GenAI4ELab/papercli-papers huggingface dataset:
Lifted Inference with Linear Order Axiom
Authors: Jan Tóth; Ondřej Kuželka
Venue: AAAI 2023 (main)

Abstract: We consider the task of weighted first-order model counting (WFOMC) used for probabilistic inference in the area of statistical relational learning. Given a formula φ, domain size n and a pair of weight functions, what is the weighted sum of all models of φ over a domain of size n? It was shown that computing WFOMC of any logical sentence with at most two logical variables can be done in time polynomial in n. However, it was also shown that the task is #P1-complete once we add the third variable, which inspired the search for extensions of the two-variable fragment that would still permit a running time polynomial in n. One of such extension is the two-variable fragment with counting quantifiers. In this paper, we prove that adding a linear order axiom (which forces one of the predicates in φ to introduce a linear ordering of the domain elements in each model of φ) on top of the counting quantifiers still permits a computation time polynomial in the domain size. We present a new dynamic programming-based algorithm which can compute WFOMC with linear order in time polynomial in n, thus proving our primary claim.
Vector Causal Inference between Two Groups of Variables
Authors: Jonas Wahl; Urmi Ninad; Jakob Runge
Venue: AAAI 2023 (main)

Abstract: Methods to identify cause-effect relationships currently mostly assume the variables to be scalar random variables. However, in many fields the objects of interest are vectors or groups of scalar variables.
We present a new constraint-based non-parametric approach for inferring the causal relationship between two vector-valued random variables from observational data. Our method employs sparsity estimates of directed and undirected graphs and is based on two new principles for groupwise causal reasoning that we justify theoretically in Pearl's graphical model-based causality framework. Our theoretical considerations are complemented by two new causal discovery algorithms for causal interactions between two random vectors which find the correct causal direction reliably in simulations even if interactions are nonlinear. We evaluate our methods empirically and compare them to other state-of-the-art techniques.
Efficient Enumeration of Markov Equivalent DAGs
Authors: Marcel Wienöbst; Malte Luttermann; Max Bannach; Maciej Liskiewicz
Venue: AAAI 2023 (main)

Abstract: Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class (MEC) is an important primitive in causal analysis. The central resource from the perspective of computational complexity is the delay, that is, the time an algorithm that lists all members of the class requires between two consecutive outputs. Commonly used algorithms for this task utilize the rules proposed by Meek (1995) or the transformational characterization by Chickering (1995), both resulting in superlinear delay. In this paper, we present the first linear-time delay algorithm. On the theoretical side, we show that our algorithm can be generalized to enumerate DAGs represented by models that incorporate background knowledge, such as MPDAGs; on the practical side, we provide an efficient implementation and evaluate it in a series of experiments. Complementary to the linear-time delay algorithm, we also provide intriguing insights into Markov equivalence itself: All members of an MEC can be enumerated such that two successive DAGs have structural Hamming distance at most three.
Differentially Private Nonlinear Causal Discovery from Numerical Data
Authors: Hao Zhang; Yewei Xia; Yixin Ren; Jihong Guan; Shuigeng Zhou
Venue: AAAI 2023 (main)

Abstract: Recently, several methods such as private ANM, EM-PC and Priv-PC have been proposed to perform differentially private causal discovery in various scenarios including bivariate, multivariate Gaussian and categorical cases. However, there is little effort on how to conduct private nonlinear causal discovery from numerical data. This work tries to challenge this problem. To this end, we propose a method to infer nonlinear causal relations from observed numerical data by using regression-based conditional independence test (RCIT) that consists of kernel ridge regression (KRR) and Hilbert-Schmidt independence criterion (HSIC) with permutation approximation. Sensitivity analysis for RCIT is given and a private constraint-based causal discovery framework with differential privacy guarantee is developed. Extensive simulations and real-world experiments for both conditional independence test and causal discovery are conducted, which show that our method is effective in handling nonlinear numerical cases and easy to implement. The source code of our method and data are available at https://github.com/Causality-Inference/PCD.
Safe Interval Path Planning with Kinodynamic Constraints
Authors: Zain Alabedeen Ali; Konstantin Yakovlev
Venue: AAAI 2023 (main)

