bluuebunny/crossref_metadata_2025 huggingface dataset:
Tailoring Local Search for Partial MaxSAT
Authors: Cai Shaowei , Luo Chuan , Thornton John , Su Kaile
Published: June 2022

Partial MaxSAT (PMS) is a generalization to SAT and MaxSAT. Many real world problems can be encoded into PMS in a more natural and compact way than SAT and MaxSAT. In this paper, we propose new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses. We use these ideas to develop a local search PMS algorithm called {\it Dist}. Experimental results on PMS benchmarks from MaxSAT Evaluation 2013 show that {\it Dist} significantly outperforms state-of-the-art PMS algorithms, including both local search algorithms and complete ones, on random and crafted benchmarks. For the industrial benchmark, {\it Dist} dramatically outperforms previous local search algorithms and is comparable with complete algorithms.
Linear-Time Filtering Algorithms for the Disjunctive Constraint
Authors: Fahimi Hamed , Quimper Claude-Guy
Published: June 2022

We present three new filtering algorithms for the Disjunctive constraint that all have a linear running time complexity in the number of tasks. The first algorithm filters the tasks according to the rules of the time tabling. The second algorithm performs an overload check that could also be used for the Cumulative constraint. The third algorithm enforces the rules of detectable precedences. The two last algorithms use a new data structure that we introduce and that we call the time line. This data structure provides many constant time operations that were previously implemented in logarithmic time by the Theta-tree data structure. Experiments show that these new algorithms are competitive even for a small number of tasks and outperform existing algorithms as the number of tasks increases.
Locality-Constrained Low-Rank Coding for Image Classification
Authors: Jiang Ziheng , Guo Ping , Peng Lihong
Published: June 2022

Low-rank coding (LRC), originated from matrix decomposition, is recently introduced into image classification. Following the standard bag-of-words (BOW) pipeline, when coding the data matrix in the sense of low-rankness incorporates contextual information into the traditional BOW model, this can capture the dependency relationship among neighbor patches. It differs from the traditional sparse coding paradigms which encode patches independently. Current LRC-based methods use l_1 norm to increase the discrimination and sparseness of the learned codes. However, such methods fail to consider the local manifold structure between dataspace and dictionary space. To solve this problem, we propose a locality-constrained low-rank coding (LCLR) algorithm for image representations. By using the geometric structure information as a regularization term,we can obtain more discriminative representations. In addition, we present a fast and stable online algorithmto solve the optimization problem. In the experiments,we evaluate LCLR with four benchmarks, including one face recognition dataset (extended Yale B), one handwrittendigit recognition dataset (USPS), and two image datasets (Scene13 for scene recognition and Caltech101 for object recognition). Experimental results show thatour approach outperforms many state-of-the-art algorithmseven with a linear classifier.
Exploiting Competition Relationship for Robust Visual Recognition
Authors: Du Liang , Ling Haibin
Published: June 2022

Joint learning of similar tasks has been a popular trend in visual recognition and proven to be beneficial. Between-task similarity often provides useful cues, such as feature sharing, for learning visual classifiers. By contrast, the competition relationship between visual recognition tasks (e.g., content independent writer identification and handwriting recognition) remains largely under-explored. A key challenge in visual recognition is to select the most discriminating features and remove irrelevant features related to intra-class variations. With the help of auxiliary competing tasks, we can identify such features within a joint learning model exploiting the competition relationship.Motivated by this intuition, we propose a novel way to exploit competition relationship for solving visual recognition problems. Specifically, given a target task and its competing tasks, we jointly model them by a generalized additive regression model with a competition constraint. This constraint effectively discourages choosing of irrelevant features (weak learners) that support the auxiliary competing tasks. We name the proposed algorithm CompBoost. In our study, CompBoost is applied to two visual recognition applications: (1) content-independent writer identification from handwriting scripts by exploiting competing tasks of handwriting recognition, and (2) actor-independent facial expression recognition by exploiting competing tasks of face recognition. In both experiments our approach demonstrates promising performance gains by exploiting the between-task competition.
Learning to Recognize Novel Objects in One Shot through Human-Robot Interactions in Natural Language Dialogues
Authors: Krause Evan , Zillich Michael , Williams Thomas , Scheutz Matthias
Published: June 2022

Being able to quickly and naturally teach robots new knowledge is critical for many future open-world human-robot interaction scenarios. In this paper we present a novel approach to using natural language context for one-shot learning of visual objects, where the robot is immediately able to recognize the described object. We describe the architectural components and demonstrate the proposed approach on a robotic platform in a proof-of-concept evaluation.
Robust Protection of Fisheries with COmPASS
Authors: Haskell William , Kar Debarun , Fang Fei , Tambe Milind , Cheung Sam , Denicola Elizabeth
Published: June 2022

