paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v35i13.17379 | Outlier Impact Characterization for Time Series Data | https://ojs.aaai.org/index.php/AAAI/article/view/17379 | https://ojs.aaai.org/index.php/AAAI/article/download/17379/17186 | [
"Jianbo Li",
"Lecheng Zheng",
"Yada Zhu",
"Jingrui He"
] | For time series data, certain types of outliers are intrinsically more harmful for parameter estimation and future predictions than others, irrespective of their frequency. In this paper, for the first time, we study the characteristics of such outliers through the lens of the influence functional from robust statistic... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17379 | 35 | 13 | 11595-11603 | official | null | null |
10.1609/aaai.v35i13.17378 | How RL Agents Behave When Their Actions Are Modified | https://ojs.aaai.org/index.php/AAAI/article/view/17378 | https://ojs.aaai.org/index.php/AAAI/article/download/17378/17185 | [
"Eric D. Langlois",
"Tom Everitt"
] | Reinforcement learning in complex environments may require supervision to prevent the agent from attempting dangerous actions. As a result of supervisor intervention, the executed action may differ from the action specified by the policy. How does this affect learning? We present the Modified-Action Markov Decision Pro... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17378 | 35 | 13 | 11586-11594 | official | 2102.07716 | title_snapshot |
10.1609/aaai.v35i13.17377 | On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17377 | https://ojs.aaai.org/index.php/AAAI/article/download/17377/17184 | [
"Eoin M. Kenny",
"Mark T Keane"
] | There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet, counterfactual explanations have become very popular in eXplainable AI (X... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17377 | 35 | 13 | 11575-11585 | official | 2009.06399 | title_snapshot |
10.1609/aaai.v35i13.17376 | Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/17376 | https://ojs.aaai.org/index.php/AAAI/article/download/17376/17183 | [
"Kentaro Kanamori",
"Takuya Takagi",
"Ken Kobayashi",
"Yuichi Ike",
"Kento Uemura",
"Hiroki Arimura"
] | Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result. Given a perturbatio... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17376 | 35 | 13 | 11564-11574 | official | 2012.11782 | title_snapshot |
10.1609/aaai.v35i13.17374 | Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization | https://ojs.aaai.org/index.php/AAAI/article/view/17374 | https://ojs.aaai.org/index.php/AAAI/article/download/17374/17181 | [
"Shir Gur",
"Ameen Ali",
"Lior Wolf"
] | Neural network visualization techniques mark image locations by their relevancy to the network's classification. Existing methods are effective in highlighting the regions that affect the resulting classification the most. However, as we show, these methods are limited in their ability to identify the support for alter... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17374 | 35 | 13 | 11545-11554 | official | 2012.02166 | title_snapshot |
10.1609/aaai.v35i13.17363 | Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise | https://ojs.aaai.org/index.php/AAAI/article/view/17363 | https://ojs.aaai.org/index.php/AAAI/article/download/17363/17170 | [
"Pengfei Chen",
"Junjie Ye",
"Guangyong Chen",
"Jingwei Zhao",
"Pheng-Ann Heng"
] | Supervised learning under label noise has seen numerous advances recently, while existing theoretical findings and empirical results broadly build up on the class-conditional noise (CCN) assumption that the noise is independent of input features given the true label. In this work, we present a theoretical hypothesis te... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17363 | 35 | 13 | 11442-11450 | official | 2012.05458 | title_snapshot |
10.1609/aaai.v35i13.17364 | Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels | https://ojs.aaai.org/index.php/AAAI/article/view/17364 | https://ojs.aaai.org/index.php/AAAI/article/download/17364/17171 | [
"Pengfei Chen",
"Junjie Ye",
"Guangyong Chen",
"Jingwei Zhao",
"Pheng-Ann Heng"
] | For multi-class classification under class-conditional label noise, we prove that the accuracy metric itself can be robust. We concretize this finding's inspiration in two essential aspects: training and validation, with which we address critical issues in learning with noisy labels. For training, we show that maximizi... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17364 | 35 | 13 | 11451-11461 | official | 2012.04193 | title_snapshot |
10.1609/aaai.v35i13.17365 | A Unified Taylor Framework for Revisiting Attribution Methods | https://ojs.aaai.org/index.php/AAAI/article/view/17365 | https://ojs.aaai.org/index.php/AAAI/article/download/17365/17172 | [
"Huiqi Deng",
"Na Zou",
"Mengnan Du",
"Weifu Chen",
"Guocan Feng",
"Xia Hu"
] | Attribution methods have been developed to understand the decision making process of machine learning models, especially deep neural networks, by assigning importance scores to individual features. Existing attribution methods often built upon empirical intuitions and heuristics. There still lacks a general and theoret... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17365 | 35 | 13 | 11462-11469 | official | 2008.09695 | title_snapshot |
10.1609/aaai.v35i13.17366 | Verifiable Machine Ethics in Changing Contexts | https://ojs.aaai.org/index.php/AAAI/article/view/17366 | https://ojs.aaai.org/index.php/AAAI/article/download/17366/17173 | [
"Louise A. Dennis",
"Martin Mose Bentzen",
"Felix Lindner",
"Michael Fisher"
] | Many systems proposed for the implementation of ethical reasoning involve an encoding of user values as a set of rules or a model. We consider the question of how changes of context affect these encodings. We propose the use of a reasoning cycle, in which information about the ethical reasoner's context is imported in ... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17366 | 35 | 13 | 11470-11478 | official | null | null |
10.1609/aaai.v35i13.17367 | Epistemic Logic of Know-Who | https://ojs.aaai.org/index.php/AAAI/article/view/17367 | https://ojs.aaai.org/index.php/AAAI/article/download/17367/17174 | [
"Sophia Epstein",
"Pavel Naumov"
] | The paper suggests a definition of "know who" as a modality using Grove-Halpern semantics of names. It also introduces a logical system that describes the interplay between modalities "knows who", "knows", and "for all agents". The main technical result is a completeness theorem for the proposed system. | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17367 | 35 | 13 | 11479-11486 | official | 2012.06651 | title_snapshot |
10.1609/aaai.v35i13.17368 | Agent Incentives: A Causal Perspective | https://ojs.aaai.org/index.php/AAAI/article/view/17368 | https://ojs.aaai.org/index.php/AAAI/article/download/17368/17175 | [
"Tom Everitt",
"Ryan Carey",
"Eric D. Langlois",
"Pedro A. Ortega",
"Shane Legg"
] | We present a framework for analysing agent incentives using causal influence diagrams. We establish that a well-known criterion for value of information is complete. We propose a new graphical criterion for value of control, establishing its soundness and completeness. We also introduce two new concepts for incentive a... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17368 | 35 | 13 | 11487-11495 | official | 2102.01685 | title_snapshot |
10.1609/aaai.v35i13.17369 | Individual Fairness in Kidney Exchange Programs | https://ojs.aaai.org/index.php/AAAI/article/view/17369 | https://ojs.aaai.org/index.php/AAAI/article/download/17369/17176 | [
"Golnoosh Farnadi",
"William St-Arnaud",
"Behrouz Babaki",
"Margarida Carvalho"
] | Kidney transplant is the preferred method of treatment for patients suffering from kidney failure. However, not all patients can find a donor which matches their physiological characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the main objective o... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17369 | 35 | 13 | 11496-11505 | official | null | null |
10.1609/aaai.v35i13.17370 | Fair Representations by Compression | https://ojs.aaai.org/index.php/AAAI/article/view/17370 | https://ojs.aaai.org/index.php/AAAI/article/download/17370/17177 | [
"Xavier Gitiaux",
"Huzefa Rangwala"
] | Organizations that collect and sell data face increasing scrutiny for the discriminatory use of data. We propose a novel unsupervised approach to map data into a compressed binary representation independent of sensitive attributes. We show that in an information bottleneck framework, a parsimonious representation shoul... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17370 | 35 | 13 | 11506-11515 | official | 2105.14044 | title_snapshot |
