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Bistra Dilkina

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41 papers
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41

IJCAI Conference 2025 Conference Paper

Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problems

  • Junyang Cai
  • Serdar Kadioğlu
  • Bistra Dilkina

Mixed-integer programming (MIP) is a powerful paradigm for modeling and solving various important combinatorial optimization problems. Recently, learning-based approaches have shown a potential to speed up MIP solving via offline training that then guides important design decisions during the search. However, a significant drawback of these methods is their heavy reliance on offline training, which requires collecting training datasets and computationally costly training epochs yet offering only limited generalization to unseen (larger) instances. In this paper, we propose Balans, an adaptive meta-solver for MIPs with online learning capability that does not require any supervision or apriori training. At its core, Balans is based on adaptive large-neighborhood search, operating on top of an MIP solver by successive applications of destroy and repair neighborhood operators. During the search, the selection among different neighborhood definitions is guided on the fly for the instance at hand via multi-armed bandit algorithms. Our extensive experiments on hard optimization instances show that Balans offers significant performance gains over the default MIP solver, is better than committing to any single best neighborhood, and improves over the state-of-the-art large-neighborhood search for MIPs. Finally, we release Balans as a highly configurable, MIP solver agnostic, open-source software.

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TMLR Journal 2025 Journal Article

Solving Multi-agent Path Finding as an LLM Benchmark: How, How Good and Why

  • Weizhe Chen
  • Sven Koenig
  • Bistra Dilkina

The rapid success of large language models (LLMs) has spurred extensive research into their ability to solve a wide range of tasks. However, their potential in multi-agent planning remains underexplored. Multi-agent planning presents unique challenges due to the combined complexity of coordination and long-horizon reasoning, often making it difficult to leverage external tools for assistance. In this paper, we introduce Multi-Agent Path Finding (MAPF), also known as multi-robot route planning, as a novel benchmark for evaluating the reasoning capabilities of LLMs. We first describe how the MAPF benchmark can be adapted for LLM-based evaluation, including dataset curation and an agentic workflow for LLMs. We show the motivating success of single-agent planning and multi-agent pathfinding in an empty room map without obstacles, then the failure to plan on the harder room map and maze map of the standard MAPF benchmark. We present our position on why directly solving MAPF with LLMs has not been successful yet, and we use various experiments to support our hypothesis. Based on our results, we discussed how researchers with different backgrounds could help with this problem from different perspectives.

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AAAI Conference 2024 Conference Paper

Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search

  • Thomy Phan
  • Taoan Huang
  • Bistra Dilkina
  • Sven Koenig

Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i.e., the neighborhood of the solution, using randomized destroy heuristics and prioritized planning. Despite their recent success in various MAPF instances, current LNS-based approaches lack exploration and flexibility due to greedy optimization with a fixed neighborhood size which can lead to low-quality solutions in general. So far, these limitations have been addressed with extensive prior effort in tuning or offline machine learning beyond actual planning. In this paper, we focus on online learning in LNS and propose Bandit-based Adaptive LArge Neighborhood search Combined with Exploration (BALANCE). BALANCE uses a bi-level multi-armed bandit scheme to adapt the selection of destroy heuristics and neighborhood sizes on the fly during search. We evaluate BALANCE on multiple maps from the MAPF benchmark set and empirically demonstrate performance improvements of at least 50% compared to state-of-the-art anytime MAPF in large-scale scenarios. We find that Thompson Sampling performs particularly well compared to alternative multi-armed bandit algorithms.

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ICML Conference 2024 Conference Paper

Contrastive Predict-and-Search for Mixed Integer Linear Programs

  • Taoan Huang
  • Aaron M. Ferber
  • Arman Zharmagambetov
  • Yuandong Tian
  • Bistra Dilkina

Mixed integer linear programs (MILP) are flexible and powerful tools for modeling and solving many difficult real-world combinatorial optimization problems. In this paper, we propose a novel machine learning (ML)-based framework ConPaS that learns to predict solutions to MILPs with contrastive learning. For training, we collect high-quality solutions as positive samples. We also collect low-quality or infeasible solutions as negative samples using novel optimization-based or sampling approaches. We then learn to make discriminative predictions by contrasting the positive and negative samples. During testing, we predict and fix the assignments for a subset of integer variables and then solve the resulting reduced MILP to find high-quality solutions. Empirically, ConPaS achieves state-of-the-art results compared to other ML-based approaches in terms of the quality of and the speed at which solutions are found.

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ICML Conference 2024 Conference Paper

GenCO: Generating Diverse Designs with Combinatorial Constraints

  • Aaron M. Ferber
  • Arman Zharmagambetov
  • Taoan Huang
  • Bistra Dilkina
  • Yuandong Tian

Deep generative models like GAN and VAE have shown impressive results in generating unconstrained objects like images. However, many design settings arising in industrial design, material science, computer graphics and more require that the generated objects satisfy hard combinatorial constraints or meet objectives in addition to modeling a data distribution. To address this, we propose GenCO, a generative framework that guarantees constraint satisfaction throughout training by leveraging differentiable combinatorial solvers to enforce feasibility. GenCO imposes the generative loss on provably feasible solutions rather than intermediate soft solutions, meaning that the deep generative network can focus on ensuring the generated objects match the data distribution without having to also capture feasibility. This shift enables practitioners to enforce hard constraints on the generated outputs during end-to-end training, enabling assessments of their feasibility and introducing additional combinatorial loss components to deep generative training. We demonstrate the effectiveness of our approach on a variety of generative combinatorial tasks, including game level generation, map creation for path planning, and photonic device design, consistently demonstrating its capability to yield diverse, high-quality solutions that verifiably adhere to user-specified combinatorial properties.

