bluuebunny/crossref_metadata_2025 huggingface dataset:
Bilateral Multi-Perspective Matching for Natural Language Sentences
Authors: Wang Zhiguo , Hamza Wael , Florian Radu
Published: July 2017

Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model. Given two sentences P and Q, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions P against Q and P against Q. In each matching direction, each time step of one sentence is matched against all time-steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fix-length matching vector. Finally, based on the matching vector, a decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks.
A Neural Model for Joint Event Detection and Summarization
Authors: Wang Zhongqing , Zhang Yue
Published: July 2017

Twitter new event detection aims to identify first stories in a tweet stream. Typical approaches consider two sub tasks. First, it is necessary to filter out mundane or irrelevant tweets. Second, tweets are grouped automatically into event clusters. Traditionally, these two sub tasks are processed separately, and integrated under a pipeline setting, despite that there is inter-dependence between the two tasks. In addition, one further related task is summarization, which is to extract a succinct summary for representing a large group of tweets. Summarization is related to detection, under the new event setting in that salient information is universal between event representing tweets and informative event summaries. In this paper, we build a joint model to filter, cluster, and summarize the tweets for new events. In particular, deep representation learning is used to vectorize tweets, which serves as basis that connects tasks. A neural stacking model is used for integrating a pipeline of different sub tasks, and for better sharing between the predecessor and successors. Experiments show that our proposed neural joint model is more effective compared to its pipeline baseline.
Additive Merge-and-Shrink Heuristics for Diverse Action Costs
Authors: Fan Gaojian , Müller Martin , Holte Robert
Published: July 2017

In many planning applications, actions can have highly diverse costs. Recent studies focus on the effects of diverse action costs on search algorithms, but not on their effects on domain-independent heuristics. In this paper, we demonstrate there are negative impacts of action cost diversity on merge-and-shrink (M&S), a successful abstraction method for producing high-quality heuristics for planning problems. We propose a new cost partitioning method for M&S to address the negative effects of diverse action costs. We investigate non-unit cost IPC domains, especially those for which diverse action costs have severe negative effects on the quality of the M&S heuristic. Our experiments demonstrate that in these domains, an additive set of M&S heuristics using the new cost partitioning method produces much more informative and effective heuristics than creating a single M&S heuristic which directly encodes diverse costs.
Diverse Weighted Bipartite b-Matching
Authors: Ahmed Faez , Dickerson John P. , Fuge Mark
Published: July 2017

Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation. A practitioner's goal is typically to maximize a matching market's economic efficiency, possibly subject to some fairness requirements that promote equal access to resources. A natural balancing act exists between fairness and efficiency in matching markets, and has been the subject of much research.In this paper, we study a complementary goal---balancing diversity and efficiency---in a generalization of bipartite matching where agents on one side of the market can be matched to sets of agents on the other. Adapting a classical definition of the diversity of a set, we propose a quadratic programming-based approach to solving a submodular minimization problem that balances diversity and total weight of the solution. We also provide a scalable greedy algorithm with theoretical performance bounds. We then define the price of diversity, a measure of the efficiency loss due to enforcing diversity, and give a worst-case theoretical bound. Finally, we demonstrate the efficacy of our methods on three real-world datasets, and show that the price of diversity is not bad in practice. Our code is publicly accessible for further research.
On Creating Complementary Pattern Databases
Authors: Franco Santiago , Torralba Álvaro , Lelis Levi H. S. , Barley Mike
Published: July 2017

A pattern database (PDB) for a planning task is a heuristic function in the form of a lookup table that contains optimal solution costs of a simplified version of the task. In this paper we introduce a method that sequentially creates multiple PDBs which are later combined into a single heuristic function.

At a given iteration, our method uses estimates of the A* running time to create a PDB that complements the strengths of the PDBs created in previous iterations. We evaluate our algorithm using explicit and symbolic PDBs. Our results show that the heuristics produced by our approach are able to outperform existing schemes, and that our method is able to create PDBs that complement the strengths of other existing heuristics such as a symbolic perimeter heuristic.
Intelligent Belief State Sampling for Conformant Planning
Authors: Grastien Alban , Scala Enrico
Published: July 2017

