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Prasad Tadepalli

Intelligent assistive systems Infer the goals of the human users and offer timely help;

applications to assistance, tutoring; Learning hierarchical task knowledge

Learning hierarchical task decomposition knowledge by watching people; learn new tasks

Transfer learning of sequential task knowledge Transferring knowledge learned in previous tasks to

new related tasks Learning from incomplete and biased data

Learn general rules from natural language texts which are incomplete and systematically biased

Learning in structured input and output spaces Learning to resolve co-references in natural language

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Learning from Demonstrations

Learn: A general plan

Explicate the goal hierarchy Generalize the plan Proactive help – complete

steps, prevent errors

Input: Single video of assembly

Recognize the activities Generate a causal annotation

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Approximate Solution Approach

Goal Recognizer Action Selection

Environment

UserUt

AtOt

P(G)

Assistant

Wt

1) Estimate posterior goal distribution given observations2) Action selection via myopic heuristics

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Folder Navigation AssistantFolder Navigation Assistant

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Folder Navigation Results

restricted folder set

all foldersconsidered

restricted to single action

multiple actions

1.3724

1.319

1.34

1.2344

Avg. no. of clicks per open/saveAs

Current Tasktracer

Full Assistant Framework

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Learning Hierarchical Task Knowledge(with Tom Dietterich)

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Basic Approach

Build a causal annotation of the trajectory using domain action models

Iteratively parse the trajectory into minimally interacting subtasks

EndStart Goto MG Goto Dep Goto CW Goto Dep

a.r a.r a.r a.r

req.gold

req.wood

a.l

req.gold

a.*reg.*

req.wood

reg.*

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Induced Wargus HierarchyRoot

Harvest WoodHarvest Gold

Get Gold Get Wood

Goto(loc)

Mine Gold Chop WoodGDeposit

Put Gold Put Wood

WGoto(townhall)GGoto(goldmine) WGoto(forest)GGoto(townhall)

WDeposit

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Results (7 replications)

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Lifelong Active Transfer Learning(with Alan Fern)

LB

Land battles

general RTS game model

SB

S1 S2

s14s13s12s11

WARCRAFT:human warfare

s21

experiences insea battle 2

L1 L2

l14l13l12l11 l21

MAGANT: ant warfare

“archers behind footmen”

“long range units behind short range units”

“dragons behindfireants”

experiences insea battle 1

Sea battles

“fast units to lure slow enemy units”

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Learning Rules from Texts(with Tom Dietterich and Xiaoli Fern)

Natural language texts are radically incomplete Worse yet, they are systematically biased.

Unusual facts are mentioned with higher frequency: the so called “man bites dog phenomenon”

Solution: explicitly or implicitly model the systematic bias and take it into account when counting evidence

Text documents

InformationExtractor

Extracted facts

Rule Learner

KB of rules

teamInGame(g,t1), teamInGame(g,t2), gameLoser(g,t2) gameWinner(g,t1)