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ECAI Conference 2024 Conference Paper

Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning

  • Junyang Cai
  • Taoan Huang
  • Bistra Dilkina

Many real-world problems can be efficiently modeled as Mixed Integer Linear Programs (MILPs) and solved with the Branch-and-Bound method. Prior work has shown the existence of MILP backdoors, small sets of variables such that prioritizing branching on them when possible leads to faster running times. However, finding high-quality backdoors that improve running times remains an open question. Previous work learns to estimate the relative solver speed of randomly sampled backdoors through ranking and learns to decide whether to use the highest-ranked backdoor candidate. In this paper, we utilize the Monte-Carlo tree search method to collect backdoors for training, rather than relying on random sampling, and adapt a contrastive learning framework to train a Graph Attention Network model to predict backdoors. Our method, evaluated on several common MILP problem domains, demonstrates performance improvements over both Gurobi and previous models.

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ICML Conference 2024 Conference Paper

Position: Application-Driven Innovation in Machine Learning

  • David Rolnick
  • Alán Aspuru-Guzik
  • Sara Beery
  • Bistra Dilkina
  • Priya L. Donti
  • Marzyeh Ghassemi
  • Hannah Kerner
  • Claire Monteleoni

In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.

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ICAPS Conference 2023 Conference Paper

Deadline-Aware Multi-Agent Tour Planning

  • Taoan Huang
  • Vikas Shivashankar
  • Michael Caldara
  • Joseph W. Durham
  • Jiaoyang Li 0001
  • Bistra Dilkina
  • Sven Koenig

The increasing demand for same-day delivery and the commitment of e-commerce companies to this service raise a number of challenges in logistics. One of these challenges for fulfillment centers is to coordinate hundreds of mobile robots in their automated warehouses efficiently to allow for the retrieval and packing of thousands of ordered items within the promised delivery deadlines. We formulate this challenge as the new problem of deadline-aware multi-agent tour planning, where the objective is to coordinate robots to visit multiple picking stations in congested warehouses to allow as many orders to be packed on time as possible. To solve it, we propose LaRge NeighbOrhood Search for DEadline-Aware MulTi-Agent Tour PlAnning (ROSETTA). We conduct extensive experiments to evaluate ROSETTA with up to 350 robots in simulated warehouses inspired by KIVA systems. We show that it increases the number of orders completed on time by up to 38% compared to several baseline algorithms and also significantly outperforms them in terms of throughput and station utilization.

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NeurIPS Conference 2023 Conference Paper

Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information

  • Arman Zharmagambetov
  • Brandon Amos
  • Aaron Ferber
  • Taoan Huang
  • Bistra Dilkina
  • Yuandong Tian

Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perform poorly without expert tuning. By learning an optimizer $\mathbf{g}$ to tackle these challenging problems with $f$ as the objective, the optimization process can be substantially accelerated by leveraging past experience. The optimizer can be trained with supervision from known optimal solutions or implicitly by optimizing the compound function $f\circ \mathbf{g}$. The implicit approach may not require optimal solutions as labels and is capable of handling problem uncertainty; however, it is slow to train and deploy due to frequent calls to optimizer $\mathbf{g}$ during both training and testing. The training is further challenged by sparse gradients of $\mathbf{g}$, especially for combinatorial solvers. To address these challenges, we propose using a smooth and learnable **Landscape Surrogate** $\mathcal{M}$ as a replacement for $f\circ \mathbf{g}$. This surrogate, learnable by neural networks, can be computed faster than the solver $\mathbf{g}$, provides dense and smooth gradients during training, can generalize to unseen optimization problems, and is efficiently learned via alternating optimization. We test our approach on both synthetic problems, including shortest path and multidimensional knapsack, and real-world problems such as portfolio optimization, achieving comparable or superior objective values compared to state-of-the-art baselines while reducing the number of calls to $\mathbf{g}$. Notably, our approach outperforms existing methods for computationally expensive high-dimensional problems.

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ICML Conference 2023 Conference Paper

Moccasin: Efficient Tensor Rematerialization for Neural Networks

  • Burak Bartan
  • Haoming Li 0002
  • Harris Teague
  • Christopher Lott
  • Bistra Dilkina

The deployment and training of neural networks on edge computing devices pose many challenges. The low memory nature of edge devices is often one of the biggest limiting factors encountered in the deployment of large neural network models. Tensor rematerialization or recompute is a way to address high memory requirements for neural network training and inference. In this paper we consider the problem of execution time minimization of compute graphs subject to a memory budget. In particular, we develop a new constraint programming formulation called Moccasin with only $O(n)$ integer variables, where $n$ is the number of nodes in the compute graph. This is a significant improvement over the works in the recent literature that propose formulations with $O(n^2)$ Boolean variables. We present numerical studies that show that our approach is up to an order of magnitude faster than recent work especially for large-scale graphs.

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ICML Conference 2023 Conference Paper

Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning

  • Taoan Huang
  • Aaron M. Ferber
  • Yuandong Tian
  • Bistra Dilkina
  • Benoit Steiner

Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems. Recently, it has been shown that Large Neighborhood Search (LNS), as a heuristic algorithm, can find high-quality solutions to ILPs faster than Branch and Bound. However, how to find the right heuristics to maximize the performance of LNS remains an open problem. In this paper, we propose a novel approach, CL-LNS, that delivers state-of-the-art anytime performance on several ILP benchmarks measured by metrics including the primal gap, the primal integral, survival rates and the best performing rate. Specifically, CL-LNS collects positive and negative solution samples from an expert heuristic that is slow to compute and learns a more efficient one with contrastive learning. We use graph attention networks and a richer set of features to further improve its performance.

