bluuebunny/crossref_metadata_2025 huggingface dataset:
Making Adversarial Examples More Transferable and Indistinguishable
Authors: Zou Junhua , Duan Yexin , Li Boyu , Zhang Wu , Pan Yu , Pan Zhisong
Published: July 2022

Fast gradient sign attack series are popular methods that are used to generate adversarial examples. However, most of the approaches based on fast gradient sign attack series cannot balance the indistinguishability and transferability due to the limitations of the basic sign structure. To address this problem, we propose a method, called Adam Iterative Fast Gradient Tanh Method (AI-FGTM), to generate indistinguishable adversarial examples with high transferability. Besides, smaller kernels and dynamic step size are also applied to generate adversarial examples for further increasing the attack success rates. Extensive experiments on an ImageNet-compatible dataset show that our method generates more indistinguishable adversarial examples and achieves higher attack success rates without extra running time and resource. Our best transfer-based attack NI-TI-DI-AITM can fool six classic defense models with an average success rate of 89.3% and three advanced defense models with an average success rate of 82.7%, which are higher than the state-of-the-art gradient-based attacks. Additionally, our method can also reduce nearly 20% mean perturbation. We expect that our method will serve as a new baseline for generating adversarial examples with better transferability and indistinguishability.
Computing Diverse Shortest Paths Efficiently: A Theoretical and Experimental Study
Authors: Hanaka Tesshu , Kobayashi Yasuaki , Kurita Kazuhiro , Lee See Woo , Otachi Yota
Published: July 2022

Finding diverse solutions in combinatorial problems recently has received considerable attention (Baste et al. 2020; Fomin et al. 2020; Hanaka et al. 2021). In this paper we study the following type of problems: given an integer k, the problem asks for k solutions such that the sum of pairwise (weighted) Hamming distances between these solutions is maximized. Such solutions are called diverse solutions. We present a polynomial-time algorithm for finding diverse shortest st-paths in weighted directed graphs. Moreover, we study the diverse version of other classical combinatorial problems such as diverse weighted matroid bases, diverse weighted arborescences, and diverse bipartite matchings. We show that these problems can be solved in polynomial time as well. To evaluate the practical performance of our algorithm for finding diverse shortest st-paths, we conduct a computational experiment with synthetic and real-world instances. The experiment shows that our algorithm successfully computes diverse solutions within reasonable computational time.
How to Find a Good Explanation for Clustering?
Authors: Bandyapadhyay Sayan , Fomin Fedor , Golovach Petr A , Lochet William , Purohit Nidhi , Simonov Kirill
Published: July 2022

k-means and k-median clustering are powerful unsupervised machine learning techniques. However, due to complicated dependences on all the features, it is challenging to interpret the resulting cluster assignments. Moshkovitz, Dasgupta, Rashtchian, and Frost proposed an elegant model of explainable k-means and k-median clustering in ICML 2020. In this model, a decision tree with k leaves provides a straightforward characterization of the data set into clusters.

We study two natural algorithmic questions about explainable clustering. (1) For a given clustering, how to find the ``best explanation'' by using a decision tree with k leaves? (2) For a given set of points, how to find a decision tree with k leaves minimizing the k-means/median objective of the resulting explainable clustering?
To address the first question, we introduce a new model of explainable clustering. Our model, inspired by the notion of outliers in robust statistics, is the following. We are seeking a small number of points (outliers) whose removal makes the existing clustering well-explainable. For addressing the second question, we initiate the study of the model of Moshkovitz et al. from the perspective of multivariate complexity. Our rigorous algorithmic analysis sheds some light on the influence of parameters like the input size, dimension of the data, the number of outliers, the number of clusters, and the approximation ratio, on the computational complexity of explainable clustering.
Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences
Authors: Gupta Vinayak , Bedathur Srikanta , De Abir
Published: July 2022

Recent developments in predictive modeling using marked temporal point processes (MTPPs) have enabled an accurate characterization of several real-world applications involving continuous-time event sequences (CTESs). However, the retrieval problem of such sequences remains largely unaddressed in literature. To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences. More specifically, NEUROSEQRET first applies a trainable unwarping function on the query sequence, which makes it comparable with corpus sequences, especially when a relevant query-corpus pair has individually different attributes. Next, it feeds the unwarped query sequence and the corpus sequence into MTPP guided neural relevance models. We develop two variants of the relevance model which offer a tradeoff between accuracy and efficiency. We also propose an optimization framework to learn binary sequence embeddings from the relevance scores, suitable for the locality-sensitive hashing leading to a significant speedup in returning top-K results for a given query sequence. Our experiments with several datasets show the significant accuracy boost of NEUROSEQRET beyond several baselines, as well as the efficacy of our hashing mechanism.
Exploring Relational Semantics for Inductive Knowledge Graph Completion
Authors: Wang Changjian , Zhou Xiaofei , Pan Shirui , Dong Linhua , Song Zeliang , Sha Ying
Published: July 2022

