bluuebunny/crossref_metadata_2025 huggingface dataset:
Trap Avoidance in Local Search Using Pseudo-Conflict Learning
Authors: Pham Duc , Duong Thach-Thao , Sattar Abdul
Published: June 2022

A key challenge in developing efficient local search solvers is to effectively minimise search stagnation (i.e. avoiding traps or local minima). A majority of the state-of-the-art local search solvers perform random and/or Novelty-based walks to overcome search stagnation. Although such strategies are effective in diversifying a search from its current local minimum, they do not actively prevent the search from visiting previously encountered local minima. In this paper, we propose a new preventative strategy to effectively minimise search stagnation using pseudo-conflict learning. We define a pseudo-conflict as a derived path from the search trajectory that leads to a local minimum. We then introduce a new variable selection scheme that penalises variables causing those pseudo-conflicts. Our experimental results show that the new preventative approach significantly improves the performance of local search solvers on a wide range of structured and random benchmarks.
A Multi-Domain Evaluation of Scaling in a General Episodic Memory
Authors: Derbinsky Nate , Li Justin , Laird John
Published: June 2022

Episodic memory endows agents with numerous general cognitive capabilities, such as action modeling and virtual sensing. However, for long-lived agents, there are numerous unexplored computational challenges in supporting useful episodic-memory functions while maintaining real-time reactivity. In this paper, we review the implementation of episodic memory in Soar and present an expansive evaluation of that system. We demonstrate useful applications of episodic memory across a variety of domains, including games, mobile robotics, planning, and linguistics. In these domains, we characterize properties of environments, tasks, and episodic cues that affect performance, and evaluate the ability of Soar’s episodic memory to support hours to days of real-time operation.
Sentic Activation: A Two-Level Affective Common Sense Reasoning Framework
Authors: Cambria Erik , Olsher Daniel , Kwok Kenneth
Published: June 2022

An important difference between traditional AI systems and human intelligence is our ability to harness common sense knowledge gleaned from a lifetime of learning and experiences to inform our decision making and behavior. This allows humans to adapt easily to novel situations where AI fails catastrophically for lack of situation-specific rules and generalization capabilities. Common sense knowledge also provides the background knowledge for humans to successfully operate in social situations where such knowledge is typically assumed. In order for machines to exploit common sense knowledge in reasoning as humans do, moreover, we need to endow them with human-like reasoning strategies. In this work, we propose a two-level affective reasoning framework that concurrently employs multi-dimensionality reduction and graph mining techniques to mimic the integration of conscious and unconscious reasoning, and exploit it for sentiment analysis.
Iterative Voting under Uncertainty for Group Recommender Systems (Research Abstract)
Authors: Naamani-Dery Lihi
Published: June 2022

Group Recommendation Systems (GRS's) assist groups when trying to reach a joint decision. I use probabilistic data and apply voting theory to GRS’s in order to minimize user interaction and output an approximate or definite “winner item
Solution Diversity in Planning
Authors: Coman Alexandra
Published: June 2022

Diverse planning consists of generating multiple different solutions for the same planning problem. I explore solution diversity, based on quantitative (domain-independent) and qualitative (domain-dependent) distance metrics, in deterministic and nondeterministic planning domains.
Generalizing and Executing Plans
Authors: Muise Christian
Published: June 2022

In a dynamic environment, an intelligent agent must consider unexpected changes to the world and plan for them. We aim to address this key issue by building more robust artificial agents through the generalization of plan representations. Our research focuses on the process of generalizing various plan forms and the development of a compact representation which embodies a generalized plan as a policy. Our techniques allow an agent to execute efficiently in an online setting. We have, to date, demonstrated our approach for sequential and partial order plans and are pursuing similar techniques for representations such as Hierarchical Task Networks and GOLOG programs
Transfer Learning in Collaborative Filtering with Uncertain Ratings
Authors: Pan Weike , Xiang Evan , Yang Qiang
Published: June 2022

