Prasad Tadepalli
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Intelligent assistive systems
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Learning hierarchical task knowledge
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
Transferring knowledge learned in previous tasks to
new related tasks
Learning from incomplete and biased data
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
Learning hierarchical task decomposition knowledge by
watching people; learn new tasks
Transfer learning of sequential task knowledge
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
Infer the goals of the human users and offer timely
help; applications to assistance, tutoring;
Learn general rules from natural language texts which
are incomplete and systematically biased
Learning in structured input and output spaces
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Learning to resolve co-references in natural language
1
Learning from Demonstrations
Input:
Single video of assembly
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Recognize the activities
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Generate a causal annotation
Learn: A general plan
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Explicate the goal hierarchy
Generalize the plan
Proactive help – complete
steps, prevent errors
2
Approximate Solution Approach
1) Estimate posterior goal distribution given observations
2) Action selection via myopic heuristics
Goal Recognizer
P(G)
Action Selection
Assistant
Wt
Ot
At
Environment
Ut
User
6
Folder Navigation Assistant
7
Folder Navigation Results
all folders
considered
restricted
folder set
1.2344
1.319
Full Assistant Framework
1.3724
1.34
Current Tasktracer
restricted to
single action
multiple actions
Avg. no. of clicks per open/saveAs
8
Learning Hierarchical Task Knowledge
(with Tom Dietterich)
9
Basic Approach

Build a causal annotation of the trajectory
using domain action models
req.wood
a.r
a.r
a.l
Start reg.* Goto
MG
req.gold

Goto
a.r
Dep
Goto
a.r
CW
Goto
a.*
reg.*
req.wood
Dep
End
req.gold
Iteratively parse the trajectory into
minimally interacting subtasks
10
Induced Wargus Hierarchy
Root
Harvest Gold
Get Gold
Put Gold
Mine Gold
GGoto(goldmine)
Harvest Wood
Get Wood
GDeposit
Chop Wood
GGoto(townhall)
WGoto(forest)
Put Wood
WDeposit
WGoto(townhall)
Goto(loc)
11
Results (7 replications)
12
Lifelong Active Transfer Learning
(with Alan Fern)
“fast units to lure
slow enemy units”
general RTS game model

Land battles
“long range units behind
short range units”
Sea battles
SB
LB
“archers behind
footmen”
L1
l11
l12
l13
L2
l14
“dragons behind
fireants”
l21
s11
S1
s12
s13
S2
s14
experiences in
sea battle 1
WARCRAFT:human warfare
s21
experiences in
sea battle 2
MAGANT: ant warfare
13
Learning Rules from Texts
(with Tom Dietterich and Xiaoli Fern)
Information
Extractor
Text documents
Rule Learner
Extracted facts
teamInGame(g,t1), teamInGame(g,t2), gameLoser(g,t2) 
gameWinner(g,t1)
KB of rules
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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
14