10.1609/aaai.v35i13.17371 | Amnesiac Machine Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17371 | https://ojs.aaai.org/index.php/AAAI/article/download/17371/17178 | [
"Laura Graves",
"Vineel Nagisetty",
"Vijay Ganesh"
] | The Right to be Forgotten is part of the recently enacted General Data Protection Regulation (GDPR) law that affects any data holder that has data on European Union residents. It gives EU residents the ability to request deletion of their personal data, including training records used to train machine learning models. ... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17371 | 35 | 13 | 11516-11524 | official | 2010.10981 | title_snapshot |
10.1609/aaai.v35i13.17372 | On the Verification of Neural ODEs with Stochastic Guarantees | https://ojs.aaai.org/index.php/AAAI/article/view/17372 | https://ojs.aaai.org/index.php/AAAI/article/download/17372/17179 | [
"Sophie Grunbacher",
"Ramin Hasani",
"Mathias Lechner",
"Jacek Cyranka",
"Scott A. Smolka",
"Radu Grosu"
] | We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems. For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an abstraction-based technique for constructing a tight Reachtube (an over-approximation of the set o... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17372 | 35 | 13 | 11525-11535 | official | 2012.08863 | title_snapshot |
10.1609/aaai.v35i13.17373 | PenDer: Incorporating Shape Constraints via Penalized Derivatives | https://ojs.aaai.org/index.php/AAAI/article/view/17373 | https://ojs.aaai.org/index.php/AAAI/article/download/17373/17180 | [
"Akhil Gupta",
"Lavanya Marla",
"Ruoyu Sun",
"Naman Shukla",
"Arinbjörn Kolbeinsson"
] | When deploying machine learning models in the real-world, system designers may wish that models exhibit certain shape behavior, i.e., model outputs follow a particular shape with respect to input features. Trends such as monotonicity, convexity, diminishing or accelerating returns are some of the desired shapes. Presen... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17373 | 35 | 13 | 11536-11544 | official | null | null |
10.1609/aaai.v35i13.17358 | Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach | https://ojs.aaai.org/index.php/AAAI/article/view/17358 | https://ojs.aaai.org/index.php/AAAI/article/download/17358/17165 | [
"Seojin Bang",
"Pengtao Xie",
"Heewook Lee",
"Wei Wu",
"Eric Xing"
] | Interpretable machine learning has gained much attention recently. Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system. However, existing interpretable machine learning methods fail to consider briefness and comprehensiven... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17358 | 35 | 13 | 11396-11404 | official | 1902.06918 | title_judge |
10.1609/aaai.v35i13.17362 | FIMAP: Feature Importance by Minimal Adversarial Perturbation | https://ojs.aaai.org/index.php/AAAI/article/view/17362 | https://ojs.aaai.org/index.php/AAAI/article/download/17362/17169 | [
"Matt Chapman-Rounds",
"Umang Bhatt",
"Erik Pazos",
"Marc-Andre Schulz",
"Konstantinos Georgatzis"
] | Instance-based model-agnostic feature importance explanations (LIME, SHAP, L2X) are a popular form of algorithmic transparency. These methods generally return either a weighting or subset of input features as an explanation for the classification of an instance. An alternative literature argues instead that counterfact... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17362 | 35 | 13 | 11433-11441 | official | null | null |
10.1609/aaai.v35i13.17361 | Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example | https://ojs.aaai.org/index.php/AAAI/article/view/17361 | https://ojs.aaai.org/index.php/AAAI/article/download/17361/17168 | [
"Serena Booth",
"Yilun Zhou",
"Ankit Shah",
"Julie Shah"
] | Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response to inputs drawn from the test set. However, the test set may have few examples that trigger some model behaviors, such as high-confidence fai... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17361 | 35 | 13 | 11423-11432 | official | 2002.10248 | title_snapshot |
10.1609/aaai.v35i13.17360 | TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments | https://ojs.aaai.org/index.php/AAAI/article/view/17360 | https://ojs.aaai.org/index.php/AAAI/article/download/17360/17167 | [
"Tom Bewley",
"Jonathan Lawry"
] | In explainable artificial intelligence, there is increasing interest in understanding the behaviour of autonomous agents to build trust and validate performance. Modern agent architectures, such as those trained by deep reinforcement learning, are currently so lacking in interpretable structure as to effectively be bla... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17360 | 35 | 13 | 11415-11422 | official | 2009.04743 | title_snapshot |
10.1609/aaai.v35i13.17359 | Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork | https://ojs.aaai.org/index.php/AAAI/article/view/17359 | https://ojs.aaai.org/index.php/AAAI/article/download/17359/17166 | [
"Gagan Bansal",
"Besmira Nushi",
"Ece Kamar",
"Eric Horvitz",
"Daniel S. Weld"
] | AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously. However, in practice, AI systems often are used to provide advice to people in domains ranging from criminal justice and finance to healthcare. In such AI-advised decision... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17359 | 35 | 13 | 11405-11414 | official | 2004.13102 | title_snapshot |
10.1609/aaai.v35i13.17430 | Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems | https://ojs.aaai.org/index.php/AAAI/article/view/17430 | https://ojs.aaai.org/index.php/AAAI/article/download/17430/17237 | [
"Liang Xin",
"Wen Song",
"Zhiguang Cao",
"Jie Zhang"
] | We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) to train multiple diverse policies, which effectively increases the chance of finding good solutions compared with existing methods that tra... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17430 | 35 | 13 | 12042-12049 | official | 2012.10638 | title_snapshot |
10.1609/aaai.v35i13.17429 | Competitive Analysis for Two-Level Ski-Rental Problem | https://ojs.aaai.org/index.php/AAAI/article/view/17429 | https://ojs.aaai.org/index.php/AAAI/article/download/17429/17236 | [
"Binghan Wu",
"Wei Bao",
"Dong Yuan"
] | In this paper, we study a two-level ski-rental problem. There are multiple commodities, each one can be “rented” (paying for on-demand usage) or “purchased” (paying for life-time usage). There is also a combo purchase available so that all commodities can be purchased as a combo. Since the usages of the commodities in ... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17429 | 35 | 13 | 12034-12041 | official | null | null |
10.1609/aaai.v35i13.17428 | Asking the Right Questions: Learning Interpretable Action Models Through Query Answering | https://ojs.aaai.org/index.php/AAAI/article/view/17428 | https://ojs.aaai.org/index.php/AAAI/article/download/17428/17235 | [
"Pulkit Verma",
"Shashank Rao Marpally",
"Siddharth Srivastava"
] | This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a rudimentary query interface with the agent and a hierarchical querying algorithm that generates an inte... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17428 | 35 | 13 | 12024-12033 | official | 1912.12613 | title_snapshot |
10.1609/aaai.v35i13.17427 | Dynamic Automaton-Guided Reward Shaping for Monte Carlo Tree Search | https://ojs.aaai.org/index.php/AAAI/article/view/17427 | https://ojs.aaai.org/index.php/AAAI/article/download/17427/17234 | [
"Alvaro Velasquez",
"Brett Bissey",
"Lior Barak",
"Andre Beckus",
"Ismail Alkhouri",
"Daniel Melcer",
"George Atia"
] | Reinforcement learning and planning have been revolutionized in recent years, due in part to the mass adoption of deep convolutional neural networks and the resurgence of powerful methods to refine decision-making policies. However, the problem of sparse reward signals and their representation remains pervasive in many... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17427 | 35 | 13 | 12015-12023 | official | null | null |
10.1609/aaai.v35i13.17426 | On the Optimal Efficiency of A* with Dominance Pruning | https://ojs.aaai.org/index.php/AAAI/article/view/17426 | https://ojs.aaai.org/index.php/AAAI/article/download/17426/17233 | [
"Álvaro Torralba"