To solve the sparsity problem in collaborative filtering, researchers have introduced transfer learning as a viable approach to make use of auxiliary data. Most previous transfer learning works in collaborative filtering have focused on exploiting point-wise ratings such as numerical ratings, stars, or binary ratings of likes/dislikes. However, in many real-world recommender systems, many users may be unwilling or unlikely to rate items with precision.In contrast, practitioners can turn to various non-preference data to estimate a range or rating distribution of a user's preference on an item.Such a range or rating distribution is called an uncertain rating since it represents a rating spectrum of uncertainty instead of an accurate point-wise score. In this paper, we propose an efficient transfer learning solution for collaborative filtering, known as {\em transfer by integrative factorization} (TIF), to leverage such auxiliary uncertain ratings to improve the performance of recommendation. In particular, we integrate auxiliary data of uncertain ratings as additional constraints in the target matrix factorization problem, and learn an expected rating value for each uncertain rating automatically. The advantages of our proposed approach include the efficiency and the improved effectiveness of collaborative filtering, showing that incorporating the auxiliary data of uncertain ratings can really bring a benefit. Experimental results on two movie recommendation tasks show that our TIF algorithm performs significantly better over a state-of-the-art non-transfer learning method.
Document Summarization Based on Data Reconstruction
Authors: He Zhanying , Chen Chun , Bu Jiajun , Wang Can , Zhang Lijun , Cai Deng , He Xiaofei
Published: June 2022

Document summarization is of great value to many real world applications, such as snippets generation for search results and news headlines generation. Traditionally, document summarization is implemented by extracting sentences that cover the main topics of a document with a minimum redundancy. In this paper, we take a different perspective from data reconstruction and propose a novel framework named Document Summarization based on Data Reconstruction (DSDR). Specifically, our approach generates a summary which consist of those sentences that can best reconstruct the original document. To model the relationship among sentences, we introduce two objective functions: (1) linear reconstruction, which approximates the document by linear combinations of the selected sentences; (2) nonnegative linear reconstruction, which allows only additive, not subtractive, linear combinations. In this framework, the reconstruction error becomes a natural criterion for measuring the quality of the summary. For each objective function, we develop an efficient algorithm to solve the corresponding optimization problem. Extensive experiments on summarization benchmark data sets DUC 2006 and DUC 2007 demonstrate the effectiveness of our proposed approach.
Content Recommendation for Attention Management in Unified Social Messaging
Authors: Jin Hongxia
Published: June 2022

With the growing popularity of social networks and collaboration systems, people are increasingly working with or socially connected with each other. Unified messaging system provides a single interface for users to receive and process information from multiple sources. It is highly desirable to design attention management solution that can help users easily navigate and process dozens of unread messages from a unified message system. Moreover, with the proliferation of mobile devices people are now selectively consuming the most important messages on the go between different activities in their daily life. The information overload problem is especially acute for mobile users with small screen to display. In this paper, we present \PAM, an intelligent end-to-end Personalized Attention Management solution that employs analytical techniques that can learn user interests and organize and prioritize incoming messages based on user interests. For a list of unread messages, \PAM generates a concise attention report that allows users to quickly scan the important new messages from his important social connections as well as messages about his most important tasks that the user is involved with. Our solution can also be applied in other applications such as news filtering and alerts on mobile devices. Our evaluation results demonstrate the effectiveness of \PAM.
Probabilistic Alternating-Time Temporal Logic of Incomplete Information and Synchronous Perfect Recall
Authors: Huang Xiaowei , Su Kaile , Zhang Chenyi
Published: June 2022

A probabilistic variant of ATL* logic is proposed to work with multi-player games of incomplete information and synchronous perfect recall. The semantics of the logic is settled over probabilistic interpreted system and partially observed probabilistic concurrent game structure. While unexpectedly, the model checking problem is in general undecidable even for single-group fragment, we find a fragment whose complexity is in 2-EXPTIME. The usefulness of this fragment is shown over a land search scenario.
Equality-Friendly Well-Founded Semantics and Applications to Description Logics
Authors: Gottlob Georg , Hernich André , Kupke Clemens , Lukasiewicz Thomas
Published: June 2022