Knowledge graph completion (KGC) aims to infer missing information in incomplete knowledge graphs (KGs). Most previous works only consider the transductive scenario where entities are existing in KGs, which cannot work effectively for the inductive scenario containing emerging entities. Recently some graph neural network-based methods have been proposed for inductive KGC by aggregating neighborhood information to capture some uncertainty semantics from the neighboring auxiliary triples. But these methods ignore the more general relational semantics underlying all the known triples that can provide richer information to represent emerging entities so as to satisfy the inductive scenario. In this paper, we propose a novel model called CFAG, which utilizes two granularity levels of relational semantics in a coarse-grained aggregator (CG-AGG) and a fine-grained generative adversarial net (FG-GAN), for inductive KGC. The CG-AGG firstly generates entity representations with multiple semantics through a hypergraph neural network-based global aggregator and a graph neural network-based local aggregator, and the FG-GAN further enhances entity representations with specific semantics through conditional generative adversarial nets. Experimental results on benchmark datasets show that our model outperforms state-of-the-art models for inductive KGC.
Powerful Graph Convolutional Networks with Adaptive Propagation Mechanism for Homophily and Heterophily
Authors: Wang Tao , Jin Di , Wang Rui , He Dongxiao , Huang Yuxiao
Published: July 2022

Graph Convolutional Networks (GCNs) have been widely applied in various fields due to their significant power on processing graph-structured data. Typical GCN and its variants work under a homophily assumption (i.e., nodes with same class are prone to connect to each other), while ignoring the heterophily which exists in many real-world networks (i.e., nodes with different classes tend to form edges). Existing methods deal with heterophily by mainly aggregating higher-order neighborhoods or combing the immediate representations, which leads to noise and irrelevant information in the result. But these methods did not change the propagation mechanism which works under homophily assumption (that is a fundamental part of GCNs). This makes it difficult to distinguish the representation of nodes from different classes. To address this problem, in this paper we design a novel propagation mechanism, which can automatically change the propagation and aggregation process according to homophily or heterophily between node pairs. To adaptively learn the propagation process, we introduce two measurements of homophily degree between node pairs, which is learned based on topological and attribute information, respectively. Then we incorporate the learnable homophily degree into the graph convolution framework, which is trained in an end-to-end schema, enabling it to go beyond the assumption of homophily. More importantly, we theoretically prove that our model can constrain the similarity of representations between nodes according to their homophily degree. Experiments on seven real-world datasets demonstrate that this new approach outperforms the state-of-the-art methods under heterophily or low homophily, and gains competitive performance under homophily.
DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events
Authors: Wilson Tyler , Tan Pang-Ning , Luo Lifeng
Published: July 2022

Geospatio-temporal data are pervasive across numerous application domains.These rich datasets can be harnessed to predict extreme events such as disease outbreaks, flooding, crime spikes, etc.
However, since the extreme events are rare, predicting them is a hard problem. Statistical methods based on extreme value theory provide a systematic way for modeling the distribution of extreme values. In particular, the generalized Pareto distribution (GPD) is useful for modeling the distribution of excess values above a certain threshold. However, applying such methods to large-scale geospatio-temporal data is a challenge due to the difficulty in capturing the complex spatial relationships between extreme events at multiple locations. This paper presents a deep learning framework for long-term prediction of the distribution of extreme values at different locations. We highlight its computational challenges and present a novel framework that combines convolutional neural networks with deep set and GPD. We demonstrate the effectiveness of our approach on a real-world dataset for modeling extreme climate events.
Multi-Type Urban Crime Prediction
Authors: Zhao Xiangyu , Fan Wenqi , Liu Hui , Tang Jiliang
Published: July 2022

Crime prediction plays an impactful role in enhancing public security and sustainable development of urban. With recent advances in data collection and integration technologies, a large amount of urban data with rich crime-related information and fine-grained spatio-temporal logs have been recorded. Such helpful information can boost our understandings of the temporal evolution and spatial factors of urban crimes and can enhance accurate crime prediction. However, the vast majority of existing crime prediction algorithms either do not distinguish different types of crime or treat each crime type separately, which fails to capture the intrinsic correlations among different types of crime. In this paper, we perform crime prediction exploiting the cross-type and spatio-temporal correlations of urban crimes. In particular, we verify the existence of correlations among different types of crime from temporal and spatial perspectives, and propose a coherent framework to mathematically model these correlations for crime prediction. Extensive experiments on real-world datasets validate the effectiveness of our framework.
OAM: An Option-Action Reinforcement Learning Framework for Universal Multi-Intersection Control
Authors: Liang Enming , Su Zicheng , Fang Chilin , Zhong Renxin
Published: July 2022