] | A well known result is that, given a consistent heuristic and no other source of information, A* does expand a minimal number of nodes up to tie-breaking. We extend this analysis for A* with dominance pruning, which exploits a dominance relation to eliminate some nodes during the search. We show that the expansion orde... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17426 | 35 | 13 | 12007-12014 | official | null | null |
10.1609/aaai.v35i13.17425 | Faster Stackelberg Planning via Symbolic Search and Information Sharing | https://ojs.aaai.org/index.php/AAAI/article/view/17425 | https://ojs.aaai.org/index.php/AAAI/article/download/17425/17232 | [
"Álvaro Torralba",
"Patrick Speicher",
"Robert Künnemann",
"Marcel Steinmetz",
"Jörg Hoffmann"
] | Stackelberg planning is a recent framework where a leader and a follower each choose a plan in the same planning task, the leader's objective being to maximize plan cost for the follower. This formulation naturally captures security-related (leader=defender, follower=attacker) as well as robustness-related (leader=adve... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17425 | 35 | 13 | 11998-12006 | official | null | null |
10.1609/aaai.v35i13.17424 | A Complexity-theoretic Analysis of Green Pickup-and-Delivery Problems | https://ojs.aaai.org/index.php/AAAI/article/view/17424 | https://ojs.aaai.org/index.php/AAAI/article/download/17424/17231 | [
"Xing Tan",
"Jimmy Xiangji Huang"
] | In a Green Pickup-and-Delivery problem (GPD), vehicles traveling in a transport network achieving pickup-and-delivery tasks are in particular subject to the two \textit{green} constraints: limited vehicle fuel capacity thus short vehicle traveling range, and limited availability of refueling infrastructure for the vehi... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17424 | 35 | 13 | 11990-11997 | official | null | null |
10.1609/aaai.v35i13.17423 | Online Action Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/17423 | https://ojs.aaai.org/index.php/AAAI/article/download/17423/17230 | [
"Alejandro Suárez-Hernández",
"Javier Segovia-Aguas",
"Carme Torras",
"Guillem Alenyà"
] | Recognition in planning seeks to find agent intentions, goals or activities given a set of observations and a knowledge library (e.g. goal states, plans or domain theories). In this work we introduce the problem of Online Action Recognition. It consists in recognizing, in an open world, the planning action that best ex... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17423 | 35 | 13 | 11981-11989 | official | 2012.07464 | title_snapshot |
10.1609/aaai.v35i13.17412 | Improved POMDP Tree Search Planning with Prioritized Action Branching | https://ojs.aaai.org/index.php/AAAI/article/view/17412 | https://ojs.aaai.org/index.php/AAAI/article/download/17412/17219 | [
"John Mern",
"Anil Yildiz",
"Lawrence Bush",
"Tapan Mukerji",
"Mykel J. Kochenderfer"
] | Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. This paper proposes a method called PA-POMCPOW to sample a subset of the action space that provides varying mixtures of exploitation and exploration for inclusion in a search tree. The propose... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17412 | 35 | 13 | 11888-11894 | official | 2010.03599 | title_snapshot |
10.1609/aaai.v35i13.17411 | Bayesian Optimized Monte Carlo Planning | https://ojs.aaai.org/index.php/AAAI/article/view/17411 | https://ojs.aaai.org/index.php/AAAI/article/download/17411/17218 | [
"John Mern",
"Anil Yildiz",
"Zachary Sunberg",
"Tapan Mukerji",
"Mykel J. Kochenderfer"
] | Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree. The performance of progressive widening searc... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17411 | 35 | 13 | 11880-11887 | official | 2010.03597 | title_snapshot |
10.1609/aaai.v35i13.17413 | Synthesis of Search Heuristics for Temporal Planning via Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17413 | https://ojs.aaai.org/index.php/AAAI/article/download/17413/17220 | [
"Andrea Micheli",
"Alessandro Valentini"
] | Automated temporal planning is the problem of synthesizing, starting from a model of a system, a course of actions to achieve a desired goal when temporal constraints, such as deadlines, are present in the problem. Despite considerable successes in the literature, scalability is still a severe limitation for existing p... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17413 | 35 | 13 | 11895-11902 | official | null | null |
10.1609/aaai.v35i13.17414 | Revealing Hidden Preconditions and Effects of Compound HTN Planning Tasks – A Complexity Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/17414 | https://ojs.aaai.org/index.php/AAAI/article/download/17414/17221 | [
"Conny Olz",
"Susanne Biundo",
"Pascal Bercher"
] | In Hierarchical Task Network (HTN) planning, compound tasks need to be refined into executable (primitive) action sequences. In contrast to their primitive counterparts, compound tasks do not specify preconditions or effects. Thus, their implications on the states in which they are applied are not explicitly known: the... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17414 | 35 | 13 | 11903-11912 | official | null | null |
10.1609/aaai.v35i13.17415 | Faster and Better Simple Temporal Problems | https://ojs.aaai.org/index.php/AAAI/article/view/17415 | https://ojs.aaai.org/index.php/AAAI/article/download/17415/17222 | [
"Dario Ostuni",
"Alice Raffaele",
"Romeo Rizzi",
"Matteo Zavatteri"
] | In this paper we give a structural characterization and extend the tractability frontier of the Simple Temporal Problem (STP) by defining the class of the Extended Simple Temporal Problem (ESTP), which augments STP with strict inequalities and monotone Boolean formulae on inequations (i.e., formulae involving the opera... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17415 | 35 | 13 | 11913-11920 | official | null | null |
10.1609/aaai.v35i13.17416 | Latent Independent Excitation for Generalizable Sensor-based Cross-Person Activity Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/17416 | https://ojs.aaai.org/index.php/AAAI/article/download/17416/17223 | [
"Hangwei Qian",
"Sinno Jialin Pan",
"Chunyan Miao"
] | In wearable-sensor-based activity recognition, it is often assumed that the training and test samples follow the same data distribution. This assumption neglects practical scenarios where the activity patterns inevitably vary from person to person. To solve this problem, transfer learning and domain adaptation approach... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17416 | 35 | 13 | 11921-11929 | official | null | null |
10.1609/aaai.v35i13.17417 | Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes | https://ojs.aaai.org/index.php/AAAI/article/view/17417 | https://ojs.aaai.org/index.php/AAAI/article/download/17417/17224 | [
"Marc Rigter",
"Bruno Lacerda",
"Nick Hawes"
] | The parameters for a Markov Decision Process (MDP) often cannot be specified exactly. Uncertain MDPs (UMDPs) capture this model ambiguity by defining sets which the parameters belong to. Minimax regret has been proposed as an objective for planning in UMDPs to find robust policies which are not overly conservative. In ... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17417 | 35 | 13 | 11930-11938 | official | 2012.04626 | title_snapshot |
10.1609/aaai.v35i13.17418 | An LP-Based Approach for Goal Recognition as Planning | https://ojs.aaai.org/index.php/AAAI/article/view/17418 | https://ojs.aaai.org/index.php/AAAI/article/download/17418/17225 | [
"Luísa R. A. Santos",
"Felipe Meneguzzi",
"Ramon Fraga Pereira",
"André Grahl Pereira"
] | Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent. In this paper, we develop a method based on the operator-counting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recogni... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17418 | 35 | 13 | 11939-11946 | official | 1905.04210 | title_snapshot |
10.1609/aaai.v35i13.17419 | Saturated Post-hoc Optimization for Classical Planning | https://ojs.aaai.org/index.php/AAAI/article/view/17419 | https://ojs.aaai.org/index.php/AAAI/article/download/17419/17226 | [
"Jendrik Seipp",
"Thomas Keller",
"Malte Helmert"
] | Saturated cost partitioning and post-hoc optimization are two powerful cost partitioning algorithms for optimal classical planning. The main idea of saturated cost partitioning is to give each considered heuristic only the fraction of remaining operator costs that it needs to prove its estimates. We show how to apply t... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17419 | 35 | 13 | 11947-11953 | official | null | null |