We tackle the problem of defining a well-founded semantics for Datalog rules with existentially quantified variables in their heads and negations in their bodies. In particular, we provide a well-founded semantics (WFS) for the recent Datalog+/- family of ontology languages, which covers several important description logics (DLs). To do so, we generalize Datalog+/- by non-stratified nonmonotonic negation in rule bodies, and we define a WFS for this generalization via guarded fixed-point logic. We refer to this approach as equality-friendly WFS, since it has the advantage that it does not make the unique name assumption (UNA); this brings it close to OWL and its profiles as well as typical DLs, which also do not make the UNA. We prove that for guarded Datalog+/- with negation under the equality-friendly WFS, conjunctive query answering is decidable, and we provide precise complexity results for this problem. From these results, we obtain precise definitions of the standard WFS extensions of EL and of members of the DL-Lite family, as well as corresponding complexity results for query answering.
Optimal Proportional Cake Cutting with Connected Pieces
Authors: Bei Xiaohui , Chen Ning , Hua Xia , Tao Biaoshuai , Yang Endong
Published: June 2022

We consider the classic cake cutting problem where one allocates a divisible cake to n participating agents. Among all valid divisions, fairness and efficiency (a.k.a. ~social welfare) are the most critical criteria to satisfy and optimize, respectively. We study computational complexity of computing an efficiency optimal division given the conditions that the allocation satisfies proportional fairness and assigns each agent a connected piece. For linear valuation functions, we give a polynomial time approximation scheme to compute an efficiency optimal allocation. On the other hand, we show that the problem is NP-hard to approximate within a factor of Ω 1/√n for general piecewise constant functions, and is NP-hard to compute for normalized functions.
A Multivariate Complexity Analysis of Lobbying in Multiple Referenda
Authors: Bredereck Robert , Chen Jiehua , Hartung Sepp , Niedermeier Rolf , Suchý Ondřej , Kratsch Stefan
Published: June 2022

We extend work by Christian et al. [Review of Economic Design 2007] on lobbying in multiple referenda by first providing a more fine-grained analysis of the computational complexity of the NP-complete Lobbying problem. Herein, given a binary matrix, the columns represent issues to vote on and the rows correspond to voters making a binary vote on each issue. An issue is approved if a majority of votes has a 1 in the corresponding column. The goal is to get all issues approved by modifying a minimum number of rows to all-1-rows. In our multivariate complexity analysis, we present a more holistic view on the nature of the computational complexity of Lobbying, providing both (parameterized) tractability and intractability results, depending on various problem parameterizations to be adopted. Moreover, we show non-existence results concerning efficient and effective preprocessing for Lobbying and introduce natural variants such as Restricted Lobbying and Partial Lobbying.
Dynamic Matching via Weighted Myopia with Application to Kidney Exchange
Authors: Dickerson John , Procaccia Ariel , Sandholm Tuomas
Published: June 2022

In many dynamic matching applications — especially high-stakes ones — the competitive ratios of prior-free online algorithms are unacceptably poor. The algorithm should take distributional information about possible futures into account in deciding what action to take now. This is typically done by drawing sample trajectories of possible futures at each time period, but may require a prohibitively large number of trajectories or prohibitive memory and/or computation to decide what action to take. Instead, we propose to learn potentials of elements (e.g., vertices) of the current problem. Then, at run time, we simply run an offline matching algorithm at each time period, but subtracting out in the objective the potentials of the elements used up in the matching. We apply the approach to kidney exchange. Kidney exchanges enable willing but incompatible patient-donor pairs (vertices) to swap donors. These swaps typically include cycles longer than two pairs and chains triggered by altruistic donors. Fielded exchanges currently match myopically, maximizing the number of patients who get kidneys in an offline fashion at each time period. Myopic matching is sub-optimal; the clearing problem is dynamic since patients, donors, and altruists appear and expire over time. We theoretically compare the power of using potentials on increasingly large elements: vertices, edges, cycles, and the entire graph (optimum). Then, experiments show that by learning vertex potentials, our algorithm matches more patients than the current practice of clearing myopically. It scales to exchanges orders of magnitude beyond those handled by the prior dynamic algorithm.
Optimizing Payments in Dominant-Strategy Mechanisms for Multi-Parameter Domains
Authors: Dufton Lachlan , Naroditskiy Victor , Polukarov Maria , Jennings Nicholas
Published: June 2022