Fish stocks around the world are in danger from illegal fishing. In collaboration with the U.S. Coast Guard (USCG), we work to defend fisheries from illegal fisherman (henceforth called Lanchas) in the U.S. Gulf of Mexico. We have developed the COmPASS (Conservative Online Patrol ASSistant) system to design USCG patrols against the Lanchas. In this application, we face a population of Lanchas with heterogeneous behavior who fish frequently. We have some data about these Lanchas, but not enough to fit a statistical model. Previous security patrol assistants have focused on counterterrorism in one-shot games where adversaries are assumed to be perfectly rational, and much less data about their behavior is available. COmPASS is novel because: (i) it emphasizes environmental crime; (ii) it is based on a repeated Stackelberg game; (iii) it allows for bounded rationality of the Lanchas and it offers a robust approach against the heterogeneity of the Lancha population; and (iv) it can learn from sparse Lancha data. We report the effectiveness of COmPASS in the Gulf in our numerical experiments based on real fish data. The COmPASS system is to be tested by USCG.
A Unified Framework for Augmented Reality and Knowledge-Based Systems in Maintaining Aircra
Authors: Jo Geun-Sik , Oh Kyeong-Jin , Ha Inay , Lee Kee-Sung , Hong Myung-Duk , Neumann Ulrich , You Suya
Published: June 2022

Aircraft maintenance and training play one of the most important roles in ensuring flight safety. The maintenance process usually involves massive numbers of components and substantial procedural knowledge of maintenance procedures. Maintenance tasks require technicians to follow rigorous procedures to prevent operational errors in the maintenance process. In addition, the maintenance time is a cost-sensitive issue for airlines. This paper proposes intelligent augmented reality (IAR) system to minimize operation errors and time-related costs and help aircraft technicians cope with complex tasks by using an intuitive UI/UX interface for their maintenance tasks. The IAR system is composed mainly of three major modules: 1) the AR module 2) the knowledge-based system (KBS) module 3) a unified platform with an integrated UI/UX module between the AR and KBS modules. The AR module addresses vision-based tracking, annotation, and recognition. The KBS module deals with ontology-based resources and context management. Overall testing of the IAR system is conducted at Korea Air Lines (KAL) hangars. Tasks involving the removal and installation of pitch trimmers in landing gear are selected for benchmarking purposes, and according to the results, the proposed IAR system can help technicians to be more effective and accurate in performing their maintenance tasks.
STREETS: Game-Theoretic Traffic Patrolling with Exploration and Exploitation
Authors: Brown Matthew , Saisubramanian Sandhya , Varakantham Pradeep , Tambe Milind
Published: June 2022

To dissuade reckless driving and mitigate accidents, cities deploy resources to patrol roads. In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defenderattacker Stackelberg game. Previous work on Stackelberg security games has focused extensively on counterterrorism settings. STREETS moves beyond counterterrorism and represents the first use of Stackelberg games for traffic patrolling, in the process providing a novel algorithm for solving such games that addresses three major challenges in modeling and scale-up. First, there exists a high degree of unpredictability in travel times through road networks, which we capture using a Markov Decision Process for planning the patrols of the defender (the police) in the game. Second, modeling all possible police patrols and their interactions with a large number of adversaries (drivers) introduces a significant scalability challenge. To address this challenge we apply a compact game representation in a novel fashion combined with adversary and state sampling. Third, patrol strategies must balance exploitation (minimizing violations) with exploration (maximizing omnipresence), a tradeoff we model by solving a biobjective optimization problem. We present experimental results using real-world traffic data from Singapore. This work is done in collaboration with the Singapore Ministry of Home Affairs and is currently being evaluated by the Singapore Police Force.
Shallow Blue: Lego-Based Embodied AI as a Platform for Cross-Curricular Project Based Learning
Authors: Selkowitz Robert , Burhan Debra
Published: June 2022

We report on Shallow Blue (SB), an autonomous chess agent constructed by a small group of faculty and undergraduate students at Canisius College. In addition to pushing the limits of consumer grade components at low cost, SB is a focal point for interdisciplinary student projects spanning computer science, engineering, and physics. We demonstrate that undergraduate students can engage in rich, long-term robotic design and applied Artificial Intelligence (AI) from both hardware and software perspectives. Student outcomes of SB include senior theses, conference presentations, peer-reviewed publications, and admission to graduate programs. Students who participated also report substantial development in skills and knowledge applicable to their post-undergraduate education and careers.
TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings
Authors: Guo Guibing , Zhang Jie , Yorke-Smith Neil
Published: June 2022