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ICML Conference 2023 Conference Paper

SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems

  • Aaron M. Ferber
  • Taoan Huang
  • Daochen Zha
  • Martin Schubert
  • Benoit Steiner
  • Bistra Dilkina
  • Yuandong Tian

Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to their linear counterparts. To bridge this gap, we propose $\textbf{\emph{\texttt{SurCo}}}$ that learns linear $\underline{\text{Sur}}$rogate costs which can be used in existing $\underline{\text{Co}}$mbinatorial solvers to output good solutions to the original nonlinear combinatorial optimization problem. The surrogate costs are learned end-to-end with nonlinear loss by differentiating through the linear surrogate solver, combining the flexibility of gradient-based methods with the structure of linear combinatorial optimization. We propose three $\texttt{SurCo}$ variants: $\texttt{SurCo}-\texttt{zero}$ for individual nonlinear problems, $\texttt{SurCo}-\texttt{prior}$ for problem distributions, and $\texttt{SurCo}-\texttt{hybrid}$ to combine both distribution and problem-specific information. We give theoretical intuition motivating $\texttt{SurCo}$, and evaluate it empirically. Experiments show that $\texttt{SurCo}$ finds better solutions faster than state-of-the-art and domain expert approaches in real-world optimization problems such as embedding table sharding, inverse photonic design, and nonlinear route planning.

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AAAI Conference 2022 Conference Paper

Anytime Multi-Agent Path Finding via Machine Learning-Guided Large Neighborhood Search

  • Taoan Huang
  • Jiaoyang Li
  • Sven Koenig
  • Bistra Dilkina

Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for a team of agents in a common environment. MAPF is NP-hard to solve optimally and, in some cases, also bounded-suboptimally. It is thus timeconsuming for (bounded-sub)optimal solvers to solve large MAPF instanc¡es. Anytime algorithms find solutions quickly for large instances and then improve them to close-to-optimal ones over time. In this paper, we improve the current state-ofthe-art anytime solver MAPF-LNS, that first finds an initial solution fast and then repeatedly replans the paths of subsets of agents via Large Neighborhood Search (LNS). It generates the subsets of agents for replanning by randomized destroy heuristics, but not all of them increase the solution quality substantially. We propose to use machine learning to learn how to select a subset of agents from a collection of subsets, such that replanning increases the solution quality more. We show experimentally that our solver, MAPF-ML-LNS, significantly outperforms MAPF-LNS on the standard MAPF benchmark set in terms of both the speed of improving the solution and the final solution quality.

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AAAI Conference 2022 Conference Paper

Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework

  • Elias B. Khalil
  • Pashootan Vaezipoor
  • Bistra Dilkina

In Mixed Integer Linear Programming (MIP), a (strong) backdoor is a “small” subset of an instance’s integer variables with the following property: in a branch-and-bound procedure, the instance can be solved to global optimality by branching only on the variables in the backdoor. Constructing datasets of pre-computed backdoors for widely used MIP benchmark sets or particular problem families can enable new questions around novel structural properties of a MIP, or explain why a problem that is hard in theory can be solved efficiently in practice. Existing algorithms for finding backdoors rely on sampling candidate variable subsets in various ways, an approach which has demonstrated the existence of backdoors for some instances from MIPLIB2003 and MI- PLIB2010. However, these algorithms fall short of consistently succeeding at the task due to an imbalance between exploration and exploitation. We propose BaMCTS, a Monte Carlo Tree Search framework for finding backdoors to MIPs. Extensive algorithmic engineering, hybridization with traditional MIP concepts, and close integration with the CPLEX solver have enabled our method to outperform baselines on MIPLIB2017 instances, finding backdoors more frequently and more efficiently.

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SoCS Conference 2022 Conference Paper

Learning a Priority Ordering for Prioritized Planning in Multi-Agent Path Finding

  • Shuyang Zhang
  • Jiaoyang Li 0001
  • Taoan Huang
  • Sven Koenig
  • Bistra Dilkina

Prioritized Planning (PP) is a fast and popular framework for solving Multi-Agent Path Finding, but its solution quality depends heavily on the predetermined priority ordering of the agents. Current PP algorithms use either greedy policies or random assignments to determine a total priority ordering, but none of them dominates the others in terms of the success rate and solution quality (measured by the sum-of-costs). We propose a machine-learning (ML) framework to learn a good priority ordering for PP. We develop two models, namely ML-T, which is trained on a total priority ordering, and ML-P, which is trained on a partial priority ordering. We propose to boost the effectiveness of PP by further applying stochastic ranking and random restarts. The results show that our ML-guided PP algorithms outperform the existing PP algorithms in success rate, runtime, and solution quality on small maps in most cases and are competitive with them on large maps despite the difficulty of collecting training data on these maps.

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AAAI Conference 2021 Conference Paper

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

  • Umang Gupta
  • Aaron M Ferber
  • Bistra Dilkina
  • Greg Ver Steeg

Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the data so that it is statistically independent of group membership, but this may throw away too much information when a reasonable compromise between fairness and accuracy is desired. Another common approach is to limit the ability of a particular adversary who seeks to maximize parity. Unfortunately, representations produced by adversarial approaches may still retain biases as their efficacy is tied to the complexity of the adversary used during training. To this end, we theoretically establish that by limiting the mutual information between representations and protected attributes, we can assuredly control the parity of any downstream classifier. We demonstrate an effective method for controlling parity through mutual information based on contrastive information estimators and show that they outperform approaches that rely on variational bounds based on complex generative models. We test our approach on UCI Adult and Heritage Health datasets and demonstrate that our approach provides more informative representations across a range of desired parity thresholds while providing strong theoretical guarantees on the parity of any downstream algorithm.