In AI research, mechanism design is typically used to allocate tasks and resources to agents holding private information about their values for possible allocations. In this context, optimizing payments within the Groves class has recently received much attention, mostly under the assumption that agent's private information is single-dimensional. Our work tackles this problem in multi-parameter domains. Specifically, we develop a generic technique to look for a best Groves mechanism for any given mechanism design problem. Our method is based on partitioning the spaces of agent values and payment functions into regions, on each of which we are able to define a feasible linear payment function. Under certain geometric conditions on partitions of the two spaces this function is optimal. We illustrate our method by applying it to the problem of allocating heterogeneous items.
Computing Optimal Strategies to Commit to in Stochastic Games
Authors: Letchford Joshua , MacDermed Liam , Conitzer Vincent , Parr Ronald , Isbell Charles
Published: June 2022

Significant progress has been made recently in the following two lines of research in the intersection of AI and game theory: (1) the computation of optimal strategies to commit to (Stackelberg strategies), and (2) the computation of correlated equilibria of stochastic games. In this paper, we unite these two lines of research by studying the computation of Stackelberg strategies in stochastic games. We provide theoretical results on the value of being able to commit and the value of being able to correlate, as well as complexity results about computing Stackelberg strategies in stochastic games. We then modify the QPACE algorithm (MacDermed et al. 2011) to compute Stackelberg strategies, and provide experimental results.
Approximately Revenue-Maximizing Auctions for Deliberative Agents
Authors: Celis L. , Karlin Anna , Leyton-Brown Kevin , Nguyen C. , Thompson David
Published: June 2022

In many real-world auctions, a bidder does not know her exact value for an item, but can perform a costly deliberation to reduce her uncertainty. Relatively little is known about such deliberative environments, which are fundamentally different from classical auction environments. In this paper, we propose a new approach that allows us to leverage classical revenue-maximization results in deliberative environments. In particular, we use Myerson (1981) to construct the first non-trivial (i.e., dependent on deliberation costs) upper bound on revenue in deliberative auctions. This bound allows us to apply existing results in the classical environment to a deliberative environment. In addition, we show that in many deliberative environments the only optimal dominant-strategy mechanisms take the form of sequential posted-price auctions.
Sequence Labeling with Non-Negative Weighted Higher Order Features
Authors: Qian Xian , Liu Yang
Published: June 2022

In sequence labeling, using higher order features leads to high inference complexity. A lot of studies have been conducted to address this problem. In this paper, we propose a new exact decoding algorithm under the assumption that weights of all higher order features are non-negative. In the worst case, the time complexity of our algorithm is quadratic on the number of higher order features. Comparing with existing algorithms, our method is more efficient and easier to implement. We evaluate our method on two sequence labeling tasks: Optical Character Recognition and Chinese part-of-speech tagging. Our experimental results demonstrate that adding higher order features significantly improves the performance while requiring only 30% additional inference time.
A Bayesian Approach to the Data Description Problem
Authors: Ghasemi Alireza , Rabiee Hamid R. , Manzuri Mohammad Taghi , Rohban Mohammad Hossein
Published: June 2022

In this paper, we address the problem of data description using a Bayesian framework. The goal of data description is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach uses a Bayesian framework to precisely compute the class boundary and therefore can utilize domain information in form of prior knowledge in the framework. It can also operate in the kernel space and therefore recognize arbitrary boundary shapes. Moreover, the proposed method can utilize unlabeled data in order to improve accuracy of discrimination. We evaluate our method using various real-world datasets and compare it with other state of the art approaches of data description. Experiments show promising results and improved performance over other data description and one-class learning algorithms.
Unsupervised Feature Selection Using Nonnegative Spectral Analysis
Authors: Li Zechao , Yang Yi , Liu Jing , Zhou Xiaofang , Lu Hanqing
Published: June 2022