10.1609/aaai.v35i13.17420 | Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability | https://ojs.aaai.org/index.php/AAAI/article/view/17420 | https://ojs.aaai.org/index.php/AAAI/article/download/17420/17227 | [
"Shashank Shekhar",
"Ronen I. Brafman",
"Guy Shani"
] | Collaborative Multi-Agent Planning (MAP) problems with uncertainty and partial observability are often modeled as Dec-POMDPs. Yet, in deterministic domains, Qualitative Dec-POMDPs can scale up to much larger problem sizes. The best current QDec solver (QDec-FP) reduces MAP problems to multiple single-agent problems. In... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17420 | 35 | 13 | 11954-11961 | official | null | null |
10.1609/aaai.v35i13.17421 | Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17421 | https://ojs.aaai.org/index.php/AAAI/article/download/17421/17228 | [
"Tom Silver",
"Rohan Chitnis",
"Aidan Curtis",
"Joshua B. Tenenbaum",
"Tomás Lozano-Pérez",
"Leslie Pack Kaelbling"
] | Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient for finding a plan. We propose a graph neural network architecture fo... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17421 | 35 | 13 | 11962-11971 | official | 2009.05613 | title_snapshot |
10.1609/aaai.v35i13.17422 | Symbolic Search for Oversubscription Planning | https://ojs.aaai.org/index.php/AAAI/article/view/17422 | https://ojs.aaai.org/index.php/AAAI/article/download/17422/17229 | [
"David Speck",
"Michael Katz"
] | The objective of optimal oversubscription planning is to find a plan that yields an end state with a maximum utility while keeping plan cost under a certain bound. In practice, the situation occurs whenever a large number of possible, often competing goals of varying value exist, or the resources are not sufficient to ... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17422 | 35 | 13 | 11972-11980 | official | null | null |
10.1609/aaai.v35i13.17410 | Progression Heuristics for Planning with Probabilistic LTL Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/17410 | https://ojs.aaai.org/index.php/AAAI/article/download/17410/17217 | [
"Ian Mallett",
"Sylvie Thiebaux",
"Felipe Trevizan"
] | Probabilistic planning subject to multi-objective probabilistic temporal logic (PLTL) constraints models the problem of computing safe and robust behaviours for agents in stochastic environments. We present novel admissible heuristics to guide the search for cost-optimal policies for these problems. These heuristics pr... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17410 | 35 | 13 | 11870-11879 | official | null | null |
10.1609/aaai.v35i13.17409 | On-line Learning of Planning Domains from Sensor Data in PAL: Scaling up to Large State Spaces | https://ojs.aaai.org/index.php/AAAI/article/view/17409 | https://ojs.aaai.org/index.php/AAAI/article/download/17409/17216 | [
"Leonardo Lamanna",
"Alfonso Emilio Gerevini",
"Alessandro Saetti",
"Luciano Serafini",
"Paolo Traverso"
] | We propose an approach to learn an extensional representation of a discrete deterministic planning domain from observations in a continuous space navigated by the agent actions. This is achieved through the use of a perception function providing the likelihood of a real-value observation being in a given state of the p... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17409 | 35 | 13 | 11862-11869 | official | null | null |
10.1609/aaai.v35i13.17408 | Branch and Price for Bus Driver Scheduling with Complex Break Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/17408 | https://ojs.aaai.org/index.php/AAAI/article/download/17408/17215 | [
"Lucas Kletzander",
"Nysret Musliu",
"Pascal Van Hentenryck"
] | This paper presents a Branch and Price approach for a real-life Bus Driver Scheduling problem with a complex set of break constraints. The column generation uses a set partitioning model as master problem and a resource constrained shortest path problem as subproblem. Due to the complex constraints, the branch and pric... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17408 | 35 | 13 | 11853-11861 | official | null | null |
10.1609/aaai.v35i13.17407 | Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction | https://ojs.aaai.org/index.php/AAAI/article/view/17407 | https://ojs.aaai.org/index.php/AAAI/article/download/17407/17214 | [
"Jinjia Huang",
"Mabel C. Chou",
"Chung-Piaw Teo"
] | Motivated by the Bike Angels Program in New York's Citi Bike and Boston's Blue Bikes, we study the use of (registered) volunteers to re-position empty bikes for riders in a bike sharing system. We propose a method that can be used to deploy the volunteers in the system, based on the real time distribution of the bikes ... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17407 | 35 | 13 | 11844-11852 | official | null | null |
10.1609/aaai.v35i13.17406 | Endomorphisms of Classical Planning Tasks | https://ojs.aaai.org/index.php/AAAI/article/view/17406 | https://ojs.aaai.org/index.php/AAAI/article/download/17406/17213 | [
"Rostislav Horčík",
"Daniel Fišer"
] | Detection of redundant operators that can be safely removed from the planning task is an essential technique allowing to greatly improve performance of planners. In this paper, we employ structure-preserving maps on labeled transition systems (LTSs), namely endomorphisms well known from model theory, in order to detect... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17406 | 35 | 13 | 11835-11843 | official | null | null |
10.1609/aaai.v35i13.17405 | Landmark Generation in HTN Planning | https://ojs.aaai.org/index.php/AAAI/article/view/17405 | https://ojs.aaai.org/index.php/AAAI/article/download/17405/17212 | [
"Daniel Höller",
"Pascal Bercher"
] | Landmarks (LMs) are state features that need to be made true or tasks that need to be contained in every solution of a planning problem. They are a valuable source of information in planning and can be exploited in various ways. LMs have been used both in classical and hierarchical planning, but while there is much wor... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17405 | 35 | 13 | 11826-11834 | official | null | null |
10.1609/aaai.v35i13.17404 | Equitable Scheduling on a Single Machine | https://ojs.aaai.org/index.php/AAAI/article/view/17404 | https://ojs.aaai.org/index.php/AAAI/article/download/17404/17211 | [
"Klaus Heeger",
"Dan Hermelin",
"George B. Mertzios",
"Hendrik Molter",
"Rolf Niedermeier",
"Dvir Shabtay"
] | We introduce a natural but seemingly yet unstudied generalization of the problem of scheduling jobs on a single machine so as to minimize the number of tardy jobs. Our generalization lies in simultaneously considering several instances of the problem at once. In particular, we have n clients over a period of m days, wh... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17404 | 35 | 13 | 11818-11825 | official | 2010.04643 | title_snapshot |
10.1609/aaai.v35i13.17403 | Revisiting Dominance Pruning in Decoupled Search | https://ojs.aaai.org/index.php/AAAI/article/view/17403 | https://ojs.aaai.org/index.php/AAAI/article/download/17403/17210 | [
"Daniel Gnad"
] | In classical planning as search, duplicate state pruning is a standard method to avoid unnecessarily handling the same state multiple times. In decoupled search, similar to symbolic search approaches, search nodes, called decoupled states, do not correspond to individual states, but to sets of states. Therefore, duplic... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17403 | 35 | 13 | 11809-11817 | official | null | null |
10.1609/aaai.v35i13.17393 | Constrained Risk-Averse Markov Decision Processes | https://ojs.aaai.org/index.php/AAAI/article/view/17393 | https://ojs.aaai.org/index.php/AAAI/article/download/17393/17200 | [
"Mohamadreza Ahmadi",
"Ugo Rosolia",
"Michel D. Ingham",
"Richard M. Murray",
"Aaron D. Ames"
] | We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints. We begin by formulating the problem in a Lagrangian framework. Under the assumption that the risk objectives and constraints can be represented by a Markov risk transition mapping, w... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17393 | 35 | 13 | 11718-11725 | official | 2012.02423 | title_snapshot |
10.1609/aaai.v35i13.17392 | Computing Plan-Length Bounds Using Lengths of Longest Paths | https://ojs.aaai.org/index.php/AAAI/article/view/17392 | https://ojs.aaai.org/index.php/AAAI/article/download/17392/17199 | [
"Mohammad Abdulaziz",
"Dominik Berger"