We propose a new method for conformant planning based on two ideas. First given a small sample of the initial belief state we reduce conformant planning for this sample to a classical planning problem, giving us a candidate solution. Second we exploit regression as a way to compactly represent necessary conditions for such a solution to be valid for the non-deterministic setting. If necessary, we use the resulting formula to extract a counter-example to populate our next sampling. Our experiments show that this approach is competitive on a class of problems that are hard for traditional planners, and also returns generally shorter plans. We are also able to demonstrate unsatisfiability of some problems.
Generating Context-Free Grammars using Classical Planning
Authors: Segovia-Aguas Javier , Jiménez Sergio , Jonsson Anders
Published: July 2017

This paper presents a novel approach for generating Context-Free Grammars (CFGs) from small sets of input strings (a single input string in some cases). Our approach is to compile this task into a classical planning problem whose solutions are sequences of actions that build and validate a CFG compliant with the input strings. In addition, we show that our compilation is suitable for implementing the two canonical tasks for CFGs, string production and string recognition.
An Improved Approximation Algorithm for the Subpath Planning Problem and Its Generalization
Authors: Sumita Hanna , Yonebayashi Yuma , Kakimura Naonori , Kawarabayashi Ken-ichi
Published: July 2017

This paper focuses on a generalization of the traveling salesman problem (TSP), called the subpath planning problem (SPP). Given 2n vertices and n independent edges on a metric space, we aim to find a shortest tour that contains all the edges. SPP is one of the fundamental problems in both artificial intelligence and robotics. Our main result is to design a 1.5-approximation algorithm that runs in polynomial time, improving the currently best approximation algorithm. The idea is direct use of techniques developed for TSP. In addition, we propose a generalization of SPP called the subgroup planning problem (SGPP). In this problem, we are given a set of disjoint groups of vertices, and we aim to find a shortest tour such that all the vertices in each group are traversed sequentially. We propose a 3-approximation algorithm for SGPP. We also conduct numerical experiments. Compared with previous algorithms, our algorithms improve the solution quality by more than 10% for large instances with more than 10,000 vertices.
Robust Advertisement Allocation
Authors: Tang Shaojie
Published: July 2017

With the rapid growth of e-commerce and World Wide Web, internet advertising revenue has surpassed broadcast revenue very recently. As online advertising has become a major source of revenue for online publishers, such as Google and Amazon, one problem facing them is to optimize the ads selection and allocation in order to maximize their revenue. Although there is a rich body of work that has been devoted to this field, uncertainty about models and parameter settings is largely ignored in existing algorithm design. To fill this gap, we are the first to formulate and study the \emph{Robust Ad Allocation} problem, by taking into account the uncertainty about parameter settings. We define a Robust Ad Allocation framework with a set of candidate parameter settings, typically derived from different users or topics. Our main aim is to develop robust ad allocation algorithms, which can provide satisfactory performance across a spectrum of parameter settings, compared to the (parameter-specific) optimum solutions. We study this problem progressively and propose a series of algorithms with bounded approximation ratio.
Temporal Planning for Compilation of Quantum Approximate Optimization Circuits
Authors: Venturelli Davide , Do Minh , Rieffel Eleanor , Frank Jeremy
Published: July 2017

We investigate the application of temporal planners to the problem of compiling quantum circuits to emerging quantum hardware. While our approach is general, we focus our initial experiments on Quantum Approximate Optimization Algorithm (QAOA) circuits that have few ordering constraints and thus allow highly parallel plans. We report on experiments using several temporal planners to compile circuits of various sizes to a realistic hardware architecture. This early empirical evaluation suggests that temporal planning is a viable approach to quantum circuit compilation.
Salient Object Detection with Semantic Priors
Authors: Nguyen Tam V. , Liu Luoqi
Published: July 2017

Salient object detection has increasingly become a popular topic in cognitive and computational sciences, including computer vision and artificial intelligence research. In this paper, we propose integrating semantic priors into the salient object detection process. Our algorithm consists of three basic steps. Firstly, the explicit saliency map is obtained based on the semantic segmentation refined by the explicit saliency priors learned from the data. Next, the implicit saliency map is computed based on a trained model which maps the implicit saliency priors embedded into regional features with the saliency values. Finally, the explicit semantic map and the implicit map are adaptively fused to form a pixel-accurate saliency map which uniformly covers the objects of interest. We further evaluate the proposed framework on two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental results demonstrate that our method outperforms other state-of-the-art methods.
Multi-Agent Planning with Baseline Regret Minimization
Authors: Wu Feng , Zilberstein Shlomo , Chen Xiaoping
Published: July 2017