Abstract: Safe Interval Path Planning (SIPP) is a powerful algorithm for solving a single-agent pathfinding problem where the agent is confined to a graph and certain vertices/edges of this graph are blocked at certain time intervals due to dynamic obstacles that populate the environment. The original SIPP algorithm relies on the assumption that the agent is able to stop instantaneously. However, this assumption often does not hold in practice, e.g. a mobile robot moving at a cruising speed cannot stop immediately but rather requires gradual deceleration to a full stop that takes time. In other words, the robot is subject to kinodynamic constraints. Unfortunately, as we show in this work, in such a case, the original SIPP is incomplete. To this end, we introduce a novel variant of SIPP that is provably complete and optimal for planning with acceleration/deceleration. In the experimental evaluation, we show that the key property of the original SIPP still holds for the modified version: it performs much fewer expansions compared to A* and, as a result, is notably faster.
Diversity Maximization in the Presence of Outliers
Authors: Daichi Amagata
Venue: AAAI 2023 (main)

Abstract: Given a set X of n points in a metric space, the problem of diversity maximization is to extract a set S of k points from X so that the diversity of S is maximized. This problem is essential in AI-related fields, such as web search, databases, recommender systems, and data mining. Although there have been extensive studies of this problem, these studies assume that X is clean. This usually does not hold, because real-world datasets usually contain outliers. The state-of-the-art algorithm for the diversity maximization problem is based on furthest point retrieval, which is too sensitive to outliers. We therefore address the problem of diversity maximization with outliers and propose two algorithms with performance guarantee. The first algorithm runs in O((k+z)n) time, guarantees 1/2-approximation, and returns no outliers, where z is the number of outliers. The second algorithm runs in O(kz) time (which is independent of n), guarantees 1/6(1+epsilon)-approximation, and returns no outliers with constant probability. We conduct experiments on real datasets to demonstrate the effectiveness and efficiency of our algorithms.
Fair Short Paths in Vertex-Colored Graphs
Authors: Matthias Bentert; Leon Kellerhals; Rolf Niedermeier
Venue: AAAI 2023 (main)

Abstract: The computation of short paths in graphs with arc lengths is a pillar of graph algorithmics and network science. In a more diverse world, however, not every short path is equally valuable. For the setting where each vertex is assigned to a group (color), we provide a framework to model multiple natural fairness aspects. We seek to find short paths in which the number of occurrences of each color is within some given lower and upper bounds. Among other results, we prove the introduced problems to be computationally intractable (NP-hard and parameterized hard with respect to the number of colors) even in very restricted settings (such as each color should appear with exactly the same frequency), while also presenting an encouraging algorithmic result ("fixed-parameter tractability") related to the length of the sought solution path for the general problem.
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
Authors: Jasmin Brandt; Elias Schede; Björn Haddenhorst; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney
Venue: AAAI 2023 (main)

Abstract: We study the algorithm configuration (AC) problem, in which one seeks to find an optimal parameter configuration of a given target algorithm in an automated way. Although this field of research has experienced much progress recently regarding approaches satisfying strong theoretical guarantees, there is still a gap between the practical performance of these approaches and the heuristic state-of-the-art approaches. Recently, there has been significant progress in designing AC approaches that satisfy strong theoretical guarantees. However, a significant gap still remains between the practical performance of these approaches and state-of-the-art heuristic methods. To this end, we introduce AC-Band, a general approach for the AC problem based on multi-armed bandits that provides theoretical guarantees while exhibiting strong practical performance. We show that AC-Band requires significantly less computation time than other AC approaches providing theoretical guarantees while still yielding high-quality configurations.
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks
Authors: Angelina Brilliantova; Hannah Miller; Ivona Bezáková
Venue: AAAI 2023 (main)

Abstract: Signed networks (networks with positive and negative edges) commonly arise in various domains from molecular biology to social media. 
The edge signs -- i.e., the graph signage -- represent the interaction pattern between the vertices and can provide insights into the underlying system formation process. Generative models considering signage formation are essential for testing hypotheses about the emergence of interactions and for creating synthetic datasets for algorithm benchmarking (especially in areas where obtaining real-world datasets is difficult).