] | We devise a method to exactly compute the length of the longest simple path in factored state spaces, like state spaces encountered in classical planning. Although the complexity of this problem is NEXP-Hard, we show that our method can be used to compute practically useful upper-bounds on lengths of plans. We show tha... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17392 | 35 | 13 | 11709-11717 | official | 2006.01011 | title_snapshot |
10.1609/aaai.v35i13.17394 | Contract Scheduling With Predictions | https://ojs.aaai.org/index.php/AAAI/article/view/17394 | https://ojs.aaai.org/index.php/AAAI/article/download/17394/17201 | [
"Spyros Angelopoulos",
"Shahin Kamali"
] | Contract scheduling is a general technique that allows to design a system with interruptible capabilities, given an algorithm that is not necessarily interruptible. Previous work on this topic has largely assumed that the interruption is a worst-case deadline that is unknown to the scheduler. In this work, we study the... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17394 | 35 | 13 | 11726-11733 | official | 2011.12439 | title_snapshot |
10.1609/aaai.v35i13.17395 | Responsibility Attribution in Parameterized Markovian Models | https://ojs.aaai.org/index.php/AAAI/article/view/17395 | https://ojs.aaai.org/index.php/AAAI/article/download/17395/17202 | [
"Christel Baier",
"Florian Funke",
"Rupak Majumdar"
] | We consider the problem of responsibility attribution in the setting of parametric Markov chains. Given a family of Markov chains over a set of parameters, and a property, responsibility attribution asks how the difference in the value of the property should be attributed to the parameters when they change from one poi... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17395 | 35 | 13 | 11734-11743 | official | null | null |
10.1609/aaai.v35i13.17396 | Symbolic Search for Optimal Total-Order HTN Planning | https://ojs.aaai.org/index.php/AAAI/article/view/17396 | https://ojs.aaai.org/index.php/AAAI/article/download/17396/17203 | [
"Gregor Behnke",
"David Speck"
] | Symbolic search has proven to be a useful approach to optimal classical planning. In Hierarchical Task Network (HTN) planning, however, there is little work on optimal planning. One reason for this is that in HTN planning, most algorithms are based on heuristic search, and admissible heuristics have to incorporate the ... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17396 | 35 | 13 | 11744-11754 | official | null | null |
10.1609/aaai.v35i13.17397 | A Multivariate Complexity Analysis of the Material Consumption Scheduling Problem | https://ojs.aaai.org/index.php/AAAI/article/view/17397 | https://ojs.aaai.org/index.php/AAAI/article/download/17397/17204 | [
"Matthias Bentert",
"Robert Bredereck",
"Péter Györgyi",
"Andrzej Kaczmarczyk",
"Rolf Niedermeier"
] | The NP-hard Material Consumption Scheduling Problem and closely related problems have been thoroughly studied since the 1980's. Roughly speaking, the problem deals with minimizing the makespan when scheduling jobs that consume non-renewable resources. We focus on the single-machine case without preemption: from time to... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17397 | 35 | 13 | 11755-11763 | official | 2102.13642 | title_snapshot |
10.1609/aaai.v35i13.17398 | General Policies, Representations, and Planning Width | https://ojs.aaai.org/index.php/AAAI/article/view/17398 | https://ojs.aaai.org/index.php/AAAI/article/download/17398/17205 | [
"Blai Bonet",
"Hector Geffner"
] | It has been observed that in many of the benchmark planning domains, atomic goals can be reached with a simple polynomial exploration procedure, called IW, that runs in time exponential in the problem width. Such problems have indeed a bounded width: a width that does not grow with the number of problem variables and i... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17398 | 35 | 13 | 11764-11773 | official | null | null |
10.1609/aaai.v35i13.17399 | Successor Feature Sets: Generalizing Successor Representations Across Policies | https://ojs.aaai.org/index.php/AAAI/article/view/17399 | https://ojs.aaai.org/index.php/AAAI/article/download/17399/17206 | [
"Kianté Brantley",
"Soroush Mehri",
"Geoff J. Gordon"
] | Successor-style representations have many advantages for reinforcement learning: for example, they can help an agent generalize from past experience to new goals, and they have been proposed as explanations of behavioral and neural data from human and animal learners. They also form a natural bridge between model-based... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17399 | 35 | 13 | 11774-11781 | official | 2103.02650 | title_snapshot |
10.1609/aaai.v35i13.17400 | GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling | https://ojs.aaai.org/index.php/AAAI/article/view/17400 | https://ojs.aaai.org/index.php/AAAI/article/download/17400/17207 | [
"Rohan Chitnis",
"Tom Silver",
"Joshua B. Tenenbaum",
"Leslie Pack Kaelbling",
"Tomás Lozano-Pérez"
] | We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspired by human curiosity, we propose goal-literal babbling (GLIB), a simple and general method for exploration in such problems. GLIB samples ... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17400 | 35 | 13 | 11782-11791 | official | 2001.08299 | title_snapshot |
10.1609/aaai.v35i13.17401 | Robust Finite-State Controllers for Uncertain POMDPs | https://ojs.aaai.org/index.php/AAAI/article/view/17401 | https://ojs.aaai.org/index.php/AAAI/article/download/17401/17208 | [
"Murat Cubuktepe",
"Nils Jansen",
"Sebastian Junges",
"Ahmadreza Marandi",
"Marnix Suilen",
"Ufuk Topcu"
] | Uncertain partially observable Markov decision processes (uPOMDPs) allow the probabilistic transition and observation functions of standard POMDPs to belong to a so-called uncertainty set. Such uncertainty, referred to as epistemic uncertainty, captures uncountable sets of probability distributions caused by, for insta... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17401 | 35 | 13 | 11792-11800 | official | 2009.11459 | title_snapshot |
10.1609/aaai.v35i13.17402 | Learning General Planning Policies from Small Examples Without Supervision | https://ojs.aaai.org/index.php/AAAI/article/view/17402 | https://ojs.aaai.org/index.php/AAAI/article/download/17402/17209 | [
"Guillem Francès",
"Blai Bonet",
"Hector Geffner"
] | Generalized planning is concerned with the computation of general policies that solve multiple instances of a planning domain all at once. It has been recently shown that these policies can be computed in two steps: first, a suitable abstraction in the form of a qualitative numerical planning problem (QNP) is learned f... | main | Planning, Routing, and Scheduling | 10.1609/aaai.v35i13.17402 | 35 | 13 | 11801-11808 | official | 2101.00692 | title_judge |
10.1609/aaai.v35i13.17449 | Bounding Causal Effects on Continuous Outcome | https://ojs.aaai.org/index.php/AAAI/article/view/17449 | https://ojs.aaai.org/index.php/AAAI/article/download/17449/17256 | [
"Junzhe Zhang",
"Elias Bareinboim"
] | We investigate the problem of bounding causal effects from experimental studies in which treatment assignment is randomized but the subject compliance is imperfect. It is well known that under such conditions, the actual causal effects are not point-identifiable due to uncontrollable unobserved confounding. In their se... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17449 | 35 | 13 | 12207-12215 | official | null | null |
10.1609/aaai.v35i13.17448 | Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs | https://ojs.aaai.org/index.php/AAAI/article/view/17448 | https://ojs.aaai.org/index.php/AAAI/article/download/17448/17255 | [
"Marcel Wienöbst",
"Max Bannach",
"Maciej Liskiewicz"
] | Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper, we show that these tasks can be performed in polynomial time, solving a long-standing open problem in this area. Our algorithms are effective and easily impl... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17448 | 35 | 13 | 12198-12206 | official | 2012.09679 | title_snapshot |
10.1609/aaai.v35i13.17447 | Learning the Parameters of Bayesian Networks from Uncertain Data | https://ojs.aaai.org/index.php/AAAI/article/view/17447 | https://ojs.aaai.org/index.php/AAAI/article/download/17447/17254 | [
"Segev Wasserkrug",
"Radu Marinescu",
"Sergey Zeltyn",
"Evgeny Shindin",
"Yishai A Feldman"
] | The creation of Bayesian networks often requires the specification of a large number of parameters, making it highly desirable to be able to learn these parameters from historical data. In many cases, such data has uncertainty associated with it, including cases in which this data comes from unstructured analysis or fr... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17447 | 35 | 13 | 12190-12197 | official | null | null |