Efficient traffic signal control is an important means to alleviate urban traffic congestion. Reinforcement learning (RL) has shown great potentials in devising optimal signal plans that can adapt to dynamic traffic congestion. However, several challenges still need to be overcome. Firstly, a paradigm of state, action, and reward design is needed, especially for an optimality-guaranteed reward function. Secondly, the generalization of the RL algorithms is hindered by the varied topologies and physical properties of intersections. Lastly, enhancing the cooperation between intersections is needed for large network applications. To address these issues, the Option-Action RL framework for universal Multi-intersection control (OAM) is proposed. Based on the well-known cell transmission model, we first define a lane-cell-level state to better model the traffic flow propagation. Based on this physical queuing dynamics, we propose a regularized delay as the reward to facilitate temporal credit assignment while maintaining the equivalence with minimizing the average travel time. We then recapitulate the phase actions as the constrained combinations of lane options and design a universal neural network structure to realize model generalization to any intersection with any phase definition. The multiple-intersection cooperation is then rigorously discussed using the potential game theory.

We test the OAM algorithm under four networks with different settings, including a city-level scenario with 2,048 intersections using synthetic and real-world datasets. The results show that the OAM can outperform the state-of-the-art controllers in reducing the average travel time.
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
Authors: Zhan Xianyuan , Xu Haoran , Zhang Yue , Zhu Xiangyu , Yin Honglei , Zheng Yu
Published: July 2022

Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy industry. We develop a new data-driven AI system, namely DeepThermal, to optimize the combustion control strategy for TPGUs. At its core, is a new model-based offline reinforcement learning (RL) framework, called MORE, which leverages historical operational data of a TGPU to solve a highly complex constrained Markov decision process problem via purely offline training. In DeepThermal, we first learn a data-driven combustion process simulator from the offline dataset. The RL agent of MORE is then trained by combining real historical data as well as carefully filtered and processed simulation data through a novel restrictive exploration scheme. DeepThermal has been successfully deployed in four large coal-fired thermal power plants in China. Real-world experiments show that DeepThermal effectively improves the combustion efficiency of TPGUs. We also report the superior performance of MORE by comparing with the state-of-the-art algorithms on the standard offline RL benchmarks.
Locally Fair Partitioning
Authors: Agarwal Pankaj K. , Ko Shao-Heng , Munagala Kamesh , Taylor Erin
Published: July 2022

We model the societal task of redistricting political districts as a partitioning problem: Given a set of n points in the plane, each belonging to one of two parties, and a parameter k, our goal is to compute a partition P of the plane into regions so that each region contains roughly s = n/k points. P should satisfy a notion of "local" fairness, which is related to the notion of core, a well-studied concept in cooperative game theory. A region is associated with the majority party in that region, and a point is unhappy in P if it belongs to the minority party. A group D of roughly s contiguous points is called a deviating group with respect to P if majority of points in D are unhappy in P. The partition P is locally fair if there is no deviating group with respect to P.

This paper focuses on a restricted case when points lie in 1D. The problem is non-trivial even in this case. We consider both adversarial and "beyond worst-case" settings for this problem. For the former, we characterize the input parameters for which a locally fair partition always exists; we also show that a locally fair partition may not exist for certain parameters. We then consider input models where there are "runs" of red and blue points. For such clustered inputs, we show that a locally fair partition may not exist for certain values of s, but an approximate locally fair partition exists if we allow some regions to have smaller sizes. We finally present a polynomial-time algorithm for computing a locally fair partition if one exists.
Universal and Tight Online Algorithms for Generalized-Mean Welfare
Authors: Barman Siddharth , Khan Arindam , Maiti Arnab
Published: July 2022

We study fair and efficient allocation of divisible goods, in an online manner, among n agents. The goods arrive online in a sequence of T time periods. The agents' values for a good are revealed only after its arrival, and the online algorithm needs to fractionally allocate the good, immediately and irrevocably, among the agents. Towards a unifying treatment of fairness and economic efficiency objectives, we develop an algorithmic framework for finding online allocations to maximize the generalized mean of the values received by the agents. In particular, working with the assumption that each agent's value for the grand bundle of goods is appropriately scaled, we address online maximization of p-mean welfare. Parameterized by an exponent term p in (-infty, 1], these means encapsulate a range of welfare functions, including social welfare (p=1), egalitarian welfare (p to -infty), and Nash social welfare (p to 0).