We propose a novel baseline regret minimization algorithm for multi-agent planning problems modeled as finite-horizon decentralized POMDPs. It guarantees to produce a policy that is provably better than or at least equivalent to the baseline policy. We also propose an iterative belief generation algorithm to effectively and efficiently minimize the baseline regret, which only requires necessary iterations to converge to the policy with minimum baseline regret. Experimental results on common benchmark problems confirm its advantage comparing to the state-of-the-art approaches.
Score Aggregation via Spectral Method
Authors: Xiao Mingyu , Wang Yuqing
Published: July 2017

The score aggregation problem is to find an aggregate scoring over all candidates given individual scores provided by different agents. This is a fundamental problem with a broad range of applications in social choice and many other areas. The simple and commonly used method is to sum up all scores of each candidate, which is called the sum-up method. In this paper, we give good algebraic and geometric explanations for score aggregation, and develop a spectral method for it. If we view the original scores as `noise data', our method can find an `optimal' aggregate scoring by minimizing the `noise information'. We also suggest a signal-to-noise indicator to evaluate the validity of the aggregation or the consistency of the agents.
XOR-Sampling for Network Design with Correlated Stochastic Events
Authors: Wu Xiaojian , Xue Yexiang , Selman Bart , Gomes Carla P.
Published: July 2017

Many network optimization problems can be formulated as stochastic network design problems in which edges are present or absent stochastically. Furthermore, protective actions can guarantee that edges will remain present. We consider the problem of finding the optimal protection strategy under a budget limit in order to maximize some connectivity measurements of the network. Previous approaches rely on the assumption that edges are independent.

In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated. We use Markov Random Fields to model the correlation and define a new stochastic network design framework. We provide a novel algorithm based on Sample Average Approximation (SAA) coupled with a Gibbs or XOR sampler. The experimental results on real road network data show that the policies produced by SAA with the XOR sampler have higher quality and lower variance compared to SAA with Gibbs sampler.
Single-Image 3D Scene Parsing Using Geometric Commonsense
Authors: Yu Chengcheng , Liu Xiaobai , Zhu Song-Chun
Published: July 2017

This paper presents a unified grammatical framework capable of reconstructing a variety of scene types (e.g., urban, campus, county etc.) from a single input image. The key idea of our approach is to study a novel commonsense reasoning framework that mainly exploits two types of prior knowledges: (i) prior distributions over a single dimension of objects, e.g., that the length of a sedan is about 4.5 meters; (ii) pair-wise relationships between the dimensions of scene entities, e.g., that the length of a sedan is shorter than a bus. These unary or relative geometric knowledge, once extracted, are fairly stable across different types of natural scenes, and are informative for enhancing the understanding of various scenes in both 2D images and 3D world. Methodologically, we propose to construct a hierarchical graph representation as a unified representation of the input image and related geometric knowledge. We formulate these objectives with a unified probabilistic formula and develop a data-driven Monte Carlo method to infer the optimal solution with both bottom-to-up and top-down computations. Results with comparisons on public datasets showed that our method clearly outperforms the alternative methods.
Achieving Coordination in Multi-Agent Systems by Stable Local Conventions under Community Networks
Authors: Hu Shuyue , Leung Ho-fung
Published: July 2017

Recently, the study of social conventions has attracted much attention in the literature. We notice that a type of interesting phenomena, local convention phenomena, may also exist in certain multi-agent systems. When agents are partitioned into compact communities, different local conventions emerge in different communities. In this paper, we provide a definition for local conventions, and propose two metrics measuring their strength and diversity. In our experimental study, we show that agents can achieve coordination via establishing diverse stable local conventions, which indicates a practical way to solve coordination problems other than the traditional global convention emergence. Moreover, we find that with smaller community sizes, denser connections and fewer available actions, diverse local conventions emerge in shorter time.
Concerning Referring Expressions in Query Answers
Authors: Borgida Alexander , Toman David , Weddell Grant
Published: July 2017

A referring expression in linguistics is a noun phrase that identifies individuals to listeners. In the context of a query over a first order knowledge base, referring expressions to answers are usually constant symbols. This paper motivates and initiates the exploration of allowing more general formulas, called singular referring expressions, to replace constants in this role. Referring expression types play a novel and significant role in analyzing the properties of candidate expressions.
Open-World Probabilistic Databases: An Abridged Report
Authors: Ceylan Ismail Ilkan , Darwiche Adnan , Van den Broeck Guy
Published: July 2017