In this work, we pose a novel Maximum-Likelihood-based optimization problem for modeling signages given their topology and showcase it in the context of gene regulation. Regulatory interactions of genes play a key role in the process of organism development, and when broken can lead to serious organism abnormalities and diseases.  Our contributions are threefold: First, we design a new class of signage models for a given topology, and, based on the parameter setting, we discuss its biological interpretations for gene regulatory networks (GRNs). Second, we design algorithms computing the Maximum Likelihood -- depending on the parameter setting, our algorithms range from closed-form expressions to MCMC sampling. Third, we evaluated the results of our algorithms on synthetic datasets and real-world large GRNs. Our work can lead to the prediction of unknown gene regulations, novel biological hypotheses, and realistic benchmark datasets in the realm of gene regulation.
Optimal Pathfinding on Weighted Grid Maps
Authors: Mark Carlson; Sajjad K. Moghadam; Daniel D. Harabor; Peter J. Stuckey; Morteza Ebrahimi
Venue: AAAI 2023 (main)

Abstract: In many computer games up to hundreds of agents navigate in real-time across a dynamically changing weighted grid map. Pathfinding in these situations is challenging because the grids are large, traversal costs are not uniform, and because each shortest path has many symmetric permutations, all of which must be considered by an optimal online search. In this work we introduce Weighted Jump Point Search (JPSW), a new type of pathfinding algorithm which breaks weighted grid symmetries by introducing a tiebreaking policy that allows us to apply effective pruning rules in symmetric regions. We show that these pruning rules preserve at least one optimal path to every grid cell and that their application can yield large performance improvements for optimal pathfinding. We give a complete theoretical description of the new algorithm, including pseudo-code. We also conduct a wide-ranging experimental evaluation, including data from real games. Results indicate JPSW is up to orders of magnitude faster than the nearest baseline, online search using A*.
Warm-Starting Nested Rollout Policy Adaptation with Optimal Stopping
Authors: Chen Dang; Cristina Bazgan; Tristan Cazenave; Morgan Chopin; Pierre-Henri Wuillemin
Venue: AAAI 2023 (main)

Abstract: Nested Rollout Policy Adaptation (NRPA) is an approach using online learning policies in a nested structure. It has achieved a great result in a variety of difficult combinatorial optimization problems. In this paper, we propose Meta-NRPA, which combines optimal stopping theory with NRPA for warm-starting and significantly improves the performance of NRPA. We also present several exploratory techniques for NRPA which enable it to perform better exploration. We establish this for three notoriously difficult problems ranging from telecommunication, transportation and coding theory namely Minimum Congestion Shortest Path Routing,  Traveling Salesman Problem with Time Windows and Snake-in-the-Box.
We also improve the lower bounds of the Snake-in-the-Box problem for multiple dimensions.
A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation
Authors: Duc-Cuong Dang; Andre Opris; Bahare Salehi; Dirk Sudholt
Venue: AAAI 2023 (main)

Abstract: Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called Pareto optimisation) as they use a population to store trade-offs between different objectives. Despite their popularity, the theoretical foundation of multiobjective evolutionary optimisation (EMO) is still in its early development. Fundamental questions such as the benefits of the crossover operator are still not fully understood.