We present a simple algorithmic template that takes a threshold as input and, with judicious choices for this threshold, leads to both universal and tailored competitive guarantees. First, we show that one can compute online a single allocation that O (sqrt(n) log n)-approximates the optimal p-mean welfare for all p
The Secretary Problem with Competing Employers on Random Edge Arrivals
Authors: Bei Xiaohui , Zhang Shengyu
Published: July 2022

The classic secretary problem concerns the problem of an employer facing a random sequence of candidates and making online hiring decisions to try to hire the best candidate. In this paper, we study a game-theoretic generalization of the secretary problem where a set of employers compete with each other to hire the best candidate. Different from previous secretary market models, our model assumes that the sequence of candidates arriving at each employer is uniformly random but independent from other sequences. We consider two versions of this secretary game where employers can have adaptive or non-adaptive strategies, and provide characterizations of the best response and Nash equilibrium of each game.
A Calculus for Computing Structured Justifications for Election Outcomes
Authors: Boixel Arthur , Endriss Ulle , Haan Ronald de
Published: July 2022

In the context of social choice theory, we develop a tableau-based calculus for reasoning about voting rules. This calculus can be used to obtain structured explanations for why a given set of axioms justifies a given election outcome for a given profile of voter preferences. We then show how to operationalise this calculus, using a combination of SAT solving and answer set programming, to arrive at a flexible framework for presenting human-readable justifications to users.
Signaling in Posted Price Auctions
Authors: Castiglioni Matteo , Romano Giulia , Marchesi Alberto , Gatti Nicola
Published: July 2022

We study single-item single-unit Bayesian posted price auctions, where buyers arrive sequentially and their valuations for the item being sold depend on a random, unknown state of nature. The seller has complete knowledge of the actual state and can send signals to the buyers so as to disclose information about it. For instance, the state of nature may reflect the condition and/or some particular features of the item, which are known to the seller only. The problem faced by the seller is about how to partially disclose information about the state so as to maximize revenue. Unlike classical signaling problems, in this setting, the seller must also correlate the signals being sent to the buyers with some price proposals for them. This introduces additional challenges compared to standard settings. We consider two cases: the one where the seller can only send signals publicly visible to all buyers, and the case in which the seller can privately send a different signal to each buyer. As a first step, we prove that, in both settings, the problem of maximizing the seller's revenue does not admit an FPTAS unless P=NP, even for basic instances with a single buyer. As a result, in the rest of the paper, we focus on designing PTASs. In order to do so, we first introduce a unifying framework encompassing both public and private signaling, whose core result is a decomposition lemma that allows focusing on a finite set of possible buyers' posteriors. This forms the basis on which our PTASs are developed. In particular, in the public signaling setting, our PTAS employs some ad hoc techniques based on linear programming, while our PTAS for the private setting relies on the ellipsoid method to solve an exponentially-sized LP in polynomial time. In the latter case, we need a custom approximate separation oracle, which we implement with a dynamic programming approach.
Bayesian Persuasion in Sequential Decision-Making
Authors: Gan Jiarui , Majumdar Rupak , Radanovic Goran , Singla Adish
Published: July 2022

We study a dynamic model of Bayesian persuasion in sequential decision-making settings. An informed principal observes an external parameter of the world and advises an uninformed agent about actions to take over time. The agent takes actions in each time step based on the current state, the principal's advice/signal, and beliefs about the external parameter. The action of the agent updates the state according to a stochastic process. The model arises naturally in many applications, e.g., an app (the principal) can advice the user (the agent) on possible choices between actions based on additional real-time information the app has. We study the problem of designing a signaling strategy from the principal's point of view. We show that the principal has an optimal strategy against a myopic agent, who only optimizes their rewards locally, and the optimal strategy can be computed in polynomial time. In contrast, it is NP-hard to approximate an optimal policy against a far-sighted agent. Further, we show that if the principal has the power to threaten the agent by not providing future signals, then we can efficiently design a threat-based strategy. This strategy guarantees the principal's payoff as if playing against an agent who is far-sighted but myopic to future signals.
Reforming an Envy-Free Matching
Authors: Ito Takehiro , Iwamasa Yuni , Kakimura Naonori , Kamiyama Naoyuki , Kobayashi Yusuke , Nozaki Yuta , Okamoto Yoshio , Ozeki Kenta
Published: July 2022