10.1609/aaai.v35i13.17446 | Robust Contextual Bandits via Bootstrapping | https://ojs.aaai.org/index.php/AAAI/article/view/17446 | https://ojs.aaai.org/index.php/AAAI/article/download/17446/17253 | [
"Qiao Tang",
"Hong Xie",
"Yunni Xia",
"Jia Lee",
"Qingsheng Zhu"
] | Upper confidence bound (UCB) based contextual bandit algorithms require one to know the tail property of the reward distribution. Unfortunately, such tail property is usually unknown or difficult to specify in real-world applications. Using a tail property heavier than the ground truth leads to a slow learning speed of... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17446 | 35 | 13 | 12182-12189 | official | null | null |
10.1609/aaai.v35i13.17445 | Probabilistic Dependency Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/17445 | https://ojs.aaai.org/index.php/AAAI/article/download/17445/17252 | [
"Oliver Richardson",
"Joseph Y Halpern"
] | We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how P... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17445 | 35 | 13 | 12174-12181 | official | 2012.10800 | title_snapshot |
10.1609/aaai.v35i13.17444 | Estimation of Spectral Risk Measures | https://ojs.aaai.org/index.php/AAAI/article/view/17444 | https://ojs.aaai.org/index.php/AAAI/article/download/17444/17251 | [
"Ajay Kumar Pandey",
"Prashanth L.A.",
"Sanjay P. Bhat"
] | We consider the problem of estimating a spectral risk measure (SRM) from i.i.d. samples, and propose a novel method that is based on numerical integration. We show that our SRM estimate concentrates exponentially, when the underlying distribution has bounded support. Further, we also consider the case when the underlyi... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17444 | 35 | 13 | 12166-12173 | official | 1912.10398 | title_snapshot |
10.1609/aaai.v35i13.17443 | A New Bounding Scheme for Influence Diagrams | https://ojs.aaai.org/index.php/AAAI/article/view/17443 | https://ojs.aaai.org/index.php/AAAI/article/download/17443/17250 | [
"Radu Marinescu",
"Junkyu Lee",
"Rina Dechter"
] | Influence diagrams provide a modeling and inference framework for sequential decision problems, representing the probabilistic knowledge by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision variables. Computing the maximum expected utility (MEU) and the optim... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17443 | 35 | 13 | 12158-12165 | official | null | null |
10.1609/aaai.v35i13.17440 | Instrumental Variable-based Identification for Causal Effects using Covariate Information | https://ojs.aaai.org/index.php/AAAI/article/view/17440 | https://ojs.aaai.org/index.php/AAAI/article/download/17440/17247 | [
"Yuta Kawakami"
] | This paper deals with the identification problem of causal effects in randomized trials with noncompliance. In this problem, generally, causal effects are not identifiable and thus have been evaluated under some strict assumptions, or through the bounds. Different from existing studies, we propose novel identification ... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17440 | 35 | 13 | 12131-12138 | official | null | null |
10.1609/aaai.v35i13.17442 | Submodel Decomposition Bounds for Influence Diagrams | https://ojs.aaai.org/index.php/AAAI/article/view/17442 | https://ojs.aaai.org/index.php/AAAI/article/download/17442/17249 | [
"Junkyu Lee",
"Radu Marinescu",
"Rina Dechter"
] | Influence diagrams (IDs) are graphical models for representing and reasoning with sequential decision-making problems under uncertainty. Limited memory influence diagrams (LIMIDs) model a decision-maker (DM) who forgets the history in the course of making a sequence of decisions. The standard inference task in IDs and ... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17442 | 35 | 13 | 12147-12157 | official | null | null |
10.1609/aaai.v35i13.17441 | Learning Continuous High-Dimensional Models using Mutual Information and Copula Bayesian Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17441 | https://ojs.aaai.org/index.php/AAAI/article/download/17441/17248 | [
"Marvin Lasserre",
"Régis Lebrun",
"Pierre-Henri Wuillemin"
] | We propose a new framework to learn non-parametric graphical models from continuous observational data. Our method is based on concepts from information theory in order to discover independences and causality between variables: the conditional and multivariate mutual information (such as \cite{verny2017learning} for di... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17441 | 35 | 13 | 12139-12146 | official | null | null |
10.1609/aaai.v35i13.17431 | Group Fairness by Probabilistic Modeling with Latent Fair Decisions | https://ojs.aaai.org/index.php/AAAI/article/view/17431 | https://ojs.aaai.org/index.php/AAAI/article/download/17431/17238 | [
"YooJung Choi",
"Meihua Dang",
"Guy Van den Broeck"
] | Machine learning systems are increasingly being used to make impactful decisions such as loan applications and criminal justice risk assessments, and as such, ensuring fairness of these systems is critical. This is often challenging as the labels in the data are biased. This paper studies learning fair probability dist... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17431 | 35 | 13 | 12051-12059 | official | 2009.09031 | title_snapshot |
10.1609/aaai.v35i13.17439 | Relational Boosted Bandits | https://ojs.aaai.org/index.php/AAAI/article/view/17439 | https://ojs.aaai.org/index.php/AAAI/article/download/17439/17246 | [
"Ashutosh Kakadiya",
"Sriraam Natarajan",
"Balaraman Ravindran"
] | Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms represent context as attribute value representation, which makes them infeasible for real world domains like social networks, which are inherently relational. We propose Relational Boos... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17439 | 35 | 13 | 12123-12130 | official | 2012.09220 | title_snapshot |
10.1609/aaai.v35i13.17438 | Estimating Identifiable Causal Effects through Double Machine Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17438 | https://ojs.aaai.org/index.php/AAAI/article/download/17438/17245 | [
"Yonghan Jung",
"Jin Tian",
"Elias Bareinboim"
] | Identifying causal effects from observational data is a pervasive challenge found throughout the empirical sciences. Very general methods have been developed to decide the identifiability of a causal quantity from a combination of observational data and causal knowledge about the underlying system. In practice, however... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17438 | 35 | 13 | 12113-12122 | official | null | null |
10.1609/aaai.v35i13.17437 | A Generative Adversarial Framework for Bounding Confounded Causal Effects | https://ojs.aaai.org/index.php/AAAI/article/view/17437 | https://ojs.aaai.org/index.php/AAAI/article/download/17437/17244 | [
"Yaowei Hu",
"Yongkai Wu",
"Lu Zhang",
"Xintao Wu"
] | Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17437 | 35 | 13 | 12104-12112 | official | null | null |
10.1609/aaai.v35i13.17436 | High Dimensional Level Set Estimation with Bayesian Neural Network | https://ojs.aaai.org/index.php/AAAI/article/view/17436 | https://ojs.aaai.org/index.php/AAAI/article/download/17436/17243 | [
"Huong Ha",
"Sunil Gupta",
"Santu Rana",
"Svetha Venkatesh"
] | Level Set Estimation (LSE) is an important problem with applications in various fields such as material design, biotechnology, machine operational testing, etc. Existing techniques suffer from the scalability issue, that is, these methods do not work well with high dimensional inputs. This paper proposes novel methods ... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17436 | 35 | 13 | 12095-12103 | official | 2012.09973 | title_snapshot |
10.1609/aaai.v35i13.17435 | Scalable First-Order Methods for Robust MDPs | https://ojs.aaai.org/index.php/AAAI/article/view/17435 | https://ojs.aaai.org/index.php/AAAI/article/download/17435/17242 | [
"Julien Grand-Clément",
"Christian Kroer"
] | Robust Markov Decision Processes (MDPs) are a powerful framework for modeling sequential decision making problems with model uncertainty. This paper proposes the first first-order framework for solving robust MDPs. Our algorithm interleaves primal-dual first-order updates with approximate Value Iteration updates. By ca... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17435 | 35 | 13 | 12086-12094 | official | 2005.05434 | title_snapshot |