We provide a theoretical analysis of well-known EMO algorithms GSEMO and NSGA-II to showcase the possible advantages of crossover. We propose a class of problems on which these EMO algorithms using crossover find the Pareto set in expected polynomial time. In sharp contrast, they and many other EMO algorithms without crossover require exponential time to even find a single Pareto-optimal point. This is the first example of an exponential performance gap through the use of crossover for the widely used NSGA-II algorithm.
Runtime Analysis for the NSGA-II: Provable Speed-Ups from Crossover
Authors: Benjamin Doerr; Zhongdi Qu
Venue: AAAI 2023 (main)

Abstract: Very recently, the first mathematical runtime analyses for the NSGA-II, the most common multi-objective evolutionary algorithm, have been conducted. Continuing this research direction, we prove that the NSGA-II optimizes the OneJumpZeroJump benchmark asymptotically faster when crossover is employed. Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt, this is the first time such an advantage of crossover is proven for the NSGA-II. Our arguments can be transferred to single-objective optimization. They then prove that crossover can speed up the (mu+1) genetic algorithm in a different way and more pronounced than known before. Our experiments confirm the added value of crossover and show that the observed advantages are even larger than what our proofs can guarantee.
From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds
Authors: Benjamin Doerr; Zhongdi Qu
Venue: AAAI 2023 (main)

Abstract: Due to the more complicated population dynamics of the NSGA-II, none of the existing runtime guarantees for this algorithm is accompanied by a non-trivial lower bound. Via a first mathematical understanding of the population dynamics of the NSGA-II, that is, by estimating the expected number of individuals having a certain objective value, we prove that the NSGA-II with suitable population size needs Omega(Nn log n) function evaluations to find the Pareto front of the OneMinMax problem and Omega(Nn^k)  evaluations on the OneJumpZeroJump problem with jump size k. These bounds are asymptotically tight (that is, they match previously shown upper bounds) and show that the NSGA-II here does not even in terms of the parallel runtime (number of iterations) profit from larger population sizes. For the OneJumpZeroJump problem and when the same sorting is used for the computation of the crowding distance contributions of the two objectives, we even obtain a runtime estimate that is tight including the leading constant.
Ultrafast Euclidean Shortest Path Computation Using Hub Labeling
Authors: Jinchun Du; Bojie Shen; Muhammad Aamir Cheema
Venue: AAAI 2023 (main)

Abstract: Finding shortest paths in a Euclidean plane containing polygonal obstacles is a well-studied problem motivated by a variety of real-world applications. 
The state-of-the-art algorithms require finding obstacle corners visible to the source and target, and need to consider potentially a large number of candidate paths. This adversely affects their query processing cost. We address these limitations by proposing a novel adaptation of hub labeling which is the state-of-the-art approach for  shortest distance computation in road networks. Our experimental study conducted on the widely used benchmark maps shows that our approach is typically 1-2  orders of magnitude faster than two state-of-the-art algorithms.
A Formal Metareasoning Model of Concurrent Planning and Execution
Authors: Amihay Elboher; Ava Bensoussan; Erez Karpas; Wheeler Ruml; Shahaf S. Shperberg; Eyal Shimony
Venue: AAAI 2023 (main)

Abstract: Agents that plan and act in the real world must deal with the fact that time passes as they are planning. When timing is tight, there may be insufficient time to complete the search for a plan before it is time to act.  By commencing execution before search concludes, one gains time to search by making planning and execution concurrent. However, this incurs the risk of making incorrect action choices, especially if actions are irreversible. This tradeoff between opportunity and risk is the problem addressed in this paper. Our main contribution is to formally define this setting as an abstract metareasoning problem. We find that the abstract problem is intractable.  However, we identify special cases that are solvable in polynomial time, develop greedy solution algorithms, and, through tests on instances derived from search problems, find several methods that achieve promising practical performance.  This work lays the foundation for a principled time-aware executive that concurrently plans and executes.
TransPath: Learning Heuristics for Grid-Based Pathfinding via Transformers
Authors: Daniil Kirilenko; Anton Andreychuk; Aleksandr Panov; Konstantin Yakovlev
Venue: AAAI 2023 (main)