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AAMAS Conference 2021 Conference Paper

Learning Node-Selection Strategies in Bounded-Suboptimal Conflict-Based Search for Multi-Agent Path Finding

  • Taoan Huang
  • Bistra Dilkina
  • Sven Koenig

Multi-Agent Path Finding (MAPF) is an NP-hard problem that has important applications for distribution centers, traffic management and computer games, and it is still difficult for current solvers to solve large instances optimally. Bounded suboptimal solvers, such as Enhanced Conflict-Based Search (ECBS) and its variants, are more efficient than optimal solvers in finding a solution with suboptimality guarantees. ECBS is a tree search algorithm that expands the search tree by repeatedly selecting search tree nodes from a focal list. In this work, we propose to use machine learning (ML) to learn a node-selection strategy to speed up ECBS. In the first phase of our framework, we use imitation learning and curriculum learning to learn node-selection strategies iteratively for different numbers of agents from training instances. In the second phase, we deploy the learned models in ECBS and test their solving performance on unseen instances drawn from the same distribution as the training instances. We demonstrate that our solver, ECBS+ML, shows substantial improvement in terms of the success rates and runtimes over ECBS on five different types of grid maps from the MAPF benchmark.

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SoCS Conference 2021 Conference Paper

Learning Pseudo-Backdoors for Mixed Integer Programs

  • Aaron M. Ferber
  • Jialin Song
  • Bistra Dilkina
  • Yisong Yue

We propose a machine learning approach for quickly solving Mixed Integer Programs (MIP) by learning to prioritize a set of decision variables, which we call pseudo-backdoors, for branching that results in faster solution times. Learning-based approaches have seen success in the area of solving combinatorial optimization problems by being able to flexibly leverage common structures in a given distribution of problems. Our approach takes inspiration from the concept of strong backdoors, which corresponds to a small set of variables such that only branching on these variables yields an optimal integral solution and a proof of optimality. Our notion of pseudo-backdoors corresponds to a small set of variables such that only branching on them leads to faster solve time (which can be solver dependent). A key advantage of pseudo-backdoors over strong backdoors is that they are much amenable to data-driven identification or prediction. Our proposed method learns to estimate the solver performance of a proposed pseudo-backdoor, using a labeled dataset collected on a set of training MIP instances. This model can then be used to identify high-quality pseudo-backdoors on new MIP instances from the same distribution. We evaluate our method on the generalized independent set problems and find that our approach can efficiently identify high-quality pseudo-backdoors. In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.

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AAAI Conference 2021 Conference Paper

Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search

  • Taoan Huang
  • Sven Koenig
  • Bistra Dilkina

Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent path finding. On the high level, CBS repeatedly detects conflicts and resolves one of them by splitting the current problem into two subproblems. Previous work chooses the conflict to resolve by categorizing conflicts into three classes and always picking one from the highest-priority class. In this work, we propose an oracle for conflict selection that results in smaller search tree sizes than the one used in previous work. However, the computation of the oracle is slow. Thus, we propose a machine-learning (ML) framework for conflict selection that observes the decisions made by the oracle and learns a conflict-selection strategy represented by a linear ranking function that imitates the oracle’s decisions accurately and quickly. Experiments on benchmark maps indicate that our approach, ML-guided CBS, significantly improves the success rates, search tree sizes and runtimes of the current state-of-the-art CBS solver.

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NeurIPS Conference 2020 Conference Paper

A General Large Neighborhood Search Framework for Solving Integer Linear Programs

  • Jialin Song
  • ravi lanka
  • Yisong Yue
  • Bistra Dilkina

This paper studies how to design abstractions of large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general-purpose ways, and that are amenable to data-driven design. The goal is to arrive at new approaches that can reliably outperform existing solvers in wall-clock time. We focus on solving integer programs and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic approaches and their software implementations. We also show that one can learn a good neighborhood selector from training data. Through an extensive empirical validation, we demonstrate that our LNS framework can significantly outperform, in wall-clock time, compared to state-of-the-art commercial solvers such as Gurobi.

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IJCAI Conference 2020 Conference Paper

Embedding Conjugate Gradient in Learning Random Walks for Landscape Connectivity Modeling in Conservation

  • Pramith Devulapalli
  • Bistra Dilkina
  • Yexiang Xue

Models capturing parameterized random walks on graphs have been widely adopted in wildlife conservation to study species dispersal as a function of landscape features. Learning the probabilistic model empowers ecologists to understand animal responses to conservation strategies. By exploiting the connection between random walks and simple electric networks, we show that learning a random walk model can be reduced to finding the optimal graph Laplacian for a circuit. We propose a moment matching strategy that correlates the model’s hitting and commuting times with those observed empirically. To find the best Laplacian, we propose a neural network capable of back-propagating gradients through the matrix inverse in an end-to-end fashion. We developed a scalable method called CGInv which back-propagates the gradients through a neural network encoding each layer as a conjugate gradient iteration. To demonstrate its effectiveness, we apply our computational framework to applications in landscape connectivity modeling. Our experiments successfully demonstrate that our framework effectively and efficiently recovers the ground-truth configurations.