10.1609/aaai.v35i13.17434 | Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17434 | https://ojs.aaai.org/index.php/AAAI/article/download/17434/17241 | [
"Hongfei Du",
"Emre Barut",
"Fang Jin"
] | Despite the popularity of Convolutional Neural Networks (CNN), the problem of uncertainty quantification (UQ) of CNN has been largely overlooked. Lack of efficient UQ tools severely limits the application of CNN in certain areas, such as medicine, where prediction uncertainty is critically important. Among the few exis... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17434 | 35 | 13 | 12078-12085 | official | 2604.11833 | title_snapshot |
10.1609/aaai.v35i13.17433 | Better Bounds on the Adaptivity Gap of Influence Maximization under Full-adoption Feedback | https://ojs.aaai.org/index.php/AAAI/article/view/17433 | https://ojs.aaai.org/index.php/AAAI/article/download/17433/17240 | [
"Gianlorenzo D'Angelo",
"Debashmita Poddar",
"Cosimo Vinci"
] | In the influence maximization (IM) problem, we are given a social network and a budget k, and we look for a set of k nodes in the network, called seeds, that maximize the expected number of nodes that are reached by an influence cascade generated by the seeds, according to some stochastic model for influence diffusion.... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17433 | 35 | 13 | 12069-12077 | official | 2006.15374 | title_snapshot |
10.1609/aaai.v35i13.17432 | GO Hessian for Expectation-Based Objectives | https://ojs.aaai.org/index.php/AAAI/article/view/17432 | https://ojs.aaai.org/index.php/AAAI/article/download/17432/17239 | [
"Yulai Cong",
"Miaoyun Zhao",
"Jianqiao Li",
"Junya Chen",
"Lawrence Carin"
] | An unbiased low-variance gradient estimator, termed GO gradient, was proposed recently for expectation-based objectives E_q_γ(y) [f(y)], where the random variable (RV) y may be drawn from a stochastic computation graph (SCG) with continuous (non-reparameterizable) internal nodes and continuous/discrete leaves. Based on... | main | Reasoning under Uncertainty | 10.1609/aaai.v35i13.17432 | 35 | 13 | 12060-12068 | official | 2006.08873 | title_snapshot |
10.1609/aaai.v35i14.17464 | Enhancing Balanced Graph Edge Partition with Effective Local Search | https://ojs.aaai.org/index.php/AAAI/article/view/17464 | https://ojs.aaai.org/index.php/AAAI/article/download/17464/17271 | [
"Zhenyu Guo",
"Mingyu Xiao",
"Yi Zhou",
"Dongxiang Zhang",
"Kian-Lee Tan"
] | Graph partition is a key component to achieve workload balance and reduce job completion time in parallel graph processing systems. Among the various partition strategies, edge partition has demonstrated more promising performance in power-law graphs than vertex partition and thereby has been more widely adopted as the... | main | Search and Optimization | 10.1609/aaai.v35i14.17464 | 35 | 14 | 12336-12343 | official | 2012.09451 | title_snapshot |
10.1609/aaai.v35i14.17477 | Improving Maximum k-plex Solver via Second-Order Reduction and Graph Color Bounding | https://ojs.aaai.org/index.php/AAAI/article/view/17477 | https://ojs.aaai.org/index.php/AAAI/article/download/17477/17284 | [
"Yi Zhou",
"Shan Hu",
"Mingyu Xiao",
"Zhang-Hua Fu"
] | In a graph, a k-plex is a vertex set in which every vertex is not adjacent to at most k vertices of this set. The maximum k-plex problem, which asks for the largest k-plex from the given graph, is a key primitive in a variety of real-world applications like community detection and so on. In the paper, we develop an exa... | main | Search and Optimization | 10.1609/aaai.v35i14.17477 | 35 | 14 | 12453-12460 | official | null | null |
10.1609/aaai.v35i14.17476 | Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem | https://ojs.aaai.org/index.php/AAAI/article/view/17476 | https://ojs.aaai.org/index.php/AAAI/article/download/17476/17283 | [
"Jiongzhi Zheng",
"Kun He",
"Jianrong Zhou",
"Yan Jin",
"Chu-Min Li"
] | We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. And we propose a variable strategy reinforced approach, denoted as VSR-LKH, which combines three reinforcement learning methods (Q-learning, Sarsa and Monte Carlo) with the well-known TSP algorithm, called Lin-Kernigha... | main | Search and Optimization | 10.1609/aaai.v35i14.17476 | 35 | 14 | 12445-12452 | official | 2012.04461 | title_snapshot |
10.1609/aaai.v35i14.17475 | Accelerated Combinatorial Search for Outlier Detection with Provable Bound on Sub-Optimality | https://ojs.aaai.org/index.php/AAAI/article/view/17475 | https://ojs.aaai.org/index.php/AAAI/article/download/17475/17282 | [
"Guihong Wan",
"Haim Schweitzer"
] | Outliers negatively affect the accuracy of data analysis. In this paper we are concerned with their influence on the accuracy of Principal Component Analysis (PCA). Algorithms that attempt to detect outliers and remove them from the data prior to applying PCA are sometimes called Robust PCA, or Robust Subspace Recovery... | main | Search and Optimization | 10.1609/aaai.v35i14.17475 | 35 | 14 | 12436-12444 | official | null | null |
10.1609/aaai.v35i14.17474 | Learning Branching Heuristics for Propositional Model Counting | https://ojs.aaai.org/index.php/AAAI/article/view/17474 | https://ojs.aaai.org/index.php/AAAI/article/download/17474/17281 | [
"Pashootan Vaezipoor",
"Gil Lederman",
"Yuhuai Wu",
"Chris Maddison",
"Roger B Grosse",
"Sanjit A. Seshia",
"Fahiem Bacchus"
] | Propositional model counting, or #SAT, is the problem of computing the number of satisfying assignments of a Boolean formula. Many problems from different application areas, including many discrete probabilistic inference problems, can be translated into model counting problems to be solved by #SAT solvers. Exact #SAT ... | main | Search and Optimization | 10.1609/aaai.v35i14.17474 | 35 | 14 | 12427-12435 | official | 2007.03204 | title_snapshot |
10.1609/aaai.v35i14.17473 | Bayes DistNet - A Robust Neural Network for Algorithm Runtime Distribution Predictions | https://ojs.aaai.org/index.php/AAAI/article/view/17473 | https://ojs.aaai.org/index.php/AAAI/article/download/17473/17280 | [
"Jake Tuero",
"Michael Buro"
] | Randomized algorithms are used in many state-of-the-art solvers for constraint satisfaction problems (CSP) and Boolean satisfiability (SAT) problems. For many of these problems, there is no single solver which will dominate others. Having access to the underlying runtime distributions (RTD) of these solvers can allow f... | main | Search and Optimization | 10.1609/aaai.v35i14.17473 | 35 | 14 | 12418-12426 | official | 2012.07197 | title_snapshot |
10.1609/aaai.v35i14.17472 | Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering | https://ojs.aaai.org/index.php/AAAI/article/view/17472 | https://ojs.aaai.org/index.php/AAAI/article/download/17472/17279 | [
"Pavel Surynek"
] | We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal v... | main | Search and Optimization | 10.1609/aaai.v35i14.17472 | 35 | 14 | 12409-12417 | official | 2009.05161 | title_snapshot |
10.1609/aaai.v35i14.17471 | Weighting-based Variable Neighborhood Search for Optimal Camera Placement | https://ojs.aaai.org/index.php/AAAI/article/view/17471 | https://ojs.aaai.org/index.php/AAAI/article/download/17471/17278 | [
"Zhouxing Su",
"Qingyun Zhang",
"Zhipeng Lü",
"Chu-Min Li",
"Weibo Lin",
"Fuda Ma"
] | The optimal camera placement problem (OCP) aims to accomplish surveillance tasks with the minimum number of cameras, which is one of the topics in the GECCO 2020 Competition and can be modeled as the unicost set covering problem (USCP). This paper presents a weighting-based variable neighborhood search (WVNS) algorithm... | main | Search and Optimization | 10.1609/aaai.v35i14.17471 | 35 | 14 | 12400-12408 | official | null | null |
10.1609/aaai.v35i14.17470 | Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures | https://ojs.aaai.org/index.php/AAAI/article/view/17470 | https://ojs.aaai.org/index.php/AAAI/article/download/17470/17277 | [
"Sebastian Risi",
"Kenneth O. Stanley"
] | Deep reinforcement learning approaches have shown impressive results in a variety of different domains, however, more complex heterogeneous architectures such as world models require the different neural components to be trained separately instead of end-to-end. While a simple genetic algorithm recently showed end-to-e... | main | Search and Optimization | 10.1609/aaai.v35i14.17470 | 35 | 14 | 12391-12399 | official | 2001.01683 | title_snapshot |