Large-scale probabilistic knowledge bases are becoming increasingly important in academia and industry alike. They are constantly extended with new data, powered by modern information extraction tools that associate probabilities with database tuples. In this paper, we revisit the semantics underlying such systems. In particular, the closed-world assumption of probabilistic databases, that facts not in the database have probability zero, clearly conflicts with their everyday use. To address this discrepancy, we propose an open-world probabilistic database semantics, which relaxes the probabilities of open facts to default intervals. For this open-world setting, we lift the existing data complexity dichotomy of probabilistic databases, and propose an efficient evaluation algorithm for unions of conjunctive queries. We also show that query evaluation can become harder for non-monotone queries.
User-Based Opinion-based Recommendation
Authors: Dong Ruihai , Smyth Barry
Published: July 2017

User-generated reviews are a plentiful source of user opinions and interests and can play an important role in a range of artificial intelligence contexts, particularly when it comes to recommender systems. In this paper, we describe how natural language processing and opinion mining techniques can be used to automatically mine useful recommendation knowledge from user generated reviews and how this information can be used by recommender systems in a number of classical settings.
An Admissible HTN Planning Heuristic
Authors: Bercher Pascal , Behnke Gregor , Höller Daniel , Biundo Susanne
Published: July 2017

Hierarchical task network (HTN) planning is well-known for being an efficient planning approach. This is mainly due to the success of the HTN planning system SHOP2. However, its performance depends on hand-designed search control knowledge. At the time being, there are only very few domain-independent heuristics, which are designed for differing hierarchical planning formalisms. Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically. It bases upon the so-called task decomposition graph (TDG), a data structure reflecting reachable parts of the task hierarchy. We show (both in theory and empirically) that rebuilding it during planning can improve heuristic accuracy thereby decreasing the explored search space. The evaluation further studies the heuristic both in terms of plan quality and coverage.
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study
Authors: Gupta Suyog , Zhang Wei , Wang Fei
Published: July 2017

Deep learning with a large number of parame-ters requires distributed training, where model accuracy and runtime are two important factors to be considered. However, there has been no systematic study of the tradeoff between these two factors during the model training process. This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study the impact of synchronization protocol, stale gradient updates, minibatch size, learning rates, and number of learners on runtime performance and model accuracy. We introduce a new learningrate modulation strategy to counter the effect of stale gradients and propose a new synchronization protocol that can effectively bound the staleness in gradients, improve runtime performance and achieve good model accuracy. Our empirical investigation reveals a principled approach for distributed training of neural networks: the mini-batch size per learner should be reduced as more learners are added to the system to preserve the model accuracy. We validate this approach using commonly-used image classification benchmarks: CIFAR10 and ImageNet.
Competence Guided Model for Casebase Maintenance
Authors: Mathew Ditty , Chakraborti Sutanu
Published: July 2017

A competence guided casebase maintenance algorithm retains a case in the casebase if it is useful to solve many problems and ensures that the casebase is highly competent. In this paper, we address the compositional adaptation process (of which single case adaptation is a special case) during casebase maintenance by proposing a case competence model for which we propose a measure called retention score to estimate the retention quality of a case. We also propose a revised algorithm based on the retention score to estimate the competent subset of a casebase. We used synthetic datasets to test the effectiveness of the competent subset obtained from the proposed model. We also applied this model in a tutoring application and analyzed the competent subset of concepts in tutoring resources. Empirical results show that the proposed model is effective and overcomes the limitation of footprint-based competence model in compositional adaptation applications.
Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report
Authors: Peterson Joshua C. , Abbott Joshua T. , Griffiths Thomas L.
Published: July 2017

Deep neural networks have become increasingly successful at solving classic perception problems (e.g., recognizing objects), often reaching or surpassing human-level accuracy. In this abridged report of Peterson et al. [2016], we examine the relationship between the image representations learned by these networks and those of humans. We find that deep features learned in service of object classification account for a significant amount of the variance in human similarity judgments for a set of animal images. However, these features do not appear to capture some key qualitative aspects of human representations. To close this gap, we present a method for adapting deep features to align with human similarity judgments, resulting in image representations that can potentially be used to extend the scope of psychological experiments and inform human-centric AI.
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity (Extended Abstract)
Authors: Cussens James , Järvisalo Matti , Korhonen Janne H. , Bartlett Mark
Published: July 2017

Developing accurate algorithms for learning structures of probabilistic graphical models is an important problem within modern AI research. Here we focus on score-based structure learning for Bayesian networks as arguably the most central class of graphical models. A successful generic approach to optimal Bayesian network structure learning (BNSL), based on integer programming (IP), is implemented in the Gobnilp system. Despite the recent algorithmic advances, current understanding of foundational aspects underlying the IP based approach to BNSL is still somewhat lacking. In this paper, we provide theoretical contributions towards understanding fundamental aspects of cutting planes and the related separation problem in this context, ranging from NP-hardness results to analysis of polytopes and the related facets in connection to BNSL.
AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)
Authors: Lindauer Marius , Hutter Frank , H. Hoos Holger , Schaub Torsten
Published: July 2017