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.
Burst Time Prediction in Cascades
Authors: Wang Senzhang , Yan Zhao , Hu Xia , Yu Philip S. , Li Zhoujun
Published: June 2022

Studying the bursty nature of cascades in social media is practically important in many applications such as product sales prediction, disaster relief, and stock market prediction. Although the cascade volume prediction has been extensively studied, how to predict when a burst will come remains an open problem. It is challenging to predict the time of the burst due to the ``quick rise and fall'' pattern and the diverse time spans of the cascades. To this end, this paper proposes a classification based approach for burst time prediction by utilizing and modeling rich knowledge in information diffusion. Particularly, we first propose a time window based approach to predict in which time window the burst will appear. This paves the way to transform the time prediction task to a classification problem. To address the challenge that the original time series data of the cascade popularity only are not sufficient for predicting cascades with diverse magnitudes and time spans, we explore rich information diffusion related knowledge and model them in a scale-independent manner. Extensive experiments on a Sina Weibo reposting dataset demonstrate the superior performance of the proposed approach in accurately predicting the burst time of posts.
A New Granger Causal Model for Influence Evolution in Dynamic Social Networks: The Case of DBLP
Authors: Chikhaoui Belkacem , Chiazzaro Mauricio , Wang Shengrui
Published: June 2022

This paper addresses a new problem concerning the evolution of influence relationships between communities in dynamic social networks. A weighted temporal multigraph is employed to represent the dynamics of the social networks and analyze the influence relationships between communities over time. To ensure the interpretability of the knowledge discovered, evolution of the influence relationships is assessed by introducing the Granger causality. Through extensive experiments, we empirically demonstrate the suitability of our model for studying the evolution of influence between communities. Moreover, we empirically show how our model is able to accurately predict the influence of communities over time using random forest regression.
A Personalized Interest-Forgetting Markov Model for Recommendations
Authors: Chen Jun , Wang Chaokun , Wang Jianmin
Published: June 2022

Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.
Relating Romanized Comments to News Articles by Inferring Multi-Glyphic Topical Correspondence
Authors: Tholpadi Goutham , Das Mrinal , Bansal Trapit , Bhattacharyya Chiranjib
Published: June 2022

Commenting is a popular facility provided by news sites. Analyzing such user-generated content has recently attracted research interest. However, in multilingual societies such as India, analyzing such user-generated content is hard due to several reasons: (1) There are more than 20 official languages but linguistic resources are available mainly for Hindi. It is observed that people frequently use romanized text as it is easy and quick using an English keyboard, resulting in multi-glyphic comments, where the texts are in the same language but in different scripts. Such romanized texts are almost unexplored in machine learning so far. (2) In many cases, comments are made on a specific part of the article rather than the topic of the entire article. Off-the-shelf methods such as correspondence LDA are insufficient to model such relationships between articles and comments. In this paper, we extend the notion of correspondence to model multi-lingual, multi-script, and inter-lingual topics in a unified probabilistic model called the Multi-glyphic Correspondence Topic Model (MCTM). Using several metrics, we verify our approach and show that it improves over the state-of-the-art.
Content-Based Collaborative Filtering for News Topic Recommendation
Authors: Lu Zhongqi , Dou Zhicheng , Lian Jianxun , Xie Xing , Yang Qiang
Published: June 2022

News recommendation has become a big attraction with which major Web search portals retain their users. Two effective approaches are Content-based Filtering and Collaborative Filtering, each serving a specific recommendation scenario. The Content-based Filtering approaches inspect rich contexts of the recommended items, while the Collaborative Filtering approaches predict the interests of long-tail users by collaboratively learning from interests of related users. We have observed empirically that, for the problem of news topic displaying, both the rich context of news topics and the long-tail users exist. Therefore, in this paper, we propose a Content-based Collaborative Filtering approach (CCF) to bring both Content-based Filtering and Collaborative Filtering approaches together. We found that combining the two is not an easy task, but the benefits of CCF are impressive. On one hand, CCF makes recommendations based on the rich contexts of the news. On the other hand, CCF collaboratively analyzes the scarce feedbacks from the long-tail users. We tailored this CCF approach for the news topic displaying on the Bing front page and demonstrated great gains in attracting users. In the experiments and analyses part of this paper, we discuss the performance gains and insights in news topic recommendation in Bing.
A Probabilistic Model for Bursty Topic Discovery in Microblogs
Authors: Yan Xiaohui , Guo Jiafeng , Lan Yanyan , Xu Jun , Cheng Xueqi
Published: June 2022

