Download Marie desJardins

Marie
M
desJar
d
rdins
Univers
U
sity off Marylland, B
Baltimore Co
ounty
““Subgoa
al Disco
overy and Lang
guage L
Learnin
ng in Re
einforce
ement
Learning Age
ents”
Thu
ursday, Nov
vember 6, 2
2014
12:00 – 1:00pm
CIT Buildin
ng, Room 36
68
As iintelligent agents and rob
bots become more commo
only used, m
methods to make interactio
on with the a
agents more
accessible will become
b
incre
easingly impo
ortant. In this talk, I will prresent a systtem for intellig
gent agents to learn task
descriptions from
m linguistically annotated demonstrations, using a reinforceme
ent learning framework ba
ased on
obje
ect-oriented Markov
M
decis
sion processes (OO-MDP
Ps). Our fram
mework learn
ns how to gro
ound natural language
com
mmands into reward functtions, using as
a input demo
onstrations o
of different ta
asks being ca
arried out in tthe
envvironment. Because langu
uage is grounded to rewa
ard functionss, rather than being directtly tied to the actions that
the agent can pe
erform, comm
mands can be high-level and
a can be ccarried out au
utonomouslyy in novel envvironments.
Ourr approach ha
as been emp
pirically valida
ated in a sim
mulated enviro
onment with both expert-created natu
ural language
e
com
mmands and commands gathered
g
from
m a user stud
dy.
I willl also describe a related, ongoing pro
oject to develop novel opttion discovery
ry methods fo
or OO-MDP d
domains.
The
ese methods permit agentts to identify new subgoa
als in complexx environmen
nts that can be transferre
ed to new
taskks. We have developed a framework called
c
Portab
ble Multi-policcy Option Disscovery for A
Automated Le
earning (PMODAL), an app
proach that extends
e
the PolicyBlocks
P
option disco
overy approacch to OO-MD
DPs.
Thiss work is collaborative res
search with Dr.
D Michael Littman
L
and D
Dr. James Ma
acGlashan o
of Brown, and
d Dr.
Sma
aranda Mure
esan of Colum
mbia Univers
sity. A numbe
er of UMBC students havve contribute
ed to the proje
ect: Shawn
Squ
uire, Nicholay
y Topin, Nick
k Haltemeyerr, Tenji Temb
bo, Michael B
Bishoff, Rose
e Carignan, a
and Nathaniel Lam.
Dr. Marie desJa
ardins is a Professor
P
in th
he Departme
ent of Compu
uter Science and Electrica
al Engineerin
ng at the
Univversity of Ma
aryland, Baltim
more County
y, where she has been a member of th
he faculty sin
nce 2001. Sh
he is a 2013-14 A
American Co
ouncil of Educ
cation Fellow
w, the 2014-17 UMBC Pre
esidential Te
eaching Profe
essor, and an
n inaugural
Hrabowski Acad
demic Innova
ation Fellow. Her researc
ch is in artificiial intelligencce, focusing o
on the areas of machine
learrning, multi-a
agent systems, planning, interactive AI
A techniques , information managemen
nt, reasoning
g with
unccertainty, and
d decision the
eory. Current research prrojects includ
de learning in
n the contextt of planning and decision
n
makking, analyzin
ng and visua
alizing uncertainty in mach
hine learning
g, trust modeling in multia
agent system
ms, and
com
mputer scienc
ce education. Dr. desJard
dins has publlished over 1 20 scientific papers in journals, confe
erences, and
worrkshops. She
e is an Assoc
ciate Editor of
o the Journal of Artificial Intelligence R
Research, is a member o
of the
edittorial board of
o AI Magazin
ne, and was the
t Program Cochair for A
AAAI-13. Sh
he has previo
ously served as AAAI
Liaison to the Bo
oard of Direc
ctors of the Computing
C
Re
esearch Asso
ociation, Vice
e-Chair of AC
CM's SIGART
T, and AAAI
Cou
uncillor. She is an ACM Distinguished
D
Member, is a AAAI Seni or Member, holds an app
pointment at the
Univversity of Ma
aryland Institu
ute for Advan
nced Studies
s, is a membe
er and forme
er chair of UM
MBC's Honors College
Advvisory Board, is the forme
er chair of UM
MBC's Faculty
y Affairs Com
mmittee, and
d serves on th
he advisory b
board of
UMBC's Center for Women in
i Technolog
gy.
For more infformation on this
t
talk and the
t HCRI Spe
eaker Series, contact hcri@
@brown.edu o
or visit hcri.brrown.edu.