Algorithm selection (AS) techniques -- which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently -- have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. In this extended abstract of our 2015 JAIR article of the same title, we summarize AutoFolio, which uses an algorithm configuration procedure to automatically select an AS approach and optimize its parameters for a given AS scenario. AutoFolio allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods and to automatically construct high-performance algorithm selectors. We demonstrate that AutoFolio was able to produce new state-of-the-art algorithm selectors for 7 well-studied AS scenarios and matches state-of-the-art performance statistically on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieved average speedup factors between 1.3 and 15.4.
CCEHC: An Efficient Local Search Algorithm for Weighted Partial Maximum Satisfiability (Extended Abstract)
Authors: Luo Chuan , Cai Shaowei , Su Kaile , Huang Wenxuan
Published: July 2017

Weighted partial maximum satisfiability (WPMS) is a significant generalization of maximum satisfiability (MAX-SAT), with many important applications. Recently, breakthroughs have been made on stochastic local search (SLS) for weighted MAX-SAT and (unweighted) partial MAX-SAT (PMS). However, the performance of SLS for WPMS lags far behind. In this work, we present a new SLS algorithm named CCEHC for WPMS. CCEHC is mainly based on a heuristic emphasizing hard clauses, which has three components: a variable selection mechanism focusing on configuration checking based only on hard clauses, a weighting scheme for hard clauses, and a biased random walk component. Experiments show that CCEHC significantly outperforms its state-of-the-art SLS competitors. Experiments comparing CCEHC with a state-of-the-art complete solver indicate the effectiveness of CCEHC on a number of application WPMS instances.
Online Bridged Pruning for Real-Time Search with Arbitrary Lookaheads
Authors: Hernandez Carlos , Botea Adi , Baier Jorge A. , Bulitko Vadim
Published: July 2017

Real-time search algorithms are relevant to time-sensitive decision-making domains such as video games and robotics. In such settings, the agent is required to decide on each action under a constant time bound, regardless of the search space size. Despite recent progress, poor-quality solutions can be produced mainly due to state re-visitation. Different techniques have been developed to reduce such a re-visitation with state pruning showing promise. In this paper, we propose a novel pruning approach applicable to the wide class of real-time search algorithms. Given a local search space of arbitrary size, our technique aggressively prunes away all states in its interior, possibly adding new edges to maintain the connectivity of the search space frontier. An experimental evaluation shows that our pruning often improves the performance of a base real-time search algorithm by over an order of magnitude.

This allows our implemented system to outperform state-of-the-art real-time search algorithms used in the evaluation.
News Across Languages - Cross-Lingual Document Similarity and Event Tracking (Extended Abstract)
Authors: Rupnik Jan , Muhič Andrej , Leban Gregor , Fortuna Blaž , Grobelnik Marko
Published: July 2017

In today's world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data.Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event.
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds
Authors: Belle Vaishak
Published: July 2017

Logical AI is concerned with formal languages to represent and reason with qualitative specifications; statistical AI is concerned with learning quantitative specifications from data. To combine the strengths of these two camps, there has been exciting recent progress on unifying logic and probability. We review the many guises for this union, while emphasizing the need for a formal language to represent a system's knowledge. Formal languages allow their internal properties to be robustly scrutinized, can be augmented by adding new knowledge, and are amenable to abstractions, all of which are vital to the design of intelligent systems that are explainable and interpretable.
Multimodal News Article Analysis
Authors: Ramisa Arnau
Published: July 2017

The intersection of Computer Vision and Natural Language Processing has been a hot topic of research in recent years, with results that were unthinkable only a few years ago. In view of this progress, we want to highlight online news articles as a potential next step for this area of research. The rich interrelations of text, tags, images or videos, as well as a vast corpus of general knowledge are an exciting benchmark for high-capacity models such as the deep neural networks. In this paper we present a series of tasks and baseline approaches to leverage corpus such as the BreakingNews dataset.
Reinforcement mechanism design
Authors: Tang Pingzhong
Published: July 2017