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AAAI Conference 2020 Conference Paper

End-to-End Game-Focused Learning of Adversary Behavior in Security Games

  • Andrew Perrault
  • Bryan Wilder
  • Eric Ewing
  • Aditya Mate
  • Bistra Dilkina
  • Milind Tambe

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.

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AAAI Conference 2020 Conference Paper

Human-Machine Collaboration for Fast Land Cover Mapping

  • Caleb Robinson
  • Anthony Ortiz
  • Kolya Malkin
  • Blake Elias
  • Andi Peng
  • Dan Morris
  • Bistra Dilkina
  • Nebojsa Jojic

We propose incorporating human labelers in a model finetuning system that provides immediate user feedback. In our framework, human labelers can interactively query model predictions on unlabeled data, choose which data to label, and see the resulting effect on the model’s predictions. This bi-directional feedback loop allows humans to learn how the model responds to new data. We implement this framework for fine-tuning high-resolution land cover segmentation models and compare human-selected points to points selected using standard active learning methods. Specifically, we fine-tune a deep neural network – trained to segment highresolution aerial imagery into different land cover classes in Maryland, USA – to a new spatial area in New York, USA using both our human-in-the-loop method and traditional active learning methods. The tight loop in our proposed system turns the algorithm and the human operator into a hybrid system that can produce land cover maps of large areas more efficiently than the traditional workflows. Our framework has applications in machine learning settings where there is a practically limitless supply of unlabeled data, of which only a small fraction can feasibly be labeled through human efforts, such as geospatial and medical image-based applications.

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AAAI Conference 2020 Conference Paper

To Signal or Not To Signal: Exploiting Uncertain Real-Time Information in Signaling Games for Security and Sustainability

  • Elizabeth Bondi
  • Hoon Oh
  • Haifeng Xu
  • Fei Fang
  • Bistra Dilkina
  • Milind Tambe

Motivated by real-world deployment of drones for conservation, this paper advances the state-of-the-art in security games with signaling. The well-known defender-attacker security games framework can help in planning for such strategic deployments of sensors and human patrollers, and warning signals to ward off adversaries. However, we show that defenders can suffer significant losses when ignoring real-world uncertainties despite carefully planned security game strategies with signaling. In fact, defenders may perform worse than forgoing drones completely in this case. We address this shortcoming by proposing a novel game model that integrates signaling and sensor uncertainty; perhaps surprisingly, we show that defenders can still perform well via a signaling strategy that exploits uncertain real-time information. For example, even in the presence of uncertainty, the defender still has an informational advantage in knowing that she has or has not actually detected the attacker; and she can design a signaling scheme to “mislead” the attacker who is uncertain as to whether he has been detected. We provide theoretical results, a novel algorithm, scale-up techniques, and experimental results from simulation based on our ongoing deployment of a conservation drone system in South Africa.

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AAMAS Conference 2019 Conference Paper

Broken Signals in Security Games: Coordinating Patrollers and Sensors in the Real World

  • Elizabeth Bondi
  • Hoon Oh
  • Haifeng Xu
  • Fei Fang
  • Bistra Dilkina
  • Milind Tambe

Mobile sensors, e. g. , unmanned aerial vehicles (UAVs), are becoming increasingly important in security domains and can be used for tasks such as searching for poachers in conservation areas. Such mobile sensors augment human patrollers by assisting in surveillance and in signaling potentially deceptive information to adversaries, and their coordinated deployment could be modeled via the well-known security games framework. Unfortunately, real-world uncertainty in the sensor’s detection of adversaries and adversaries’ observation of the sensor’s signals present major challenges in the sensors’ use. This leads to significant detriments in security performance. We first discuss the current shortcomings in more detail, and then propose a novel game model that incorporates uncertainty with sensors. The defender strategy in this game model will consist of three interdependent stages: an allocation stage, a signaling stage, and a reaction stage.

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AAMAS Conference 2019 Conference Paper

Don't Put All Your Strategies in One Basket: Playing Green Security Games with Imperfect Prior Knowledge

  • Shahrzad Gholami
  • Amulya Yadav
  • Long Tran-Thanh
  • Bistra Dilkina
  • Milind Tambe

Security efforts for wildlife monitoring and protection of endangered species (e. g. , elephants, rhinos, etc.) are constrained by limited resources available to law enforcement agencies. Recent progress in Green Security Games (GSGs) has led to patrol planning algorithms for strategic allocation of limited patrollers to deter adversaries in environmental settings. Unfortunately, previous approaches to these problems suffer from several limitations. Most notably, (i) previous work in GSG literature relies on exploitation of error-prone machine learning (ML) models of poachers’ behavior trained on (spatially) biased historical data; and (ii) online learning approaches for repeated security games (similar to GSGs) do not account for spatio-temporal scheduling constraints while planning patrols, potentially causing significant shortcomings in the effectiveness of the planned patrols. Thus, this paper makes the following novel contributions: (I) We propose MINION-sm, a novel online learning algorithm for GSGs which does not rely on any prior error-prone model of attacker behavior, instead, it builds an implicit model of the attacker on-the-fly while simultaneously generating schedulingconstraint-aware patrols. MINION-sm achieves a sublinear regret against an optimal hindsight patrol strategy. (II) We also propose MINION, a hybrid approach where our MINION-sm model and an ML model (based on historical data) are considered as two patrol planning experts and we obtain a balance between them based on their observed empirical performance. (III) We show that our online learning algorithms significantly outperform existing state-of-theart solvers for GSGs.