10.1609/aaai.v35i14.17469 | Policy-Guided Heuristic Search with Guarantees | https://ojs.aaai.org/index.php/AAAI/article/view/17469 | https://ojs.aaai.org/index.php/AAAI/article/download/17469/17276 | [
"Laurent Orseau",
"Levi H. S. Lelis"
] | The use of a policy and a heuristic function for guiding search can be quite effective in adversarial problems, as demonstrated by AlphaGo and its successors, which are based on the PUCT search algorithm. While PUCT can also be used to solve single-agent deterministic problems, it lacks guarantees on its search effort ... | main | Search and Optimization | 10.1609/aaai.v35i14.17469 | 35 | 14 | 12382-12390 | official | 2103.11505 | title_snapshot |
10.1609/aaai.v35i14.17468 | Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model | https://ojs.aaai.org/index.php/AAAI/article/view/17468 | https://ojs.aaai.org/index.php/AAAI/article/download/17468/17275 | [
"Felix Mohr",
"Viktor Bengs",
"Eyke Hüllermeier"
] | The problem of minimal cost path search is especially difficult when no useful heuristics are available. A common solution is roll-out-based search like Monte Carlo Tree Search (MCTS). However, MCTS is mostly used in stochastic or adversarial environments, with the goal to identify an agent's best next move. For this r... | main | Search and Optimization | 10.1609/aaai.v35i14.17468 | 35 | 14 | 12373-12381 | official | null | null |
10.1609/aaai.v35i14.17467 | Correlation-Aware Heuristic Search for Intelligent Virtual Machine Provisioning in Cloud Systems | https://ojs.aaai.org/index.php/AAAI/article/view/17467 | https://ojs.aaai.org/index.php/AAAI/article/download/17467/17274 | [
"Chuan Luo",
"Bo Qiao",
"Wenqian Xing",
"Xin Chen",
"Pu Zhao",
"Chao Du",
"Randolph Yao",
"Hongyu Zhang",
"Wei Wu",
"Shaowei Cai",
"Bing He",
"Saravanakumar Rajmohan",
"Qingwei Lin"
] | The optimization of resource is crucial for the operation of public cloud systems such as Microsoft Azure, as well as servers dedicated to the workloads of large customers such as Microsoft 365. Those optimization tasks often need to take unknown parameters into consideration and can be formulated as Prediction+Optimiz... | main | Search and Optimization | 10.1609/aaai.v35i14.17467 | 35 | 14 | 12363-12372 | official | null | null |
10.1609/aaai.v35i14.17466 | EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding | https://ojs.aaai.org/index.php/AAAI/article/view/17466 | https://ojs.aaai.org/index.php/AAAI/article/download/17466/17273 | [
"Jiaoyang Li",
"Wheeler Ruml",
"Sven Koenig"
] | Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, is important for many applications where small runtimes are necessary, including the kind of automated warehouses operated by Amazon. CBS is a leading two-level search algorithm for solving MAPF optimally. ECBS is a bounded-subopti... | main | Search and Optimization | 10.1609/aaai.v35i14.17466 | 35 | 14 | 12353-12362 | official | 2010.01367 | title_snapshot |
10.1609/aaai.v35i14.17465 | Submodular Span, with Applications to Conditional Data Summarization | https://ojs.aaai.org/index.php/AAAI/article/view/17465 | https://ojs.aaai.org/index.php/AAAI/article/download/17465/17272 | [
"Lilly Kumari",
"Jeff Bilmes"
] | As an extension to the matroid span problem, we propose the submodular span problem that involves finding a large set of elements with small gain relative to a given query set. We then propose a two-stage Submodular Span Summarization (S3) framework to achieve a form of conditional or query-focused data summarization. ... | main | Search and Optimization | 10.1609/aaai.v35i14.17465 | 35 | 14 | 12344-12352 | official | null | null |
10.1609/aaai.v35i14.17463 | Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings | https://ojs.aaai.org/index.php/AAAI/article/view/17463 | https://ojs.aaai.org/index.php/AAAI/article/download/17463/17270 | [
"Niels Grüttemeier",
"Christian Komusiewicz",
"Nils Morawietz"
] | In Bayesian Network Structure Learning (BNSL), we are given a variable set and parent scores for each variable and aim to compute a DAG, called Bayesian network, that maximizes the sum of parent scores, possibly under some structural constraints. Even very restricted special cases of BNSL are computationally hard, and,... | main | Search and Optimization | 10.1609/aaai.v35i14.17463 | 35 | 14 | 12328-12335 | official | 2204.02902 | title_snapshot |
10.1609/aaai.v35i14.17450 | A Fast Exact Algorithm for the Resource Constrained Shortest Path Problem | https://ojs.aaai.org/index.php/AAAI/article/view/17450 | https://ojs.aaai.org/index.php/AAAI/article/download/17450/17257 | [
"Saman Ahmadi",
"Guido Tack",
"Daniel D. Harabor",
"Philip Kilby"
] | Resource constrained path finding is a well studied topic in AI, with real-world applications in different areas such as transportation and robotics. This paper introduces several heuristics in the resource constrained path finding context that significantly improve the algorithmic performance of the initialisation pha... | main | Search and Optimization | 10.1609/aaai.v35i14.17450 | 35 | 14 | 12217-12224 | official | null | null |
10.1609/aaai.v35i14.17462 | OpEvo: An Evolutionary Method for Tensor Operator Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/17462 | https://ojs.aaai.org/index.php/AAAI/article/download/17462/17269 | [
"Xiaotian Gao",
"Wei Cui",
"Lintao Zhang",
"Mao Yang"
] | Training and inference efficiency of deep neural networks highly rely on the performance of tensor operators on hardware platforms. Manually optimizing tensor operators has limitations in terms of supporting new operators or hardware platforms. Therefore, automatically optimizing device code configurations of tensor op... | main | Search and Optimization | 10.1609/aaai.v35i14.17462 | 35 | 14 | 12320-12327 | official | 2006.05664 | title_snapshot |
10.1609/aaai.v35i14.17461 | Choosing the Initial State for Online Replanning | https://ojs.aaai.org/index.php/AAAI/article/view/17461 | https://ojs.aaai.org/index.php/AAAI/article/download/17461/17268 | [
"Maximilian Fickert",
"Ivan Gavran",
"Ivan Fedotov",
"Jörg Hoffmann",
"Rupak Majumdar",
"Wheeler Ruml"
] | The need to replan arises in many applications. However, in the context of planning as heuristic search, it raises an annoying problem: if the previous plan is still executing, what should the new plan search take as its initial state? If it were possible to accurately predict how long replanning would take, it would b... | main | Search and Optimization | 10.1609/aaai.v35i14.17461 | 35 | 14 | 12311-12319 | official | null | null |
10.1609/aaai.v35i14.17460 | Multi-Objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee | https://ojs.aaai.org/index.php/AAAI/article/view/17460 | https://ojs.aaai.org/index.php/AAAI/article/download/17460/17267 | [
"Chao Feng",
"Chao Qian"
] | Submodular maximization has attracted much attention due to its wide application and attractive property. Previous works mainly considered one single objective function, while there can be multiple ones in practice. As the objectives are usually conflicting, there exists a set of Pareto optimal solutions, attaining dif... | main | Search and Optimization | 10.1609/aaai.v35i14.17460 | 35 | 14 | 12302-12310 | official | null | null |
10.1609/aaai.v35i14.17459 | Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives | https://ojs.aaai.org/index.php/AAAI/article/view/17459 | https://ojs.aaai.org/index.php/AAAI/article/download/17459/17266 | [
"Benjamin Doerr",
"Weijie Zheng"
] | Previous theory work on multi-objective evolutionary algorithms considers mostly easy problems that are composed of unimodal objectives. This paper takes a first step towards a deeper understanding of how evolutionary algorithms solve multi-modal multi-objective problems. We propose the OneJumpZeroJump problem, a bi-ob... | main | Search and Optimization | 10.1609/aaai.v35i14.17459 | 35 | 14 | 12293-12301 | official | 2012.07231 | title_judge |
10.1609/aaai.v35i14.17458 | Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/17458 | https://ojs.aaai.org/index.php/AAAI/article/download/17458/17265 | [
"Anh Viet Do",
"Frank Neumann"
] | In this study, we consider the subset selection problems with submodular or monotone discrete objective functions under partition matroid constraints where the thresholds are dynamic. We focus on POMC, a simple Pareto optimization approach that has been shown to be effective on such problems. Our analysis departs from ... | main | Search and Optimization | 10.1609/aaai.v35i14.17458 | 35 | 14 | 12284-12292 | official | 2012.08738 | title_snapshot |
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