In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the cluster labels of the input samples, during which the feature selection is performed simultaneously. The joint learning of the cluster labels and feature selection matrix enables NDFS to select the most discriminative features. To learn more accurate cluster labels, a nonnegative constraint is explicitly imposed to the class indicators. To reduce the redundant or even noisy features, l2,1-norm minimization constraint is added into the objective function, which guarantees the feature selection matrix sparse in rows. Our algorithm exploits the discriminative information and feature correlation simultaneously to select a better feature subset. A simple yet efficient iterative algorithm is designed to optimize the proposed objective function. Experimental results on different real world datasets demonstrate the encouraging performance of our algorithm over the state-of-the-arts.
Learning the Kernel Matrix with Low-Rank Multiplicative Shaping
Authors: Levinboim Tomer , Sha Fei
Published: June 2022

Selecting the optimal kernel is an important and difficult challenge in applying kernel methods to pattern recognition. To address this challenge, multiple kernel learning (MKL) aims to learn a kernel from a combination of base kernel functions that perform optimally on the task. In this paper, we propose a novel MKL-themed approach to combine base kernels that are multiplicatively shaped with low-rank positive semidefinitve matrices. The proposed approach generalizes several popular MKL methods and thus provides more flexibility in modeling data. Computationally, we show how these low-rank matrices can be learned efficiently from data using convex quadratic programming. Empirical studies on several standard benchmark datasets for MKL show that the new approach often improves prediction accuracy statistically significantly over very competitive single kernel and other MKL methods.
Towards Population Scale Activity Recognition: A Framework for Handling Data Diversity
Authors: Abdullah Saeed , Lane Nicholas , Choudhury Tanzeem
Published: June 2022

The rising popularity of the sensor-equipped smartphone is changing the possible scale and scope of human activity inference. The diversity in user population seen in large user bases can overwhelm conventional one-size-fits-all classiﬁcation approaches. Although personalized models are better able to handle population diversity, they often require increased effort from the end user during training and are computationally expensive. In this paper, we propose an activity classification framework that is scalable and can tractably handle an increasing number of users. Scalability is achieved by maintaining distinct groups of similar users during the training process, which makes it possible to account for the differences between users without resorting to training individualized classifiers. The proposed framework keeps user burden low by leveraging crowd-sourced data labels, where simple natural language processing techniques in combination with multi-instance learning are used to handle labeling errors introduced by low-commitment everyday users. Experiment results on a large public dataset demonstrate that the framework can cope with population diversity irrespective of population size.
Clustering Documents Along Multiple Dimensions
Authors: Dasgupta Sajib , Golden Richard , Ng Vincent
Published: June 2022

Traditional clustering algorithms are designed to search for a single clustering solution despite the fact that multiple alternative solutions might exist for a particular dataset. For example, a set of news articles might be clustered by topic or by the author's gender or age. Similarly, book reviews might be clustered by sentiment or comprehensiveness. In this paper, we address the problem of identifying alternative clustering solutions by developing a Probabilistic Multi-Clustering (PMC) model that discovers multiple, maximally different clusterings of a data sample. Empirical results on six datasets representative of real-world applications show that our PMC model exhibits superior performance to comparable multi-clustering algorithms.
Generalized Monte-Carlo Tree Search Extensions for General Game Playing
Authors: Finnsson Hilmar
Published: June 2022

General Game Playing (GGP) agents must be capable of playing a wide variety of games skillfully. Monte-Carlo Tree Search (MCTS) has proven an effective reasoning mechanism for this challenge, as is reflected by its popularity among designers of GGP agents. Providing GGP agents with the knowledge relevant to the game at hand in real time is, however, a challenging task. In this paper we propose two enhancements for MCTS in the context of GGP, aimed at improving the effectiveness of the simulations in real time based on in-game statistical feedback. The first extension allows early termination of lengthy and uninformative simulations while the second improves the action-selection strategy when both explored and unexplored actions are available. The methods are empirically evaluated in a state-of-the-art GGP agent and shown to yield an overall significant improvement in playing strength.
Algorithmic and Human Teaching of Sequential Decision Tasks
Authors: Cakmak Maya , Lopes Manuel
Published: June 2022