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NeurIPS Conference 2019 Conference Paper

End to end learning and optimization on graphs

  • Bryan Wilder
  • Eric Ewing
  • Bistra Dilkina
  • Milind Tambe

Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communities (or solve other common graph optimization problems such as facility location, maxcut, and so on). However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization. Standard approaches treat learning and optimization entirely separately, while recent machine learning work aims to predict the optimal solution directly from the inputs. Here, we propose an alternative decision-focused learning approach that integrates a differentiable proxy for common graph optimization problems as a layer in learned systems. The main idea is to learn a representation that maps the original optimization problem onto a simpler proxy problem that can be efficiently differentiated through. Experimental results show that our ClusterNet system outperforms both pure end-to-end approaches (that directly predict the optimal solution) and standard approaches that entirely separate learning and optimization. Code for our system is available at https: //github. com/bwilder0/clusternet.

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AAAI Conference 2019 Conference Paper

Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

  • Bryan Wilder
  • Bistra Dilkina
  • Milind Tambe

Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to predictive models, to decisions. These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision. However, the loss function used to train the model may easily be misaligned with the end goal, which is to make the best decisions possible. Hand-tuning the loss function to align with optimization is a difficult and error-prone process (which is often skipped entirely). We focus on combinatorial optimization problems and introduce a general framework for decision-focused learning, where the machine learning model is directly trained in conjunction with the optimization algorithm to produce highquality decisions. Technically, our contribution is a means of integrating common classes of discrete optimization problems into deep learning or other predictive models, which are typically trained via gradient descent. The main idea is to use a continuous relaxation of the discrete problem to propagate gradients through the optimization procedure. We instantiate this framework for two broad classes of combinatorial problems: linear programs and submodular maximization. Experimental results across a variety of domains show that decisionfocused learning often leads to improved optimization performance compared to traditional methods. We find that standard measures of accuracy are not a reliable proxy for a predictive model’s utility in optimization, and our method’s ability to specify the true goal as the model’s training objective yields substantial dividends across a range of decision problems.

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AAMAS Conference 2019 Conference Paper

Using Game Theory in Real Time in the Real World: A Conservation Case Study

  • Elizabeth Bondi
  • Hoon Oh
  • Haifeng Xu
  • Fei Fang
  • Bistra Dilkina
  • Milind Tambe

In the real world, real-time data are now widely available, especially in security domains. Security cameras, aerial imagery, and even social media keep defenders informed when protecting important events, locations, and people. Further, advances in artificial intelligence have led to tools that can interpret these data automatically. Game theoretic models, for example, have shown great success in security. However, most of them ignore real-time information. In this paper, we demonstrate the potential to use real-time information from imagery to better inform our decisions in game theoretic models for security. As a concrete example, a conservation group called Air Shepherd uses conservation drones equipped with thermal infrared cameras to locate poachers at night and alert park rangers. They have also used lights aboard the drones, or signaled, to warn poachers of their presence, which often deters the poachers. We propose a system that (i) allocates drones and humans strategically throughout a protected area, (ii) detects poachers in the thermal infrared videos recorded by the conservation drones flying through the protected area in the predetermined location, and (iii) recommends moving to the location and/or signaling to the poacher that a patroller is nearby depending on real-time detections. View the demonstration: http: //bit. ly/aamas19-demo-bondi-et-al.

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AAMAS Conference 2018 Conference Paper

Adversary Models Account for Imperfect Crime Data: Forecasting and Planning against Real-world Poachers

  • Shahrzad Gholami
  • Sara Mc Carthy
  • Bistra Dilkina
  • Andrew Plumptre
  • Milind Tambe
  • Margaret Driciru
  • Fred Wanyama
  • Aggrey Rwetsiba

Poachers are engaged in extinction level wholesale slaughter, so it is critical to harness historical data for predicting poachers’ behavior. However, in these domains, data collected about adversarial actions are remarkably imperfect, where reported negative instances of crime may be mislabeled or uncertain. Unfortunately, past attempts to develop predictive and prescriptive models to address this problem suffer from shortcomings from a modeling perspective as well as in the implementability of their techniques. Most notably these models i) neglect the uncertainty in crime data, leading to inaccurate and biased predictions of adversary behavior, ii) use coarse-grained crime analysis and iii) do not provide a convincing evaluation as they only look at a single protected area. Additionally, they iv) proposed time-consuming techniques which cannot be directly integrated into low resource outposts. In this innovative application paper, we (I) introduce iWare-E a novel imperfect-observation aWare Ensemble (iWare-E) technique1, which is designed to handle the uncertainty in crime information efficiently. This approach leads to superior accuracy for adversary behavior prediction (up to 34% increase in AUC) compared to the previous state-of-the-art. We also demonstrate the country-wide efficiency of the models and are the first to (II) evaluate our adversary behavioral model across different protected areas in Uganda, i. e. , Murchison Fall and Queen Elizabeth National Park, (totaling about 7500 square km) as well as (III) on fine-grained temporal resolutions. Lastly, (IV) we provide a scalable planning algorithm to design fine-grained patrol routes for the rangers, which achieves up to 150% improvement in number of predicted attacks detected.

