

t

PLATE1

Color : Red#Holes : 4Bolted-To :

BRACKET7

Color : BlueSize : Large

t+1

Action:Bolt

PLATE1

Color : Red#Holes : 4Bolted-To :

BRACKET7

Color : BlueSize : Large

Inference in DPRMs

• Relational uncertainty huge state space

– E.g. 100 parts 10,000 possible attachments

• Particle filter likely to perform poorly– Rao-Blackwellization ??

• Assumptions (relaxed afterwards)– Uncertain reference slots do not appear in slot

chains or as parents– Single-valued uncertain reference slots

Rao-Blackwellization in DPRMs

• Sample propositional attributes– Smaller space and less error– Constitute the particle

• For each uncertain reference slot and particle state – Maintain a multinomial distribution over the set of

objects in the target class – Conditioned on values of propositional variables

RBPF: A Particle

Propositional attributes

Bolted-To-1 Bolted-To-2

Pl1 Pl2 Pl3 Pl4 Pl5Color Size Wt Pl1 Pl2 Pl3 Pl4 Pl5

0.1 0.1 0.2 0.1 0.5

Bracket1

Red Large 2lbs 0.3 0.2 0.2 0.1 0.2Pl6 Pl7 Pl8 Pl9 Pl10

0.25 0.3 0.1 0.25 0.1

Reference slots

Pl1 Pl2 Pl3 Pl4 Pl5Pl1 Pl2 Pl3 Pl4 Pl5

0.1 0.1 0.2 0.1 0.5

Bracket2

Blue Small 1lb 0.4 0.1 0.1 0.3 0.1Pl6 Pl7 Pl8 Pl9 Pl10

0.1 0.1 0.2 0.1 0.5

………………

Experimental Setup• Assembly Domain (AIPS98)

– Objects : Plates, Brackets, Bolts– Attributes : Color, Size, Weight, Hole type, etc.– Relations : Bolted-To, Welded-To– Propositional Actions: Paint, Polish, etc.– Relational Actions: Weld, Bolt– Observations

• Fault model– Faults cause uncertainty– Actions and observations uncertain– Governed by global fault probability (fp)

• Task: State Monitoring

RBPF vs PF

Problems with DPRMs

• Relationships modeled using slots– Slots and slot chains hard to represent and

understand

• Modeling ternary relationships becomes hard• Small subset of first-order logic (conjunctive

expressions) used to specify dependencies • Independence between objects participating in

multi-valued slots• Unstructured conditional model

Relational Dynamic Bayes Nets

• Replace slots and attributes with predicates (like in MLNs)

• Each predicate has parents which are other predicates

• The conditional model is a first-order probability tree

• The predicate graph is acyclic

• A copy of the model at each time slice

Inference: Relaxing the assumptions

• RBPF is infeasible when assumptions relaxed• Observation: Similar objects behave similarly• Sample all predicates

– Small number of samples, but large relational predicate space

– Smoothing : Likelihood of a small number of points can tell relative likelihood of others

• Given a particle smooth each relational predicate towards similar states

Simple Smoothing

Pl1 Pl2 Pl3 Pl4 Pl5Pl1 Pl2 Pl3 Pl4 Pl5

0.1 0.1 0.2 0.1 0.5 0.9 0.4 0.9 0.9 0.9

Propositional attributes

Color Size Wt

Red Large 2lbs

Bolted-To (Bracket_1, X)

Pl1 Pl2 Pl3 Pl4 Pl5Pl1 Pl2 Pl3 Pl4 Pl5

0.1 0.1 0.2 0.1 0.5 1 0 1 1 1

Particle Filtering : A particle

after smoothing

Simple Smoothing Problems

• Simple smoothing : probability of an object pair depends upon values of all other object pairs of the relation– E.g. P( Bolt(Br_1,Pl_1) ) depends on

Bolt(Br_i, Pl_j) for all i and j.

• Solution : Make an object pair depend more upon similar pairs– Similarity given by properties of the objects

Abstraction Lattice Smoothing

• Abstraction represents a set of similar object pairs.– Bolt(Br1, Pl1)– Bolt(red, large)– Bolt(*,*)

• Abstraction Lattice: a hierarchy of abstractions– Each abstraction has a coefficient

Abstraction Lattice : an example

Abstraction Lattice Smoothing

• P(Bolt(B1, P1))= w1 Ppf (Bolt(B1, P1) +

w2 Ppf(Bolt(red, large)) +

w3 Ppf(Bolt(*,*))

• Joint distributions are estimated using relational kernel density estimation– Kernel K(x, xi) gives distance between the

state and the particle– Distance measured using abstractions

Abstraction Smoothing vs PF

Learning with DMLNs

• Task: Can MLN learning be directly applied to learn time-based models?

• Domains– Predicting author, topic distribution in High-

Energy Theoretical Physics papers from KDDCup 2003

– Learning action models of manufacturing assembly processes

Learning with DMLNs

• DMLNs = MLNs + Time predicates– R(x,y) -> R(x,y,t), Succ(11, 10), Gt(10,5)

• Now directly apply MLN structure learning algorithm (Stanley and Pedro)

• To make it work– Use templates to model Markovian

assumption – Restrict number of predicates per clause– Add background knowledge

Physics dataset

Manufacturing Assembly

Current and Future Work

• Current Work – Programming by Demonstration using Dynamic First

Order Probabilistic Models

• Future Work– Learning object creation models– Learning in presence of missing data– Modeling hierarchies (very useful for fast inference)– Applying abstraction smoothing to ``static’’ relational

models