3 research outputs found
End-to-End Game-Focused Learning of Adversary Behavior in Security Games
Stackelberg security games are a critical tool for maximizing the utility of
limited defense resources to protect important targets from an intelligent
adversary. Motivated by green security, where the defender may only observe an
adversary's response to defense on a limited set of targets, we study the
problem of learning a defense that generalizes well to a new set of targets
with novel feature values and combinations. Traditionally, this problem has
been addressed via a two-stage approach where an adversary model is trained to
maximize predictive accuracy without considering the defender's optimization
problem. We develop an end-to-end game-focused approach, where the adversary
model is trained to maximize a surrogate for the defender's expected utility.
We show both in theory and experimental results that our game-focused approach
achieves higher defender expected utility than the two-stage alternative when
there is limited data.Comment: Appeared at AAAI 202
MIPaaL: Mixed Integer Program as a Layer
Machine learning components commonly appear in larger decision-making
pipelines; however, the model training process typically focuses only on a loss
that measures accuracy between predicted values and ground truth values.
Decision-focused learning explicitly integrates the downstream decision problem
when training the predictive model, in order to optimize the quality of
decisions induced by the predictions. It has been successfully applied to
several limited combinatorial problem classes, such as those that can be
expressed as linear programs (LP), and submodular optimization. However, these
previous applications have uniformly focused on problems from specific classes
with simple constraints. Here, we enable decision-focused learning for the
broad class of problems that can be encoded as a Mixed Integer Linear Program
(MIP), hence supporting arbitrary linear constraints over discrete and
continuous variables. We show how to differentiate through a MIP by employing a
cutting planes solution approach, which is an exact algorithm that iteratively
adds constraints to a continuous relaxation of the problem until an integral
solution is found. We evaluate our new end-to-end approach on several real
world domains and show that it outperforms the standard two phase approaches
that treat prediction and prescription separately, as well as a baseline
approach of simply applying decision-focused learning to the LP relaxation of
the MIP
Balanced Order Batching with Task-Oriented Graph Clustering
Balanced order batching problem (BOBP) arises from the process of warehouse
picking in Cainiao, the largest logistics platform in China. Batching orders
together in the picking process to form a single picking route, reduces travel
distance. The reason for its importance is that order picking is a labor
intensive process and, by using good batching methods, substantial savings can
be obtained. The BOBP is a NP-hard combinational optimization problem and
designing a good problem-specific heuristic under the quasi-real-time system
response requirement is non-trivial. In this paper, rather than designing
heuristics, we propose an end-to-end learning and optimization framework named
Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by
reducing it to balanced graph clustering optimization problem. In BTOGCN, a
task-oriented estimator network is introduced to guide the type-aware
heterogeneous graph clustering networks to find a better clustering result
related to the BOBP objective. Through comprehensive experiments on
single-graph and multi-graphs, we show: 1) our balanced task-oriented graph
clustering network can directly utilize the guidance of target signal and
outperforms the two-stage deep embedding and deep clustering method; 2) our
method obtains an average 4.57m and 0.13m picking distance ("m" is the
abbreviation of the meter (the SI base unit of length)) reduction than the
expert-designed algorithm on single and multi-graph set and has a good
generalization ability to apply in practical scenario.Comment: 10 pages, 6 figure