We put forward a modeling and algorithmic framework to design and optimize mechanisms in dynamic industrial environments where a designer can make use of the data generated in the process to automatically improve future design. Our solution, coined reinforcement mechanism design, is rooted in game theory but incorporates recent AI techniques to get rid of nonrealistic modeling assumptions and to make automated optimization feasible. We instantiate our framework on the key application scenarios of Baidu and Taobao, two of the largest mobile app companies in China. For the Taobao case, our framework automatically designs mechanisms that allocate buyer impressions for the e-commerce website; for the Baidu case, our framework automatically designs dynamic reserve pricing schemes of advertisement auctions of the search engine. Experiments show that our solutions outperform the state-of-the-art alternatives and those currently deployed, under both scenarios.
Probabilistic Inference in Hybrid Domains
Authors: Morettin Paolo
Published: July 2017

This extended abstract presents my PhD research project on learning and reasoning in Hybrid Domains. In particular, it focuses on my current work on exact probabilistic inference in these domains, as well as presenting other research directions that are going to be explored.
Modeling Bias Reduction Strategies in a Biased Agent
Authors: Scheuerman Jaelle , Acklin Dina
Published: July 2017

Costly mistakes can occur when decision makers rely on intuition or learned biases to make decisions. To better understand the cognitive processes that lead to bias and develop strategies to combat it, we developed an intelligent agent using the cognitive architecture, ACT-R 7.0. The agent simulates a human participating in a decision making task designed to assess the effectiveness of bias reduction strategies. The agent's performance is compared to that of human participants completing a similar task. Similar results support the underlying cognitive theories and reveal limitations of reducing bias in human decision making. This should provide insights for designing intelligent agents that can reason about bias while supporting decision makers.
Refinement of Intentions
Authors: Xiao Zhanhao
Published: July 2017

The aim of this paper is to provide a logical analysis of intention refinement process which plays a fundamental role in the belief-desire-intention (BDI) theory. We briefly show the existing results: a logical framework for intention refinement and the extension of hierarchical task network (HTN) planning to capture high-level intentions. We also present two ongoing directions: extending our logical framework with hierarchical decomposition and revision of intentions based on instrumentality.
The DNA Word Design Problem: A New Constraint Model and New Results
Authors: Codish Michael , Frank Michael , Lagoon Vitaly
Published: July 2017

A fundamental problem in coding theory concerns the computation of

the maximum cardinality of a set S of length

n code words over an alphabet of size q, such that every pair of

code words has Hamming distance at least d, and the set of

additional constraints U on S is satisfied. This problem has

application in several areas, one of which is the design of DNA

codes where q=4 and the alphabet is {A,C,G,T}.

We describe a new constraint model for this problem and demonstrate

that it improves on previous solutions (computes better lower

bounds) for various instances of the problem. Our approach is based

on a clustering of DNA words into small sets of words. Solutions

are then obtained as the union of such clusters. Our approach is

SAT based: we specify constraints on clusters of DNA words and solve

these using a Boolean satisfiability solver.
Learning-Based Abstractions for Nonlinear Constraint Solving
Authors: Dathathri Sumanth , Arechiga Nikos , Gao Sicun , Murray Richard M.
Published: July 2017

We propose a new abstraction refinement procedure based on machine learning to improve the performance of nonlinear constraint solving algorithms on large-scale problems. The proposed approach decomposes the original set of constraints into smaller subsets, and uses learning algorithms to propose sequences of abstractions that take the form of conjunctions of classifiers. The core procedure is a refinement loop that keeps improving the learned results based on counterexamples that are obtained from partial constraints that are easy to solve. Experiments show that the proposed techniques significantly improve the performance of state-of-the-art constraint solvers on many challenging benchmarks. The mechanism is capable of producing intermediate symbolic abstractions that are also important for many applications and for understanding the internal structures of hard constraint solving problems.
Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation
Authors: Fan Yi , Li Nan , Li Chengqian , Ma Zongjie , Latecki Longin Jan , Su Kaile
Published: July 2017

The Maximum Vertex Weight Clique (MVWC) problem is NP-hard and also important in real-world applications. In this paper we propose to use the restart and the random walk strategies to improve local search for MVWC. If a solution is revisited in some particular situation, the search will restart. In addition, when the local search has no other options except dropping vertices, it will use random walk. Experimental results show that our solver outperforms state-of-the-art solvers in DIMACS and finds a new best-known solution. Also it is the unique solver which is comparable with state-of-the-art methods on both BHOSLIB and large crafted graphs. Furthermore we evaluated our solver in clustering aggregation. Experimental results on a number of real data sets demonstrate that our solver outperforms the state-of-the-art for solving the derived MVWC problem and helps improve the final clustering results.
Finding Robust Solutions to Stable Marriage
Authors: Genc Begum , Siala Mohamed , O'Sullivan Barry , Simonin Gilles
Published: July 2017