Bursty topics discovery in microblogs is important for people to grasp essential and valuable information. However, the task is challenging since microblog posts are particularly short and noisy. This work develops a novel probabilistic model, namely Bursty Biterm Topic Model (BBTM), to deal with the task. BBTM extends the Biterm Topic Model (BTM) by incorporating the burstiness of biterms as prior knowledge for bursty topic modeling, which enjoys the following merits: 1) It can well solve the data sparsity problem in topic modeling over short texts as the same as BTM; 2) It can automatical discover high quality bursty topics in microblogs in a principled and efficient way. Extensive experiments on a standard Twitter dataset show that our approach outperforms the state-of-the-art baselines significantly.
Approximating Model-Based ABox Revision in DL-Lite: Theory and Practice
Authors: Qi Guilin , Wang Zhe , Wang Kewen , Fu Xuefeng , Zhuang Zhiqiang
Published: June 2022

Model-based approaches provide a semantically well justified way to revise ontologies. However, in general, model-based revision operators are limited due to lack of efficient algorithms and inexpressibility of the revision results. In this paper, we make both theoretical and practical contribution to efficient computation of model-based revisions in DL-Lite. Specifically, we show that maximal approximations of two well-known model-based revisions for DL-Lite_R can be computed using a syntactic algorithm. However, such a coincidence of model-based and syntactic approaches does not hold when role functionality axioms are allowed. As a result, we identify conditions that guarantee such a coincidence for DL-Lite_FR. Our result shows that both model-based and syntactic revisions can co-exist seamlessly and the advantages of both approaches can be taken in one revision operator. Based on our theoretical results, we develop a graph-based algorithm for the revision operat
An Association Network for Computing Semantic Relatedness
Authors: Zhang Keyang , Zhu Kenny , Hwang Seung-won
Published: June 2022

To judge how much a pair of words (or texts) are semantically related is acognitive process. However, previous algorithms for computing semanticrelatedness are largely based on co-occurrences within textualwindows, and do not actively leverage cognitive human perceptions ofrelatedness. To bridge this perceptional gap, we propose to utilizefree association as signals to capture such human perceptions.However, free association, being manually evaluated,has limited lexical coverage and is inherently sparse. We propose to expand lexical coverage and overcome sparseness by constructing an association network of terms and concepts that combines signals from free association norms and five types of co-occurrences extracted from therich structures of Wikipedia. Our evaluation results validate thatsimple algorithms on this network give competitive results incomputing semantic relatedness between words and between shorttexts.
Spontaneous Retrieval from Long-Term Memory for a Cognitive Architecture
Authors: Li Justin , Laird John
Published: June 2022

This paper presents the first functional evaluation of spontaneous, uncued retrieval from long-term memory in a cognitive architecture. The key insight is that current deliberate cued retrieval mechanisms require the agent to have knowledge of when and what to retrieve --- knowledge that may be missing or incorrect. Spontaneous uncued retrieval eliminates these requirements through automatic retrievals that use the agent's problem solving context as a heuristic for relevance, thus supplementing deliberate cued retrieval. Using constraints derived from this insight, we sketch the space of spontaneous retrieval mechanisms and describe an implementation of spontaneous retrieval in Soar together with an agent that takes advantage of that mechanism. Empirical evidence is provided in the Missing Link word-puzzle domain, where agents using spontaneous retrieval out-perform agents without that capability, leading us to conclude that spontaneous retrieval can be a useful mechanism and is worth further exploration.
Data Analysis and Optimization for (Citi)Bike Sharing
Authors: O'Mahony Eoin , Shmoys David
Published: June 2022

Bike-sharing systems are becoming increasingly prevalent in urban environments. They provide a low-cost, environmentally-friendly transportation alternative for cities. The management of these systems gives rise to many optimization problems. Chief among these problems is the issue of bicycle rebalancing. Users imbalance the system by creating demand in an asymmetric pattern. This necessitates action to put the system back in balance with the requisite levels of bicycles at each station to facilitate future use. In this paper, we tackle the problem of maintaing system balance during peak rush-hour usageas well as rebalancing overnight to prepare the systemfor rush-hour usage. We provide novel problem formulationsthat have been motivated by both a close collaborationwith the New York City bike share (Citibike) and a careful analysisof system usage data. We analyze system data to discover the best placement of bikes tofacilitate usage. We solve routing problems forovernight shifts as well as clustering problems for handlingmid rush-hour usage. The tools developed from this research are currently in daily use at NYC Bike Share LLC, operators of Citibike.
Optimal Multi-Agent Pathfinding Algorithms
Authors: Sharon Guni
Published: June 2022