We consider the problem of reforming an envy-free matching when each agent is assigned a single item. Given an envy-free matching, we consider an operation to exchange the item of an agent with an unassigned item preferred by the agent that results in another envy-free matching. We repeat this operation as long as we can. We prove that the resulting envy-free matching is uniquely determined up to the choice of an initial envy-free matching, and can be found in polynomial time. We call the resulting matching a reformist envy-free matching, and then we study a shortest sequence to obtain the reformist envy-free matching from an initial envy-free matching. We prove that a shortest sequence is computationally hard to obtain even when each agent accepts at most four items and each item is accepted by at most three agents. On the other hand, we give polynomial-time algorithms when each agent accepts at most three items or each item is accepted by at most two agents. Inapproximability and fixed-parameter (in)tractability are also discussed.
The Semi-random Likelihood of Doctrinal Paradoxes
Authors: Liu Ao , Xia Lirong
Published: July 2022

When aggregating logically interconnected judgements from n agents, the result might be logically inconsistent. This phenomenon is known as the doctrinal paradox, which plays a central role in the field of judgement aggregation. Previous work has mostly focused on the worst-case analysis of the doctrinal paradox, leading to many impossibility results. Little is known about its likelihood of occurrence in practical settings, except for the study under certain distributions by List in 2005. In this paper, we characterize the likelihood of the doctrinal paradox under a general and realistic model called semi-random social choice framework (proposed by Xia in 2020). In the framework, agents' ground truth judgements can be arbitrarily correlated, while the noises are independent. Our main theorem states that under mild conditions, the semi-random likelihood of the doctrinal paradox is either 0, exp(-Θ(n)), Θ(n\^~(-0.5)) or Θ(1). This not only answers open questions by List in 2005, but also draws clear lines between situations with frequent paradoxes and with vanishing paradoxes.
Online Elicitation of Necessarily Optimal Matchings
Authors: Peters Jannik
Published: July 2022

In this paper, we study the problem of eliciting preferences of agents in the house allocation model. For this we build on a recently introduced model and focus on the task of eliciting preferences to find matchings which are necessarily optimal, i.e., optimal under all possible completions of the elicited preferences. In particular, we investigate the elicitation of necessarily Pareto optimal (NPO) and necessarily rank-maximal (NRM) matchings. Most importantly, we answer an open question and give an online algorithm for eliciting an NRM matching in the next-best query model which is 3/2-competitive, i.e., it takes at most 3/2 as many queries as an optimal algorithm. Besides this, we extend this field of research by introducing two new natural models of elicitation and by studying both the complexity of determining whether a necessarily optimal matching exists in them, and by giving online algorithms for these models.
Online Task Assignment Problems with Reusable Resources
Authors: Sumita Hanna , Ito Shinji , Takemura Kei , Hatano Daisuke , Fukunaga Takuro , Kakimura Naonori , Kawarabayashi Ken-ichi
Published: July 2022

We study online task assignment problem with reusable resources, motivated by practical applications such as ridesharing, crowdsourcing and job hiring. In the problem, we are given a set of offline vertices (agents), and, at each time, an online vertex (task) arrives randomly according to a known time-dependent distribution. Upon arrival, we assign the task to agents immediately and irrevocably. The goal of the problem is to maximize the expected total profit produced by completed tasks. The key features of our problem are (1) an agent is reusable, i.e., an agent comes back to the market after completing the assigned task, (2) an agent may reject the assigned task to stay the market, and (3) a task may accommodate multiple agents. The setting generalizes that of existing work in which an online task is assigned to one agent under (1).

In this paper, we propose an online algorithm that is 1/2-competitive for the above setting, which is tight. Moreover, when each agent can reject assigned tasks at most Δ times, the algorithm is shown to have the competitive ratio Δ/(3Δ-1), which is at least 1/3. We also evaluate our proposed algorithm with numerical experiments.
Deceptive Decision-Making under Uncertainty
Authors: Savas Yagiz , Verginis Christos K. , Topcu Ufuk
Published: July 2022

We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks in stochastic, complex environments. By modeling the agent's behavior as a Markov decision process, we consider a setting where the agent aims to reach one of multiple potential goals while deceiving outside observers about its true goal. We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies via linear programming. The proposed approach enables the agent to exhibit a variety of tunable deceptive behaviors while ensuring the satisfaction of probabilistic constraints on the behavior. We evaluate the performance of the proposed approach via comparative user studies and present a case study on the streets of Manhattan, New York, using real travel time distributions.
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework
Authors: Zou Xinyi , Yan Yan , Xue Jing-Hao , Chen Si , Wang Hanzi
Published: July 2022