A helpful teacher can significantly improve the learning rate of a learning agent. Teaching algorithms have been formally studied within the field of Algorithmic Teaching. These give important insights into how a teacher can select the most informative examples while teachinga new concept. However the field has so far focused purely on classification tasks. In this paper we introducea novel method for optimally teaching sequential decision tasks. We present an algorithm that automatically selects the set of most informative demonstrations andevaluate it on several navigation tasks. Next, we explore the idea of using this algorithm to produce instructions for humans on how to choose examples when teaching sequential decision tasks. We present a user study that demonstrates the utility of such instructions.
HyperPlay: A Solution to General Game Playing with Imperfect Information
Authors: Schofield Michael , Cerexhe Timothy , Thielscher Michael
Published: June 2022

General Game Playing is the design of AI systems able to understand the rules of new games and to use such descriptions to play those games effectively. Games with imperfectinformation have recently been added as a new challenge forexisting general game-playing systems. The HyperPlay technique presents a solution to this challenge by maintaining a collection of models of the true game as a foundation for reasoning, and move selection. The technique provides existing game players with a bolt-on solution to convert from perfect-information games to imperfect-information games. In this paper we describe the HyperPlay technique, show how it was adapted for use with a Monte Carlo decision making process and give experimental results for its performance.
Performance and Preferences: Interactive Refinement of Machine Learning Procedures
Authors: Kapoor Ashish , Lee Bongshin , Tan Desney , Horvitz Eric
Published: June 2022

Problem-solving procedures have been typically aimed at achieving well-defined goals or satisfying straightforward preferences. However, learners and solvers may often generate rich multiattribute results with procedures guided by sets of controls that define different dimensions of quality. We explore methods that enable people to explore and express preferences about the operation of classification models in supervised multiclass learning. We leverage a leave-one-out confusion matrix that provides users with views and real-time controls of a model space. The approach allows people to consider in an interactive manner the global implications of local changes in decision boundaries. We focus on kernel classifiers and show the effectiveness of the methodology on a variety of tasks.
Automatically Generating Algebra Problems
Authors: Singh Rohit , Gulwani Sumit , Rajamani Sriram
Published: June 2022

We propose computer-assisted techniques for helping with pedagogy in Algebra. In particular, given a proof problem p (of the form “Left-hand-side-term = Right-hand-side-term”), we show how to automatically generate problems that are similar to p. We believe that such a tool can be used by teachers in making examinations where they need to test students on problems similar to what they taught in class, and by students in generating practice problems tailored to their specific needs. Our first insight is that we can generalize p syntactically to a query Q that implicitly represents a set of problems [[Q]] (which includes p). Our second insight is that we can explore the space of problems [[Q]] automatically, use classical results from polynomial identity testing to generate only those problems in [[Q]] that are correct, and then use pruning techniques to generate only unique and interesting problems. Our third insight is that with a small amount of manual tuning on the query Q, the user can interactively guide the computer to generate problems of interest to her. We present the technical details of the above mentioned steps, and also describe a tool where these steps have been implemented. We also present an empirical evaluation on a wide variety of problems from various sub-fields of algebra including polynomials, trigonometry, calculus, determinants etc. Our tool is able to generate a rich corpus of similar problems from each given problem; while some of these similar problems were already present in the textbook, several were new!
Emoticon Smoothed Language Models for Twitter Sentiment Analysis
Authors: Liu Kun-Lin , Li Wu-Jun , Guo Minyi
Published: June 2022