The multi-agent path finding (MAPF) problem is a generalization of the single-agent path finding problem for k > 1 agents. It consists of a graph and a number of agents. Foreach agent, a unique start state and a unique goal state are given, the task is to find paths for all agents from their start states to their goal states, under the constraint that agents cannot collide during their movements. In many cases there is an additional goal of minimizing a cumulative cost function such as the sum of the time steps required for every agent to reach its goal. The goal of my research is providing new methods to solve MAPF optimally and provide theoretical understandings that will help choose the best solver given a problem instance.
Risk-Aware Scheduling throughout Planning and Execution
Authors: Wang Andrew
Published: June 2022

The paper is an extended abstract for the doctoral consortium.
Realistic Assumptions for Attacks on Elections
Authors: Fitzsimmons Zack
Published: June 2022

We must properly model attacks and the preferences of the electorate for the computational study of attacks on elections to give us insight into the hardness of attacks in practice. Theoretical and empirical analysis are equally important methods to understand election attacks. I discuss my recent work on domain restrictions on partial preferences and on new election attacks. I propose further study into modeling realistic election attacks and the advancement of the current state of empirical analysis of their hardness by using more advanced statistical techniques.
Explaining Answer Set Programming in Argumentative Terms
Authors: Schulz Claudia
Published: June 2022

Argumentation Theory and Answer Set Programming (ASP) are two prominent theories in the field of knowledge representation and non-monotonic reasoning,where Argumentation Theory stands for a variety of approaches following similar ideas.The main difference between Argumentation Theory and ASP is that the former focusses on representing knowledge and reasoning about it in a way that resembles human reasoning, neglecting the efficiency of the reasoning procedure,whereas the latter is concerned with the efficient computation of solutions to a reasoning problem, resulting in a less human-understandable process. In recent years, ASP has been frequently applied for the computation of reasoning problems represented in argumentation-theoretical-terms and has been found an efficient method for determining solutions to problems in Argumentation Theory. My research is concerned with the opposite direction, i.e. with applying Argumentation Theory to ASP in order to explain the solutions to an ASP reasoning problem in a more human-understandable way.Developing such an explanation method also involves to investigate both the exact relationship between different approaches in Argumentation Theory in order to find the most suitable one for explanations and their connection with ASP, in particular with respect to their semantics.
Non-Classical Planning for Robotic Applications
Authors: Kiesel Scott
Published: June 2022

For my dissertation I am focusing on non-classical planning for robotic applications. Much classical planning research relies on assumptions that do not hold in real world robotics applications. In many cases the entire world state is not known in advance and the events that occur in the future can not be known with certainty. Robots operating in the real world also need to be responsive and react to dynamic obstacles and events.
World WordNet Database Structure: An Efficient Schema for Storing Information of WordNets of the World
Authors: Redkar Hanumant , Bhingardive Sudha , Kanojia Diptesh , Bhattacharyya Pushpak
Published: June 2022

WordNet is an online lexical resource which expresses unique concepts in a language. English WordNet is the first WordNet which was developed at Princeton University. Over a period of time, many language WordNets were developed by various organizations all over the world. It has always been a challenge to store the WordNet data. Some WordNets are stored using file system and some WordNets are stored using different database models. In this paper, we present the World WordNet Database Structure which can be used to efficiently store the WordNet information of all languages of the World. This design can be adapted by most language WordNets to store information such as synset data, semantic and lexical relations, ontology details, language specific features, linguistic information, etc. An attempt is made to develop Application Programming Interfaces to manipulate the data from these databases. This database structure can help in various Natural Language Processing applications like Multilingual Information Retrieval, Word Sense Disambiguation, Machine Translation, etc.
Towards Cognitive Automation of Data Science
Authors: Biem Alain , Butrico Maria , Feblowitz Mark , Klinger Tim , Malitsky Yuri , Ng Kenney , Perer Adam , Reddy Chandra , Riabov Anton , Samulowitz Horst , Sow Daby , Tesauro Gerald , Turaga Deepak
Published: June 2022

A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e.g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, complex, and sometimes artful process requiring substantial time and effort, especially because of the combinatorial explosion in choices of algorithms (and platforms), their parameters, and their compositions. Tools that can help automate steps in this process have the potential to accelerate the time-to-delivery of useful results, expand the reach of data science to non-experts, and offer a more systematic exploration of the available options. This work presents a step towards this goal.
Circumventing Robots' Failures by Embracing Their Faults: A Practical Approach to Planning for Autonomous Construction
Authors: Witwicki Stefan , Mondada Francesco
Published: June 2022

This paper overviews our application of state-of-the-art automated planning algorithms to real mobile robots performing an autonomous construction task, a domain in which robots are prone to faults.  We describe how embracing these faults leads to better representations and smarter planning, allowing robots with limited precision to avoid catastrophic failures and succeed in intricate constructions.
Congestion Games with Distance-Based Strict Uncertainty
Authors: Meir Reshef , Parkes David
Published: June 2022