Human emotions involve basic and compound facial expressions. However, current research on facial expression recognition (FER) mainly focuses on basic expressions, and thus fails to address the diversity of human emotions in practical scenarios. Meanwhile, existing work on compound FER relies heavily on abundant labeled compound expression training data, which are often laboriously collected under the professional instruction of psychology. In this paper, we study compound FER in the cross-domain few-shot learning setting, where only a few images of novel classes from the target domain are required as a reference. In particular, we aim to identify unseen compound expressions with the model trained on easily accessible basic expression datasets. To alleviate the problem of limited base classes in our FER task, we propose a novel Emotion Guided Similarity Network (EGS-Net), consisting of an emotion branch and a similarity branch, based on a two-stage learning framework. Specifically, in the first stage, the similarity branch is jointly trained with the emotion branch in a multi-task fashion. With the regularization of the emotion branch, we prevent the similarity branch from overfitting to sampled base classes that are highly overlapped across different episodes. In the second stage, the emotion branch and the similarity branch play a “two-student game” to alternately learn from each other, thereby further improving the inference ability of the similarity branch on unseen compound expressions. Experimental results on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed method against several state-of-the-art methods.
Multi-Relational Graph Representation Learning with Bayesian Gaussian Process Network
Authors: Chen Guanzheng , Fang Jinyuan , Meng Zaiqiao , Zhang Qiang , Liang Shangsong
Published: July 2022

Learning effective representations of entities and relations for knowledge graphs (KGs) is critical to the success of many multi-relational learning tasks. Existing methods based on graph neural networks learn a deterministic embedding function, which lacks sufficient flexibility to explore better choices when dealing with the imperfect and noisy KGs such as the scarce labeled nodes and noisy graph structure. To this end, we propose a novel multi-relational graph Gaussian Process network (GGPN), which aims to improve the flexibility of deterministic methods by simultaneously learning a family of embedding functions, i.e., a stochastic embedding function. Specifically, a Bayesian Gaussian Process (GP) is proposed to model the distribution of this stochastic function and the resulting representations are obtained by aggregating stochastic function values, i.e., messages, from neighboring entities. The two problems incurred when leveraging GP in GGPN are the proper choice of kernel function and the cubic computational complexity. To address the first problem, we further propose a novel kernel function that can explicitly take the diverse relations between each pair of entities into account and be adaptively learned in a data-driven way. We address the second problem by reformulating GP as a Bayesian linear model, resulting in a linear computational complexity. With these two solutions, our GGPN can be efficiently trained in an end-to-end manner. We evaluate our GGPN in link prediction and entity classification tasks, and the experimental results demonstrate the superiority of our method. Our code is available at https://github.com/sysu-gzchen/GGPN.
Machine Learning for Utility Prediction in Argument-Based Computational Persuasion
Authors: Donadello Ivan , Hunter Anthony , Teso Stefano , Dragoni Mauro
Published: July 2022

Automated persuasion systems (APS) aim to persuade a user to believe something by entering into a dialogue in which arguments and counterarguments are exchanged. To maximize the probability that an APS is successful in persuading a user, it can identify a global policy that will allow it to select the best arguments it presents at each stage of the dialogue whatever arguments the user presents. However, in real applications, such as for healthcare, it is unlikely the utility of the outcome of the dialogue will be the same, or the exact opposite, for the APS and user. In order to deal with this situation, games in extended form have been harnessed for argumentation in Bi-party Decision Theory. This opens new problems that we address in this paper: (1) How can we use Machine Learning (ML) methods to predict utility functions for different subpopulations of users? and (2) How can we identify for a new user the best utility function from amongst those that we have learned. To this extent, we develop two ML methods, EAI and EDS, that leverage information coming from the users to predict their utilities. EAI is restricted to a fixed amount of information, whereas EDS can choose the information that best detects the subpopulations of a user. We evaluate EAI and EDS in a simulation setting and in a realistic case study concerning healthy eating habits. Results are promising in both cases, but EDS is more effective at predicting useful utility functions.
On the Complexity of Inductively Learning Guarded Clauses
Authors: Draghici Andrei , Gottlob Georg , Lanzinger Matthias
Published: July 2022

We investigate the computational complexity of mining guarded clauses from clausal datasets through the framework of inductive logic programming (ILP). We show that learning guarded clauses is NP-complete and thus one step below the Sigma2-complete task of learning Horn clauses on the polynomial hierarchy. Motivated by practical applications on large datasets we identify a natural tractable fragment of the problem. Finally, we also generalise all of our results to k-guarded clauses for constant k.
Linear-Time Verification of Data-Aware Dynamic Systems with Arithmetic
Authors: Felli Paolo , Montali Marco , Winkler Sarah
Published: July 2022

