TY - GEN
T1 - POMDPs for assisting homeless shelters – Computational and deployment challenges
AU - Yadav, Amulya
AU - Chan, Hau
AU - Jiang, Albert
AU - Rice, Eric
AU - Kamar, Ece
AU - Grosz, Barbara
AU - Tambe, Milind
N1 - Funding Information:
This research was supported by MURI Grant W911NF-11-1-0332 and NIMH Grant number R01-MH093336.
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - This paper looks at challenges faced during the ongoing deployment of HEALER, a POMDP based software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER’s sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. In order to compute its plans, HEALER (i) casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; (ii) and constructs social networks of homeless youth at low cost, using a Facebook application. HEALER is currently being deployed in the real world in collaboration with a homeless shelter. Initial feedback from the shelter officials has been positive but they were surprised by the solutions generated by HEALER as these solutions are very counter-intuitive. Therefore, there is a need to justify HEALER’s solutions in a way that mirrors the officials’ intuition. In this paper, we report on progress made towards HEALER’s deployment and detail first steps taken to tackle the issue of explaining HEALER’s solutions.
AB - This paper looks at challenges faced during the ongoing deployment of HEALER, a POMDP based software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER’s sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. In order to compute its plans, HEALER (i) casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; (ii) and constructs social networks of homeless youth at low cost, using a Facebook application. HEALER is currently being deployed in the real world in collaboration with a homeless shelter. Initial feedback from the shelter officials has been positive but they were surprised by the solutions generated by HEALER as these solutions are very counter-intuitive. Therefore, there is a need to justify HEALER’s solutions in a way that mirrors the officials’ intuition. In this paper, we report on progress made towards HEALER’s deployment and detail first steps taken to tackle the issue of explaining HEALER’s solutions.
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U2 - 10.1007/978-3-319-46840-2_5
DO - 10.1007/978-3-319-46840-2_5
M3 - Conference contribution
AN - SCOPUS:84989868492
SN - 9783319468396
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 67
EP - 87
BT - Autonomous Agents and Multiagent Systems, IFAAMAS 2016 Workshops, Best Papers, Revised Selected Papers
A2 - Osman, Nardine
A2 - Sierra, Carles
PB - Springer Verlag
T2 - International Foundation for Autonomous Agents and Multiagent Systems, IFAAMAS 2016
Y2 - 9 May 2016 through 10 May 2016
ER -