We study the notion of robustness in stable matching problems. We first define robustness by introducing (a,b)-supermatches. An (a,b)-supermatch is a stable matching in which if a pairs break up it is possible to find another stable matching by changing the partners of those a pairs and at most b other pairs. In this context, we define the most robust stable matching as a (1,b)-supermatch where b is minimum. We show that checking whether a given stable matching is a (1,b)-supermatch can be done in polynomial time. Next, we use this procedure to design a constraint programming model, a local search approach, and a genetic algorithm to find the most robust stable matching. Our empirical evaluation on large instances show that local search outperforms the other approaches.
A Core-Guided Approach to Learning Optimal Causal Graphs
Authors: Hyttinen Antti , Saikko Paul , Järvisalo Matti
Published: July 2017

Discovery of causal relations is an important part of data analysis. Recent exact Boolean optimization approaches enable tackling very general search spaces of causal graphs with feedback cycles and latent confounders, simultaneously obtaining high accuracy by optimally combining conflicting independence information in sample data. We propose several domain-specific techniques and integrate them into a core-guided maximum satisfiability solver, thereby speeding up current state of the art in exact search for causal graphs with cycles and latent confounders on simulated and real-world data.
An Improved Decision-DNNF Compiler
Authors: Lagniez Jean-Marie , Marquis Pierre
Published: July 2017

We present and evaluate a new compiler, called d4, targeting the Decision-DNNF language.

As the state-of-the-art compilers C2D and Dsharp targeting the same language,

d4 is a top-down tree-search algorithm exploring the space of propositional

interpretations. d4 is based on the same ingredients as those considered in C2D and Dsharp

(mainly, disjoint component analysis, conflict analysis and non-chronological backtracking, component caching).

d4 takes advantage of a dynamic decomposition approach based on hypergraph partitioning, used sparingly.

Some simplification rules are also used to minimize the time spent in the partitioning steps and to promote the

quality of the decompositions. Experiments show that the compilation times and the sizes of the

Decision-DNNF representations computed by d4 are in many cases significantly lower than the

ones obtained by C2D and Dsharp.
Automatic Synthesis of Smart Table Constraints by Abstraction of Table Constraints
Authors: Le Charlier Baudouin , Khong Minh Thanh , Lecoutre Christophe , Deville Yves
Published: July 2017

The smart table constraint represents a powerful modeling tool that has been recently introduced. This constraint allows the user to represent compactly a number of well-known (global) constraints and more generally any arbitrarily structured constraints, especially when disjunction is at stake. In many problems, some constraints are given under the basic and simple form of tables explicitly listing the allowed combinations of values. In this paper, we propose an algorithm to convert automatically any (ordinary) table into a compact smart table. Its theoretical time complexity is shown to be quadratic in the size of the input table. Experimental results demonstrate its compression efficiency on many constraint cases while showing its reasonable execution time. It is then shown that using filtering algorithms on the resulting smart table is more efficient than using state of the art filtering algorithms on the initial table.
A Partitioning Algorithm for Maximum Common Subgraph Problems
Authors: McCreesh Ciaran , Prosser Patrick , Trimble James
Published: July 2017

We introduce a new branch and bound algorithm for the maximum common subgraph and maximum common connected subgraph problems which is based around vertex labelling and partitioning. Our method in some ways resembles a traditional constraint programming approach, but uses a novel compact domain store and supporting inference algorithms which dramatically reduce the memory and computation requirements during search, and allow better dual viewpoint ordering heuristics to be calculated cheaply. Experiments show a speedup of more than an order of magnitude over the state of the art, and demonstrate that we can operate on much larger graphs without running out of memory.
PENGARUH LEVERAGE, LIKUIDITAS, UKURAN PERUSAHAAN, DAN KUALITAS AUDIT TERHADAP KUALITAS INFORMASI LAPORAN KEUANGAN (Studi empiris pada perusahaan Properti dan Real Estate yang terdaftar di Bursa Efek Indonesia)
Authors: Susanti Susanti
Published: July 2017

The purpose of this study was to examine issues relating to the measurement of the quality of empirical studies of financial statement information on the company property and real estate the period 2008-2011 . This study use the four independent variables , namely , leverage , liquidity , firm size , and audit quality and the use of the dependent variable as a proxy for the quality of the timeliness of financial information.