Combined modeling and verification of dynamic systems and the data they operate on has gained momentum in AI and in several application domains. We investigate the expressive yet concise framework of data-aware dynamic systems (DDS), extending it with linear arithmetic, and providing the following contributions.
First, we introduce a new, semantic property of “finite summary”, which guarantees the existence of a faithful finite-state abstraction. We rely on this to show that checking whether a witness exists for a linear-time, finite-trace property is decidable for DDSs with finite summary. Second, we demonstrate that several decidability conditions studied in formal methods and database theory can be seen as concrete, checkable instances of this property. This also gives rise to new decidability results. Third, we show how the abstract, uniform property of finite summary leads to modularity results: a system enjoys finite summary if it can be partitioned appropriately into smaller systems that possess the property. Our results allow us to analyze systems that were out of reach in earlier approaches. Finally, we demonstrate the feasibility of our approach in a prototype implementation.
Rushing and Strolling among Answer Sets – Navigation Made Easy
Authors: Fichte Johannes Klaus , Gaggl Sarah Alice , Rusovac Dominik
Published: July 2022

Answer set programming (ASP) is a popular declarative programming paradigm
with a wide range of applications in artificial intelligence. Oftentimes,
when modeling an AI problem with ASP, and in particular when we are interested
beyond simple search for optimal solutions, an actual solution, differences
between solutions, or number of solutions of the ASP program matter. For
example, when a user aims to identify a specific answer set according to her
needs, or requires the total number of diverging solutions to comprehend
probabilistic applications such as reasoning in medical domains. Then, there
are only certain problem specific and handcrafted encoding techniques
available to navigate the solution space of ASP programs, which is oftentimes
not enough. In this paper, we propose a formal and general framework for
interactive navigation toward desired subsets of answer sets analogous to
faceted browsing. Our approach enables the user to explore the solution space
by consciously zooming in or out of sub-spaces of solutions at a certain
configurable pace. We illustrate that weighted faceted navigation is
computationally hard. Finally, we provide an implementation of our approach
that demonstrates the feasibility of our framework for incomprehensible
solution spaces.
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks
Authors: Hoernle Nick , Karampatsis Rafael Michael , Belle Vaishak , Gal Kobi
Published: July 2022

We propose a novel way to incorporate expert knowledge into the training of deep neural networks. Many approaches encode domain constraints directly into the network architecture, requiring non-trivial or domain-specific engineering. In contrast, our approach, called MultiplexNet, represents domain knowledge as a quantifier-free logical formula in disjunctive normal form (DNF) which is easy to encode and to elicit from human experts. It introduces a latent Categorical variable that learns to choose which constraint term optimizes the error function of the network and it compiles the constraints directly into the output of existing learning algorithms. We demonstrate the efficacy of this approach empirically on several classical deep learning tasks, such as density estimation and classification in both supervised and unsupervised settings where prior knowledge about the domains was expressed as logical constraints. Our results show that the MultiplexNet approach learned to approximate unknown distributions well, often requiring fewer data samples than the alternative approaches. In some cases, MultiplexNet finds better solutions than the baselines; or solutions that could not be achieved with the alternative approaches. Our contribution is in encoding domain knowledge in a way that facilitates inference. We specifically focus on quantifier-free logical formulae that are specified over the output domain of a network. We show that this approach is both efficient and general; and critically, our approach guarantees 100% constraint satisfaction in a network's output.
Tractable Explanations for d-DNNF Classifiers
Authors: Huang Xuanxiang , Izza Yacine , Ignatiev Alexey , Cooper Martin , Asher Nicholas , Marques-Silva Joao
Published: July 2022

Compilation into propositional languages finds a growing number of practical uses, including in constraint programming, diagnosis and machine learning (ML), among others. One concrete example is the use of propositional languages as classifiers, and one natural question is how to explain the predictions made. This paper shows that for classifiers represented with some of the best-known propositional languages, different kinds of explanations can be computed in polynomial time. These languages include deterministic decomposable negation normal form (d-DNNF), and so any propositional language that is strictly less succinct than d-DNNF. Furthermore, the paper describes optimizations, specific to Sentential Decision Diagrams (SDDs), which are shown to yield more efficient algorithms in practice.
How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View
Authors: Li Ren , Cao Yanan , Zhu Qiannan , Bi Guanqun , Fang Fang , Liu Yi , Li Qian
Published: July 2022

Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), a trained model can still correctly predict t from (h, r, ?), or h from (?, r, t), such extrapolation ability is impressive. However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate. Therefore in this work, we attempt to study the KGE extrapolation of two problems: 1. How does KGE extrapolate to unseen data? 2. How to design the KGE model with better extrapolation ability?
For the problem 1, we first discuss the impact factors for extrapolation and from relation, entity and triple level respectively, propose three Semantic Evidences (SEs), which can be observed from train set and provide important semantic information for extrapolation. Then we verify the effectiveness of SEs through extensive experiments on several typical KGE methods.
For the problem 2, to make better use of the three levels of SE, we propose a novel GNN-based KGE model, called Semantic Evidence aware Graph Neural Network (SE-GNN). In SE-GNN, each level of SE is modeled explicitly by the corresponding neighbor pattern, and merged sufficiently by the multi-layer aggregation, which contributes to obtaining more extrapolative knowledge representation.
Finally, through extensive experiments on FB15k-237 and WN18RR datasets, we show that SE-GNN achieves state-of-the-art performance on Knowledge Graph Completion task and performs a better extrapolation ability. Our code is available at https://github.com/renli1024/SE-GNN.
Using Conditional Independence for Belief Revision
Authors: Lynn Matthew James , Delgrande James P. , Peppas Pavlos
Published: July 2022

We present an approach to incorporating qualitative assertions of conditional irrelevance into belief revision, in order to address the limitations of existing work which considers only unconditional irrelevance. These assertions serve to enforce the requirement of minimal change to existing beliefs, while also suggesting a route to reducing the computational cost of belief revision by excluding irrelevant beliefs from consideration. In our approach, a knowledge engineer specifies a collection of multivalued dependencies that encode domain-dependent assertions of conditional irrelevance in the knowledge base. We consider these as capturing properties of the underlying domain which should be taken into account during belief revision. We introduce two related notions of what it means for a multivalued dependency to be taken into account by a belief revision operator: partial and full compliance. We provide characterisations of partially and fully compliant belief revision operators in terms of semantic conditions on their associated faithful rankings. Using these characterisations, we show that the constraints for partially and fully compliant belief revision operators are compatible with the AGM postulates. Finally, we compare our approach to existing work on unconditional irrelevance in belief revision.
Compilation of Aggregates in ASP Systems
Authors: Mazzotta Giuseppe , Ricca Francesco , Dodaro Carmine
Published: July 2022

Answer Set Programming (ASP) is a well-known declarative AI formalism for knowledge representation and reasoning. State-of-the-art ASP implementations employ the ground&solve approach, and they were successfully applied to industrial and academic problems. Nonetheless there are classes of ASP programs whose evaluation is not efficient (sometimes not feasible) due to the combinatorial blow-up of the program produced by the grounding step. Recent researches suggest that compilation-based techniques can mitigate the grounding bottleneck problem. However, no compilation-based technique has been developed for ASP programs that contain aggregates, which are one of the most relevant and commonly-employed constructs of ASP. In this paper, we propose a compilation-based approach for ASP programs with aggregates. We implement it on top of a state-of-the-art ASP system, and evaluate the performance on publicly-available benchmarks. Experiments show our approach is effective on ground-intensive ASP programs.
Knowledge Compilation Meets Logical Separability
Authors: Qiu Junming , Li Wenqing , Xiao Zhanhao , Guan Quanlong , Fang Liangda , Lai Zhao-Rong , Dong Qian
Published: July 2022

Knowledge compilation is an alternative solution to address demanding reasoning tasks with high complexity via converting knowledge bases into a suitable target language. Interestingly, the notion of logical separability, proposed by Levesque, offers a general explanation for the tractability of clausal entailment for two remarkable languages: decomposable negation normal form and prime implicates. It is interesting to explore what role logical separability on earth plays in problem tractability. In this paper, we apply the notion of logical separability in three reasoning problems within the context of propositional logic: satisfiability check (CO), clausal entailment check (CE) and model counting (CT), contributing to three corresponding polytime procedures. We provide three logical separability based properties: CO- logical separability, CE-logical separability and CT-logical separability. We then identify three novel normal forms: CO-LSNNF, CE-LSNNF and CT-LSNNF based on the above properties. Besides, we show that every normal form is the necessary and sufficient condition under which the corresponding procedure is correct. We finally integrate the above four normal forms into the knowledge compilation map.
Propositional Encodings of Acyclicity and Reachability by Using Vertex Elimination
Authors: Rankooh Masood Feyzbakhsh , Rintanen Jussi
Published: July 2022

We introduce novel methods for encoding acyclicity and s-t-reachability constraints for propositional formulas with underlying directed graphs, based on vertex elimination graphs, which makes them suitable for cases where the underlying graph has a low directed elimination width. In contrast to solvers with ad hoc constraint propagators for graph constraints such as GraphSAT, our methods encode these constraints as standard propositional clauses, making them directly applicable with any SAT solver. An empirical study demonstrates that our methods do often outperform both earlier encodings of these constraints as well as GraphSAT especially when underlying graphs have a low directed elimination width.