Dynamic Resource Allocation for Optimizing Population Diffusion

Shan Xue, Alan Fern, Daniel Sheldon
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1033-1041, 2014.

Abstract

This paper addresses adaptive conservation planning, where the objective is to maximize the population spread of a species by allocating limited resources over time to conserve land parcels. This problem is characterized by having highly stochastic exogenous events (population spread), a large action branching factor (number of allocation options) and state space, and the need to reason about numeric resources. Together these characteristics render most existing AI planning techniques ineffective. The main contribution of this paper is to design and evaluate an online planner for this problem based on Hindsight Optimization (HOP), a technique that has shown promise in other stochastic planning problems. Unfortunately, standard implementations of HOP scale linearly with the number of actions in a domain, which is not feasible for conservation problems such as ours. Thus, we develop a new approach for computing HOP policies based on mixed-integer programming and dual decomposition. Our experiments on synthetic and real-world scenarios show that this approach is effective and scalable compared to existing alternatives.

Cite this Paper


BibTeX
@InProceedings{pmlr-v33-xue14, title = {{Dynamic Resource Allocation for Optimizing Population Diffusion}}, author = {Shan Xue and Alan Fern and Daniel Sheldon}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {1033--1041}, year = {2014}, editor = {Samuel Kaski and Jukka Corander}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/xue14.pdf}, url = {http://proceedings.mlr.press/v33/xue14.html}, abstract = {This paper addresses adaptive conservation planning, where the objective is to maximize the population spread of a species by allocating limited resources over time to conserve land parcels. This problem is characterized by having highly stochastic exogenous events (population spread), a large action branching factor (number of allocation options) and state space, and the need to reason about numeric resources. Together these characteristics render most existing AI planning techniques ineffective. The main contribution of this paper is to design and evaluate an online planner for this problem based on Hindsight Optimization (HOP), a technique that has shown promise in other stochastic planning problems. Unfortunately, standard implementations of HOP scale linearly with the number of actions in a domain, which is not feasible for conservation problems such as ours. Thus, we develop a new approach for computing HOP policies based on mixed-integer programming and dual decomposition. Our experiments on synthetic and real-world scenarios show that this approach is effective and scalable compared to existing alternatives.} }
Endnote
%0 Conference Paper %T Dynamic Resource Allocation for Optimizing Population Diffusion %A Shan Xue %A Alan Fern %A Daniel Sheldon %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-xue14 %I PMLR %P 1033--1041 %U http://proceedings.mlr.press/v33/xue14.html %V 33 %X This paper addresses adaptive conservation planning, where the objective is to maximize the population spread of a species by allocating limited resources over time to conserve land parcels. This problem is characterized by having highly stochastic exogenous events (population spread), a large action branching factor (number of allocation options) and state space, and the need to reason about numeric resources. Together these characteristics render most existing AI planning techniques ineffective. The main contribution of this paper is to design and evaluate an online planner for this problem based on Hindsight Optimization (HOP), a technique that has shown promise in other stochastic planning problems. Unfortunately, standard implementations of HOP scale linearly with the number of actions in a domain, which is not feasible for conservation problems such as ours. Thus, we develop a new approach for computing HOP policies based on mixed-integer programming and dual decomposition. Our experiments on synthetic and real-world scenarios show that this approach is effective and scalable compared to existing alternatives.
RIS
TY - CPAPER TI - Dynamic Resource Allocation for Optimizing Population Diffusion AU - Shan Xue AU - Alan Fern AU - Daniel Sheldon BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-xue14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 1033 EP - 1041 L1 - http://proceedings.mlr.press/v33/xue14.pdf UR - http://proceedings.mlr.press/v33/xue14.html AB - This paper addresses adaptive conservation planning, where the objective is to maximize the population spread of a species by allocating limited resources over time to conserve land parcels. This problem is characterized by having highly stochastic exogenous events (population spread), a large action branching factor (number of allocation options) and state space, and the need to reason about numeric resources. Together these characteristics render most existing AI planning techniques ineffective. The main contribution of this paper is to design and evaluate an online planner for this problem based on Hindsight Optimization (HOP), a technique that has shown promise in other stochastic planning problems. Unfortunately, standard implementations of HOP scale linearly with the number of actions in a domain, which is not feasible for conservation problems such as ours. Thus, we develop a new approach for computing HOP policies based on mixed-integer programming and dual decomposition. Our experiments on synthetic and real-world scenarios show that this approach is effective and scalable compared to existing alternatives. ER -
APA
Xue, S., Fern, A. & Sheldon, D.. (2014). Dynamic Resource Allocation for Optimizing Population Diffusion. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:1033-1041 Available from http://proceedings.mlr.press/v33/xue14.html.

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