We put forward a new model of congestion games where agents have uncertainty over the routes used by other agents. We take a non-probabilistic approach, assuming that each agent knows that the number of agents using an edge is within a certain range. Given this uncertainty, we model agents who either minimize their worst-case cost (WCC) or their worst-case regret (WCR), and study implications on equilibrium existence, convergence through adaptive play, and efficiency. Under the WCC behavior the game reduces to a modified congestion game, and welfare improves when agents have moderate uncertainty. Under WCR behavior the game is not, in general, a congestion game, but we show convergence and efficiency bounds for a simple class of games.
Facility Location with Double-Peaked Preferences
Authors: Filos-Ratsikas Aris , Li Minming , Zhang Jie , Zhang Qiang
Published: June 2022

We study the problem of locating a single facility on a real line based on the reports of self-interested agents, when agents have double-peaked preferences, with the peaks being on opposite sides of their locations.We observe that double-peaked preferences capture real-life scenarios and thus complement the well-studied notion of single-peaked preferences. We mainly focus on the case where peaks are equidistant from the agents’ locations and discuss how our results extend to more general settings. We show that most of the results for single-peaked preferences do not directly apply to this setting; this makes the problem essentially more challenging. As our main contribution, we present a simple truthful-in-expectation mechanism that achieves an approximation ratio of 1+b/c for both the social and the maximum cost, where b is the distance of the agent from the peak and c is the minimum cost of an agent. For the latter case, we provide a 3/2 lower bound on the approximation ratio of any truthful-in-expectation mechanism. We also study deterministic mechanisms under some natural conditions, proving lower bounds and approximation guarantees. We prove that among a large class of reasonable mechanisms, there is no deterministic mechanism that outpeforms our truthful-in-expectation mechanism.
A Stackelberg Game Approach for Incentivizing Participation in Online Educational Forums with Heterogeneous Student Population
Authors: Vallam Rohith , Bhatt Priyanka , Mandal Debmalya , Y. Narahari
Published: June 2022

Increased interest in web-based education has spurred the proliferation of online learning environments. However, these platforms suffer from high dropout rates due to lack of sustained motivation among the students taking the course. In an effort to address this problem, we propose an incentive-based, instructor-driven approach to orchestrate the interactions in online educational forums (OEFs). Our approach takes into account the heterogeneity in skills among the students as well as the limited budget available to the instructor. We first analytically model OEFs in a non-strategic setting using ideas from lumpable continuous time Markov chains and compute expected aggregate transient net-rewards for the instructor and the students. We next consider a strategic setting where we use the rewards computed above to set up a mixed-integer linear program which views an OEF as a single-leader-multiple-followers Stackelberg game and recommends an optimal plan to the instructor for maximizing student participation. Our experimental results reveal several interesting phenomena including a striking non-monotonicity in the level of participation of students vis-a-vis the instructor's arrival rate.
The Pricing War Continues: On Competitive Multi-Item Pricing
Authors: Lev Omer , Oren Joel , Boutilier Craig , Rosenschein Jeffrey
Published: June 2022

We study a game with \emph{strategic} vendors (the agents) who own multiple items and a single buyer with a submodular valuation function. The goal of the vendors is to maximize their revenue via pricing of the items, given that the buyer will buy the set of items that maximizes his net payoff.% (valuation minus the prices). We show this game may not always have a pure Nash equilibrium, in contrast to previous results for the special case where each vendor owns a single item. We do so by relating our game to an intermediate, discrete game in which the vendors only choose the available items, and their prices are set exogenously afterwards. We further make use of the intermediate game to provide tight bounds on the price of anarchy for the subset games that have pure Nash equilibria; we find that the optimal PoA reached in the previous special cases does not hold, but only a logarithmic one. Finally, we show that for a special case of submodular functions, efficient pure Nash equilibria always exist.
A Graphical Representation for Games in Partition Function Form
Authors: Skibski Oskar , Michalak Tomasz , Sakurai Yuko , Wooldridge Michael , Yokoo Makoto
Published: June 2022

We propose a novel representation for coalitional games with externalities, called Partition Decision Trees. This representation is based on rooted directed trees, where non-leaf nodes are labelled with agents' names, leaf nodes are labelled with payoff vectors, and edges indicate membership of agents in coalitions. We show that this representation is fully expressive, and for certain classes of games significantly more concise than an extensive representation. Most importantly, Partition Decision Trees are the first formalism in the literature under which most of the direct extensions of the Shapley value to games with externalities can be computed in polynomial time.
The Complexity of Recognizing Incomplete Single-Crossing Preferences
Authors: Elkind Edith , Faliszewski Piotr , Lackner Martin , Obraztsova Svetlana
Published: June 2022