Abstract: Heuristic search algorithms, e.g. A*, are the commonly used tools for pathfinding on grids, i.e. graphs of regular structure that are widely employed to represent environments in robotics, video games, etc. Instance-independent heuristics for grid graphs, e.g. Manhattan distance, do not take the obstacles into account, and thus the search led by such heuristics performs poorly in obstacle-rich environments. To this end, we suggest learning the instance-dependent heuristic proxies that are supposed to notably increase the efficiency of the search. The first heuristic proxy we suggest to learn is the correction factor, i.e. the ratio between the instance-independent cost-to-go estimate and the perfect one (computed offline at the training phase). Unlike learning the absolute values of the cost-to-go heuristic function, which was known before, learning the correction factor utilizes the knowledge of the instance-independent heuristic. The second heuristic proxy is the path probability, which indicates how likely the grid cell is lying on the shortest path. This heuristic can be employed in the Focal Search framework as the secondary heuristic, allowing us to preserve the guarantees on the bounded sub-optimality of the solution. We learn both suggested heuristics in a supervised fashion with the state-of-the-art neural networks containing attention blocks (transformers). We conduct a thorough empirical evaluation on a comprehensive dataset of planning tasks, showing that the suggested techniques i) reduce the computational effort of the A* up to a factor of 4x while producing the solutions, whose costs exceed those of the optimal solutions by less than 0.3% on average; ii) outperform the competitors, which include the conventional techniques from the heuristic search, i.e. weighted A*, as well as the state-of-the-art learnable planners.

The project web-page is: https://airi-institute.github.io/TransPath/.
Large-State Reinforcement Learning for Hyper-Heuristics
Authors: Lucas Kletzander; Nysret Musliu
Venue: AAAI 2023 (main)

Abstract: Hyper-heuristics are a domain-independent problem solving approach where the main task is to select effective chains of problem-specific low-level heuristics on the fly for an unseen instance. This task can be seen as a reinforcement learning problem, however, the information available to the hyper-heuristic is very limited, usually leading to very limited state representations. In this work, for the first time we use the trajectory of solution changes for a larger set of features for reinforcement learning in the novel hyper-heuristic LAST-RL (Large-State Reinforcement Learning). Further, we introduce a probability distribution for the exploration case in our epsilon-greedy policy that is based on the idea of Iterated Local Search to increase the chance to sample good chains of low-level heuristics. The benefit of the collaboration of our novel components is shown on the academic benchmark of the Cross Domain Heuristic Challenge 2011 consisting of six different problem domains. Our approach can provide state-of-the-art results on this benchmark where it outperforms recent hyper-heuristics based on reinforcement learning, and also demonstrates high performance on a benchmark of complex real-life personnel scheduling domains.
Human Assisted Learning by Evolutionary Multi-Objective Optimization
Authors: Dan-Xuan Liu; Xin Mu; Chao Qian
Venue: AAAI 2023 (main)

Abstract: Machine learning models have liberated manpower greatly in many real-world tasks, but their predictions are still worse than humans on some specific instances. To improve the performance, it is natural to optimize machine learning models to take decisions for most instances while delivering a few tricky instances to humans, resulting in the problem of Human Assisted Learning (HAL). Previous works mainly formulated HAL as a constrained optimization problem that tries to find a limited subset of instances for human decision such that the sum of model and human errors can be minimized; and employed the greedy algorithms, whose performance, however, may be limited due to the greedy nature. In this paper, we propose a new framework HAL-EMO based on Evolutionary Multi-objective Optimization, which reformulates HAL as a bi-objective optimization problem that minimizes the number of selected instances for human decision and the total errors simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve it. We implement HAL-EMO using two MOEAs, the popular NSGA-II as well as the theoretically grounded GSEMO. We also propose a specific MOEA, called BSEMO, with biased selection and balanced mutation for HAL-EMO, and prove that for human assisted regression and classification, HAL-EMO using BSEMO can achieve better and same theoretical guarantees than previous greedy algorithms, respectively. Experiments on the tasks of medical diagnosis and content moderation show the superiority of HAL-EMO (with either NSGA-II, GSEMO or BSEMO) over previous algorithms, and that using BSEMO leads to the best performance of HAL-EMO.