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IJCAI Conference 2018 Conference Paper

Discrete Interventions in Hawkes Processes with Applications in Invasive Species Management

  • Amrita Gupta
  • Mehrdad Farajtabar
  • Bistra Dilkina
  • Hongyuan Zha

The spread of invasive species to new areas threatens the stability of ecosystems and causes major economic losses. We propose a novel approach to minimize the spread of an invasive species given a limited intervention budget. We first model invasive species spread using Hawkes processes, and then derive closed-form expressions for characterizing the effect of an intervention action on the invasion process. We use this to obtain an optimal intervention plan based on an integer programming formulation, and compare the optimal plan against several ecologically-motivated heuristic strategies used in practice. We present an empirical study of two variants of the invasive control problem: minimizing the final rate of invasions, and minimizing the number of invasions at the end of a given time horizon. The optimized intervention achieves nearly the same level of control that would be attained by completely eradicating the species, but at only 60-80\% of the cost.

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AAAI Conference 2017 Conference Paper

Dynamic Optimization of Landscape Connectivity Embedding Spatial-Capture-Recapture Information

  • Yexiang Xue
  • Xiaojian Wu
  • Dana Morin
  • Bistra Dilkina
  • Angela Fuller
  • J. Royle
  • Carla Gomes

Maintaining landscape connectivity is increasingly important in wildlife conservation, especially for species experiencing the effects of habitat loss and fragmentation. We propose a novel approach to dynamically optimize landscape connectivity. Our approach is based on a mixed integer program formulation, embedding a spatial capture-recapture model that estimates the density, space usage, and landscape connectivity for a given species. Our method takes into account the fact that local animal density and connectivity change dynamically and non-linearly with different habitat protection plans. In order to scale up our encoding, we propose a sampling scheme via random partitioning of the search space using parity functions. We show that our method scales to realworld size problems and dramatically outperforms the solution quality of an expectation maximization approach and a sample average approximation approach.

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NeurIPS Conference 2017 Conference Paper

Learning Combinatorial Optimization Algorithms over Graphs

  • Elias Khalil
  • Hanjun Dai
  • Yuyu Zhang
  • Bistra Dilkina
  • Le Song

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error. Can we automate this challenging, tedious process, and learn the algorithms instead? In many real-world applications, it is typically the case that the same optimization problem is solved again and again on a regular basis, maintaining the same problem structure but differing in the data. This provides an opportunity for learning heuristic algorithms that exploit the structure of such recurring problems. In this paper, we propose a unique combination of reinforcement learning and graph embedding to address this challenge. The learned greedy policy behaves like a meta-algorithm that incrementally constructs a solution, and the action is determined by the output of a graph embedding network capturing the current state of the solution. We show that our framework can be applied to a diverse range of optimization problems over graphs, and learns effective algorithms for the Minimum Vertex Cover, Maximum Cut and Traveling Salesman problems.

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IJCAI Conference 2017 Conference Paper

Learning to Run Heuristics in Tree Search

  • Elias B. Khalil
  • Bistra Dilkina
  • George L. Nemhauser
  • Shabbir Ahmed
  • Yufen Shao

``Primal heuristics'' are a key contributor to the improved performance of exact branch-and-bound solvers for combinatorial optimization and integer programming. Perhaps the most crucial question concerning primal heuristics is that of at which nodes they should run, to which the typical answer is via hard-coded rules or fixed solver parameters tuned, offline, by trial-and-error. Alternatively, a heuristic should be run when it is most likely to succeed, based on the problem instance's characteristics, the state of the search, etc. In this work, we study the problem of deciding at which node a heuristic should be run, such that the overall (primal) performance of the solver is optimized. To our knowledge, this is the first attempt at formalizing and systematically addressing this problem. Central to our approach is the use of Machine Learning (ML) for predicting whether a heuristic will succeed at a given node. We give a theoretical framework for analyzing this decision-making process in a simplified setting, propose a ML approach for modeling heuristic success likelihood, and design practical rules that leverage the ML models to dynamically decide whether to run a heuristic at each node of the search tree. Experimentally, our approach improves the primal performance of a state-of-the-art Mixed Integer Programming solver by up to 6% on a set of benchmark instances, and by up to 60% on a family of hard Independent Set instances.

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AAAI Conference 2016 Conference Paper

Learning to Branch in Mixed Integer Programming

  • Elias Khalil
  • Pierre Le Bodic
  • Le Song
  • George Nemhauser
  • Bistra Dilkina

The design of strategies for branching in Mixed Integer Programming (MIP) is guided by cycles of parameter tuning and offline experimentation on an extremely heterogeneous testbed, using the average performance. Once devised, these strategies (and their parameter settings) are essentially input-agnostic. To address these issues, we propose a machine learning (ML) framework for variable branching in MIP. Our method observes the decisions made by Strong Branching (SB), a time-consuming strategy that produces small search trees, collecting features that characterize the candidate branching variables at each node of the tree. Based on the collected data, we learn an easy-to-evaluate surrogate function that mimics the SB strategy, by means of solving a learning-to-rank problem, common in ML. The learned ranking function is then used for branching. The learning is instance-specific, and is performed on-the-fly while executing a branch-and-bound search to solve the instance. Experiments on benchmark instances indicate that our method produces significantly smaller search trees than existing heuristics, and is competitive with a state-of-the-art commercial solver.