We study the complexity of deciding if a given profile of incomplete votes (i.e., a profile of partial orders over a given set of alternatives) can be extended to a single-crossing profile of complete votes (total orders). This problem models settings where we have partial knowledge regarding voters' preferences and we would like to understand whether the given preference profile may be single-crossing. We show that this problem admits a polynomial-time algorithm when the order of votes is fixed and the input profile consists of top orders, but becomes NP-complete if we are allowed to permute the votes and the input profile consists of weak orders or independent-pairs orders. Also, we identify a number of practical special cases of both problems that admit polynomial-time algorithms.
Online Learning and Profit Maximization from Revealed Preferences
Authors: Amin Kareem , Cummings Rachel , Dworkin Lili , Kearns Michael , Roth Aaron
Published: June 2022

We consider the problem of learning from revealed preferences in an online setting. In our framework, each period a consumer buys an optimal bundle of goods from a merchant according to her (linear) utility function and current prices, subject to a budget constraint. The merchant observes only the purchased goods, and seeks to adapt prices to optimize his profits. We give an efficient algorithm for the merchant's problem that consists of a learning phase in which the consumer's utility function is (perhaps partially) inferred, followed by a price optimization step. We also give an alternative online learning algorithm for the setting where prices are set exogenously, but the merchant would still like to predict the bundle that will be bought by the consumer, for purposes of inventory or supply chain management. In contrast with most prior work on the revealed preferences problem, we demonstrate that by making stronger assumptions on the form of utility functions, efficient algorithms for both learning and profit maximization are possible, even in adaptive, online settings.
Efficient Task Sub-Delegation for Crowdsourcing
Authors: Yu Han , Miao Chunyan , Shen Zhiqi , Leung Cyril , Chen Yiqiang , Yang Qiang
Published: June 2022

Reputation-based approaches allow a crowdsourcing system to identify reliable workers to whom tasks can be delegated. In crowdsourcing systems that can be modeled as multi-agent trust networks consist of resource constrained trustee agents (i.e., workers), workers may need to further sub-delegate tasks to others if they determine that they cannot complete all pending tasks before the stipulated deadlines. Existing reputation-based decision-making models cannot help workers decide when and to whom to sub-delegate tasks. In this paper, we proposed a reputation aware task sub-delegation (RTS) approach to bridge this gap. By jointly considering a worker's reputation, workload, the price of its effort and its trust relationships with others, RTS can be implemented as an intelligent agent to help workers make sub-delegation decisions in a distributed manner. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of a crowd, and provides provable performance guarantees. Experimental comparisons with state-of-the-art approaches based on the Epinions trust network demonstrate significant advantages of RTS under high workload conditions.
Collaboration in Social Problem-Solving: When Diversity Trumps Network Efficiency
Authors: Noble Diego , Prates Marcelo , Bossle Daniel , Lamb Luís
Published: June 2022

Recent studies have suggested that current agent-based models are not sufficiently sophisticated to reproduce results achieved by human collaborative learning and reasoning. Such studies suggest that humans are diverse and dynamic when solving problems socially. However, despite their relevance to problem-solving, these two behavioral features have not yet been fully investigated. In this paper we analyse a recent social problem-solving model and attempt to address its shortcomings. Specifically, we investigate the effects of separating exploitation from exploration in agent behaviors and explore the concept of diversity in such models. We found out that diverse populations outperform homogeneous ones in both efficient and inefficient networks. Finally, we show that agent diversity is more relevant than the strategic behavioral dynamics. This work contributes towards understanding the role of diverse and dynamic behaviors in social problem-solving as well as the advancement of state-of-art social problem-solving models.
A Theoretical Analysis of Optimization by Gaussian Continuation
Authors: Mobahi Hossein , Fisher III John
Published: June 2022

Optimization via continuation method is a widely used approach for solving nonconvex minimization problems. While this method generally does not provide a global minimum, empirically it often achieves a superior local minimum compared to alternative approaches such as gradient descent. However, theoretical analysis of this method is largely unavailable. Here, we provide a theoretical analysis that provides a bound on the endpoint solution of the continuation method. The derived bound depends on a problem specific characteristic that we refer to as optimization complexity. We show that this characteristic can be analytically computed when the objective function is expressed in some suitable basis functions. Our analysis combines elements of scale-space theory, regularization and differential equations.
Solving Distributed Constraint Optimization Problems Using Logic Programming
Authors: Le Tiep , Son Tran , Pontelli Enrico , Yeoh William
Published: June 2022