This study uses targeted Sampling (purposive sampling) . Population in this research is that companies engaged in property and real estate are listed in the Indonesia Stock Exchange during the study period between 2008 to 2011 . Sample totaled 128 with 32 companies that have been based on the criteria specified . Analysis of research data using multiple linear regression with SPSS Version 16.0 .

Based on the analysis performed in this study shows that of the four proposed variables (leverage , liquidity , firm size , and quality audit) only liquidity that have a significant positive impact on the quality of financial reporting information , while leverage , firm size , and quality audit is not affect the quality of financial reporting information.

Keywords : leverage , liquidity , firm size , audit quality , timeliness .
PENGARUH PEMAHAMAN WAJIB PAJAK, KESADARAN WAJIB PAJAK, DAN PELAYANAN FISKUS TERHADAP KEPATUHAN WAJIB PAJAK BUMI DAN BANGUNAN
Authors: Oktafiyanto Imam , Wardani Dewi Kusuma
Published: July 2017

The purpose of this study was to determine the affect of understanding the taxpayer, the taxpayer awareness, and services tax authorities on tax compliance and building earth. This research was conducted in the department of Regional Tax and Financial Management of Yogyakarta. Data Collected by using a questionnaire. Tecnical analysis of the data used is

multiple linier regression technique. Based on the analysis that has beeb done, it can be seen that the understanding of the taxpayer, the taxpayer awarenness, and service tax authoroties on tax compliance affect the land and building.

Keywords: Understanding taxpayer, taxpayer Awareness, Service tax authorities andTaxpayer Compliance.
DE UNA SUJETO FEMENINA A UNA SUJETO MUJER-CRÍTICA. PEDAGOGÍAS DEL CUERPO EN LANGUIDEZ Y OCRE DE ALFONSINA STORNI
Authors: Zapata de Aston Arcea
Published: July 2017

In this article, we propose a reading of the poetry of Alfonsina Storni, considering writing/biography as the backbone that guides and regulates a dialogue between her experience and a political, social, and economic scene penetrated by the own dynamics of the patriarchal discourse. It is at this juncture that the author manifests a writing approach that enables the body to generate its own pedagogy aimed at unveiling the necessary tools to, as Alicia Salomone states “with masks or without them,” becoming a female subject “predominantly a critical woman-subject.”
Percepciones de autoevaluación: Autoestima, autoeficacia y satisfacción vital en la adolescencia
Authors: Reina Mª Del Carmen
Published: July 2017

Resumen: En este estudio se analizan las relaciones entre tres variables relativas a la autoevaluación como son la autoestima, la autoeficacia y la satisfacción vital, así como sus tendencias evolutivas y las diferencias de género en una muestra de 2400 adolescentes con edades comprendidas entre los 12 y 17 años pertenecientes a 20 centros educativos de Andalucía Occidental. Por otro lado, se presentan las relaciones de estas tres variables con el estilo educativo parental. Los resultados indicaron correlaciones significativas entre las variables de autoevaluación, así como con las dimensiones del estilo parental. Concretamente, los niveles de afecto-comunicación y el sentido del humor de madres y padres fueron aspectos que se relacionaron de forma muy significativa con la autoevaluación adolescente. Por otra parte, aparecieron importantes diferencias de género en las trayectorias que la autoestima, la autoeficacia y la satisfacción vital siguen durante la adolescencia, ya que mientras que en los chicos se observaron pocos cambios, en las chicas la tendencia fue descendente, de forma que las adolescentes de más edad mostraron las autovaloraciones más bajas. Adolescents own perceptions of self-evaluation: Self esteem, self efficacy and life satisfaction Abstract: In this study we analyse the relationships between three variables of self-evaluations, being self esteem, self efficacy and life satisfaction. Moreover, we study the evolution of these three aspects during adolescence paying attention to gender differences. The sample was made up of 2400 teenagers aged between 12 and 17 years old taken from 20 high schools in Western Andalusia. In this study we also analyse the relationship between teenager self evaluation and parenting style. Our results show, on one hand, high correlation between self esteem, self efficacy and life satisfaction, and on the other hand, between teenager self evaluation and parenting style. In fact, parental warmth and communication and sense of humor show high correlation with adolescent self evaluation. We also found increasing differences between boys and girls as regards their self esteem, self efficacy and life satisfaction as adolescence progressed. Specifically, little changed was observed in boys, whereas girls’ self-evaluation decreased with age.