Twitter sentiment analysis (TSA) has become a hot research topic in recent years. The goal of this task is to discover the attitude or opinion of the tweets, which is typically formulated as a machine learning based text classification problem. Some methods use manually labeled data to train fully supervised models, while others use some noisy labels, such as emoticons and hashtags, for model training. In general, we can only get a limited number of training data for the fully supervised models because it is very labor-intensive and time-consuming to manually label the tweets. As for the models with noisy labels, it is hard for them to achieve satisfactory performance due to the noise in the labels although it is easy to get a large amount of data for training. Hence, the best strategy is to utilize both manually labeled data and noisy labeled data for training. However, how to seamlessly integrate these two different kinds of data into the same learning framework is still a challenge. In this paper, we present a novel model, called emoticon smoothed language model (ESLAM), to handle this challenge. The basic idea is to train a language model based on the manually labeled data, and then use the noisy emoticon data for smoothing. Experiments on real data sets demonstrate that ESLAM can effectively integrate both kinds of data to outperform those methods using only one of them.
Structural Patterns Beyond Forks: Extending the Complexity Boundaries of Classical Planning
Authors: Katz Michael , Keyder Emil
Published: June 2022

Tractability analysis in terms of the causal graphs of planning problems has emerged as an important area of research in recent years, leading to new methods for the derivation of domain-independent heuristics (Katz and Domshlak 2010). Here we continue this work, extending our knowledge of the frontier between tractable and NP-complete fragments. We close some gaps left in previous work, and introduce novel causal graph fragments that we call the hourglass and semifork, for which under certain additional assumptions optimal planning is in P. We show that relaxing any one of the restrictions required for this tractability leads to NP-complete problems. Our results are of both theoretical and practical interest, as these fragments can be used in existing frameworks to derive new abstraction heuristics. Before they can be used, however, a number of practical issues must be addressed. We discuss these issues and propose some solutions.
Efficient Approximate Value Iteration for Continuous Gaussian POMDPs
Authors: Van den Berg Jur , Patil Sachin , Alterovitz Ron
Published: June 2022

We introduce a highly efficient method for solving continuous partially-observable Markov decision processes (POMDPs) in which beliefs can be modeled using Gaussian distributions over the state space. Our method enables fast solutions to sequential decision making under uncertainty for a variety of problems involving noisy or incomplete observations and stochastic actions. We present an efficient approach to compute locally-valid approximations to the value function over continuous spaces in time polynomial (O[n^4]) in the dimension n of the state space. To directly tackle the intractability of solving general POMDPs, we leverage the assumption that beliefs are Gaussian distributions over the state space, approximate the belief update using an extended Kalman filter (EKF), and represent the value function by a function that is quadratic in the mean and linear in the variance of the belief. Our approach iterates towards a linear control policy over the state space that is locally-optimal with respect to a user defined cost function, and is approximately valid in the vicinity of a nominal trajectory through belief space. We demonstrate the scalability and potential of our approach on problems inspired by robot navigation under uncertainty for state spaces of up to 128 dimensions.
Occupancy Grid Models for Robot Mapping in Changing Environments
Authors: Meyer-Delius Daniel , Beinhofer Maximilian , Burgard Wolfram
Published: June 2022

The majority of existing approaches to mobile robot mapping assumes that the world is static, which is generally not justified in real-world applications. However, in many navigation tasks including trajectory planning, surveillance, and coverage, accurate maps are essential for the effective behavior of the robot.  In this paper we present a probabilistic grid-based approach for modeling changing environments. Our method represents both, the occupancy and its changes in the corresponding area where the dynamics are characterized by the state transition probabilities of a Hidden Markov Model. We apply an offline and an online technique to learn the parameters from observed data. The advantage of the online approach is that it can dynamically adapt the parameters and at the same time does not require storing the complete observation sequences.  Experimental results obtained with data acquired by real robots demonstrate that our model is well-suited for representing changing environments. Further results show that our technique can be used to substantially improve the effectiveness of path planning procedures.
Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information
Authors: Pandey Gaurav , McBride James , Savarese Silvio , Eustice Ryan
Published: June 2022