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AAAI Conference 2015 Conference Paper

Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa

  • Stefano Ermon
  • Yexiang Xue
  • Russell Toth
  • Bistra Dilkina
  • Richard Bernstein
  • Theodoros Damoulas
  • Patrick Clark
  • Steve DeGloria

Understanding spatio-temporal resource preferences is paramount in the design of policies for sustainable development. Unfortunately, resource preferences are often unknown to policy-makers and have to be inferred from data. In this paper we consider the problem of inferring agents’ preferences from observed movement trajectories, and formulate it as an Inverse Reinforcement Learning (IRL) problem. With the goal of informing policy-making, we take a probabilistic approach and consider generative models that can be used to simulate behavior under new circumstances such as changes in resource availability, access policies, or climate. We study the Dynamic Discrete Choice (DDC) models from econometrics and prove that they generalize the Max-Entropy IRL model, a widely used probabilistic approach from the machine learning literature. Furthermore, we develop SPL-GD, a new learning algorithm for DDC models that is considerably faster than the state of the art and scales to very large datasets. We consider an application in the context of pastoralism in the arid and semi-arid regions of Africa, where migratory pastoralists face regular risks due to resource availability, droughts, and resource degradation from climate change and development. We show how our approach based on satellite and survey data can accurately model migratory pastoralism in East Africa and that it considerably outperforms other approaches on a largescale real-world dataset of pastoralists’ movements in Ethiopia collected over 3 years.

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AAAI Conference 2013 Conference Paper

Large Landscape Conservation — Synthetic and Real-World Datasets

  • Bistra Dilkina
  • Katherine Lai
  • Ronan Le Bras
  • Yexiang Xue
  • Carla Gomes
  • Ashish Sabharwal
  • Jordan Suter
  • Kevin McKelvey

Biodiversity underpins ecosystem goods and services and hence protecting it is key to achieving sustainability. However, the persistence of many species is threatened by habitat loss and fragmentation due to human land use and climate change. Conservation efforts are implemented under very limited economic resources, and therefore designing scalable, cost-efficient and systematic approaches for conservation planning is an important and challenging computational task. In particular, preserving landscape connectivity between good habitat has become a key conservation priority in recent years. We give an overview of landscape connectivity conservation and some of the underlying graph-theoretic optimization problems. We present a synthetic generator capable of creating families of randomized structured problems, capturing the essential features of real-world instances but allowing for a thorough typical-case performance evaluation of different solution methods. We also present two large-scale real-world datasets, including economic data on land cost, and species data for grizzly bears, wolverines and lynx.

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AAAI Conference 2013 Conference Paper

Robust Network Design For Multispecies Conservation

  • Ronan Le Bras
  • Bistra Dilkina
  • Yexiang Xue
  • Carla Gomes
  • Kevin McKelvey
  • Michael Schwartz
  • Claire Montgomery

Our work is motivated by an important network design application in computational sustainability concerning wildlife conservation. In the face of human development and climate change, it is important that conservation plans for protecting landscape connectivity exhibit certain level of robustness. While previous work has focused on conservation strategies that result in a connected network of habitat reserves, the robustness of the proposed solutions has not been taken into account. In order to address this important aspect, we formalize the problem as a node-weighted bi-criteria network design problem with connectivity requirements on the number of disjoint paths between pairs of nodes. While in most previous work on survivable network design the objective is to minimize the cost of the selected network, our goal is to optimize the quality of the selected paths within a specified budget, while meeting the connectivity requirements. We characterize the complexity of the problem under different restrictions. We provide a mixed-integer programming encoding that allows for finding solutions with optimality guarantees, as well as a hybrid local search method with better scaling behavior but no guarantees. We evaluate the typical-case performance of our approaches using a synthetic benchmark, and apply them to a large-scale real-world network design problem concerning the conservation of wolverine and lynx populations in the U. S. Rocky Mountains (Montana).

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UAI Conference 2010 Conference Paper

Maximizing the Spread of Cascades Using Network Design

  • Daniel Sheldon
  • Bistra Dilkina
  • Adam N. Elmachtoub
  • Ryan Finseth
  • Ashish Sabharwal
  • Jon Conrad
  • Carla P. Gomes
  • David B. Shmoys

We introduce a new optimization framework to maximize the expected spread of cascades in networks. Our model allows a rich set of actions that directly manipulate cascade dynamics by adding nodes or edges to the network. Our motivating application is one in spatial conservation planning, where a cascade models the dispersal of wild animals through a fragmented landscape. We propose a mixed integer programming (MIP) formulation that combines elements from network design and stochastic optimization. Our approach results in solutions with stochastic optimality guarantees and points to conservation strategies that are fundamentally different from naive approaches.

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SAT Conference 2009 Conference Paper

Backdoors in the Context of Learning

  • Bistra Dilkina
  • Carla P. Gomes
  • Ashish Sabharwal

Abstract The concept of backdoor variables has been introduced as a structural property of combinatorial problems that provides insight into the surprising ability of modern satisfiability (SAT) solvers to tackle extremely large instances. This concept is, however, oblivious to “learning” during search—a key feature of successful combinatorial reasoning engines for SAT, mixed integer programming (MIP), etc. We extend the notion of backdoors to the context of learning during search. We prove that the smallest backdoors for SAT that take into account clause learning and order-sensitivity of branching can be exponentially smaller than “traditional” backdoors. We also study the effect of learning empirically.

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AAAI Conference 2007 Conference Paper

The Impact of Network Topology on Pure Nash Equilibria in Graphical Games

  • Bistra Dilkina

Graphical games capture some of the key aspects relevant to the study and design of multi-agent systems. It is often of interest to find the conditions under which a game is stable, i. e. , the players have reached a consensus on their actions. In this paper, we characterize how different topologies of the interaction network affect the probability of existence of a pure Nash equilibrium in a graphical game with random payoffs. We show that for tree topologies with unbounded diameter the probability of a pure Nash equilibrium vanishes as the number of players grows large. On the positive side, we de- fine several families of graphs for which the probability of a pure Nash equilibrium is at least 1 − 1 /e even as the number of players goes to infinity. We also empirically show that adding a small number of connection “shortcuts” can increase the probability of pure Nash.

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