This paper explores the use of answer set programming (ASP) in solving distributed constraint optimization problems (DCOPs). It makes the following contributions: (i)~It shows how one can formulate DCOPs as logic programs; (ii)~It introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (iii)~It experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative-programming counterpart) as well as solve some problems that DPOP fails to solve due to memory limitations; and (iv)~It demonstrates the applicability of ASP in the wide array of multi-agent problems currently modeled as DCOPs.
Resilient Upgrade of Electrical Distribution Grids
Authors: Yamangil Emre , Bent Russell , Backhaus Scott
Published: June 2022

Modern society is critically dependent on the services provided by engineered infrastructure networks. When natural disasters (e.g. Hurricane Sandy) occur, the ability of these networks to provide service is often degraded because of physical damage to network components. One of the most critical of these networks is the electrical distribution grid, with medium voltage circuits often suffering the most severe damage. However, well-placed upgrades to these distribution grids can greatly improve post-event network performance. We formulate an optimal electrical distribution grid design problem as a two-stage, stochastic mixed-integer program with damage scenarios from natural disasters modeled as a set of stochastic events. We develop and investigate the tractability of an exact and several heuristic algorithms based on decompositions that are hybrids of techniques developed by the AI and operations research communities. We provide computational evidence that these algorithms have significant benefits when compared with commercial, mixed-integer programming software.
Pattern-Based Variant-Best-Neighbors Respiratory Motion Prediction Using Orthogonal Polynomials Approximation
Authors: Kam KinMing , Wang Shouyi , Bowen Stephen , Chaovalitwongse Wanpracha
Published: June 2022

Motion-adaptive radiotherapy techniques are promising to deliver truly ablative radiation doses to tumors with minimal normal tissue exposure by accounting for real-time tumor movement. However, a major challenge of successful applications of these techniques is the real-time prediction of breathing-induced tumor motion to accommodate system delivery latencies. Predicting respiratory motion in real-time is challenging. The current respiratory motion prediction approaches are still not satisfactory in terms of accuracy and interpretability due to the complexity of breathing patterns and the high inter-individual variability across patients. In this paper, we propose a novel respiratory motion prediction framework which integrates four key components: a personalized monitoring window generator, an orthogonal polynomial approximation-based pattern library builder, a variant best neighbor pattern searcher, and a statistical prediction decision maker. The four functional components work together into a real-time prediction system and is capable of performing personalized tumor position prediction during radiotherapy. Based on a study of respiratory motion of 27 patients with lung cancer, the proposed prediction approach generated consistently better prediction performances than the current respiratory motion prediction approaches, particularly for long prediction horizons.
An Abstract View on Modularity in Knowledge Representation
Authors: Lierler Yuliya , Truszczynski Miroslaw
Published: June 2022

Modularity is an essential aspect of knowledge representation theory and practice. It has received substantial attention. We introduce model-based modular systems, an abstract framework for modular knowledge representation formalisms, similar in scope to multi-context systems but employing a simpler information-flow mechanism. We establish the precise relationship between the two frameworks, showing that they can simulate each other. We demonstrate that recently introduced modular knowledge representation formalisms integrating logic programming with satisfiability and, more generally, with constraint satisfaction can be cast as modular systems in our sense. These results show that our formalism offers a simple unifying framework for studies of modularity in knowledge representation.
Interactive Query-Based Debugging of ASP Programs
Authors: Shchekotykhin Kostyantyn
Published: June 2022

Broad application of answer set programming (ASP) for declarative problem solving requires the development of tools supporting the coding process. Program debugging is one of the crucial activities within this process. Modern ASP debugging approaches allow efficient computation of possible explanations of a fault. However, even for a small program a debugger might return a large number of possible explanations and selection of the correct one must be done manually. In this paper we present an interactive query-based ASP debugging method which extends previous approaches and finds the preferred explanation by means of observations. The system automatically generates a sequence of queries to a programmer asking whether a set of ground atoms must be true in all (cautiously) or some (bravely) answer sets of the program. Since some queries can be more informative than the others, we discuss query selection strategies which - given user's preferences for an explanation - can find the most informative query reducing the overall number of queries required for the identification of a preferred explanation.
Learning Partial Lexicographic Preference Trees over Combinatorial Domains
Authors: Liu Xudong , Truszczynski Miroslaw
Published: June 2022

We introduce partial lexicographic preference trees (PLPtrees) as a formalism for compact representations of preferences over combinatorial domains. Our main results concern the problem of passive learning of PLP-trees. Specifically, forseveral classes of PLP-trees, we study how to learn (i) a PLP-tree consistent with a dataset of examples, possibly subject to requirements on the size of the tree, and (ii) a PLP-tree correctly ordering as many of the examples as possible in case the dataset of examples is inconsistent. We establish complexity of these problems and, in all cases where the problem is in the class P, propose polynomial time algorithms.