This paper reports on a mutual information (MI) based algorithm for automatic extrinsic calibration of a 3D laser scanner and optical camera system. By using MI as the registration criterion, our method is able to work in situ without the need for any specific calibration targets, which makes it practical for in-field calibration. The calibration parameters are estimated by maximizing the mutual information obtained between the sensor-measured surface intensities. We calculate the Cramer-Rao-Lower-Bound (CRLB) and show that the sample variance of the estimated parameters empirically approaches the CRLB for a sufficient number of views. Furthermore, we compare the calibration results to independent ground-truth and observe that the mean error also empirically approaches to zero as the number of views are increased. This indicates that the proposed algorithm, in the limiting case, calculates a minimum variance unbiased (MVUB) estimate of the calibration parameters. Experimental results are presented for data collected by a vehicle mounted with a 3D laser scanner and an omnidirectional camera system.
Searching for Optimal Off-Line Exploration Paths in Grid Environments for a Robot with Limited Visibility
Authors: Quattrini Li Alberto , Amigoni Francesco , Basilico Nicola
Published: June 2022

Robotic exploration is an on-line problem in which autonomous mobile robots incrementally discover and map the physical structure of initially unknown environments. Usually, the performance of exploration strategies used to decide where to go next is not compared against the optimal performance obtainable in the test environments, because the latter is generally unknown. In this paper, we present a method to calculate an approximation of the optimal (shortest) exploration path in an arbitrary environment. We consider a mobile robot with limited visibility, discretize a two-dimensional environment with a regular grid, and formulate a search problem for finding the optimal exploration path in the grid, which is solved using A*. Experimental results show the viability of our approach for realistically large environments and its potential for better assessing the performance of on-line exploration strategies.
Visual Saliency Estimation through Manifold Learning
Authors: Jiang Richard , Crookes Danny
Published: June 2022

Saliency detection has been a desirable way for robotic vision to find the most noticeable objects in a scene. In this paper, a robust manifold-based saliency estimation method has been developed to help capture the most salient objects in front of robotic eyes, namely cameras. In the proposed approach, an image is considered as a manifold of visual signals (stimuli) spreading over a connected grid, and local visual stimuli are compared against the global image variation to model the visual saliency. With this model, manifold learning is then applied to minimize the local variation while keeping the global contrast, and turns the RGB image into a multi-channel image. After the projection through manifold learning, histogram-based contrast is then computed for saliency modeling of all channels of the projected images, and mutual information is introduced to evaluate each single-channel saliency map against prior knowledge to provide cues for the fusion of multiple channels. In the last step, the fusion procedure combines all single-channel saliency maps according to their mutual information score, and generates the final saliency map. In our experiment, the proposed method is evaluated using one of the largest publicly available image datasets. The experimental results demonstrate that our algorithm consistently outperforms the state-of-the-art unsupervised saliency detection methods, yielding higher precision and better recall rates. Furthermore, the proposed method is tested on a video-type test dataset where a moving camera is trying to catch up with the walking person---a salient object in the video sequence. Our experimental results show that the proposed approach can successful accomplish this task, revealing its potential use for similar robotic applications.
Modeling Context Aware Dynamic Trust Using Hidden Markov Model
Authors: Liu Xin , Datta Anwitaman
Published: June 2022

Modeling trust in complex dynamic environments is an important yet challenging issue since an intelligent agent may strategically change its behavior to maximize its profits. In thispaper, we propose a context aware trust model to predict dynamic trust by using a Hidden Markov Model (HMM) to model an agent's interactions. Although HMMs have already been applied in the past to model an agent's dynamic behavior to greatly improve the traditional static probabilistic trust approaches, most HMM based trust models only focus on outcomes of the past interactions without considering interaction context, which we believe, reflects immensely on the dynamic behavior or intent of an agent. Interaction contextual information is comprehensively studied and integrated into the model to more precisely approximate an agent's dynamic behavior. Evaluation using real auction data and synthetic data demonstrates the efficacy of our approach in comparison with previous state-of-the-art trust mechanisms.
Lifted MEU by Weighted Model Counting
Authors: Apsel Udi , Brafman Ronen
Published: June 2022

Recent work in the field of probabilistic inference demonstrated the efficiency of weighted model counting (WMC) enginesfor exact inference in propositional and, very recently, first order models. To date, these methods have not been applied to decision making models, propositional or first order, such as influence diagrams, and Markov decision networks (MDN). In this paper we show how this technique can be applied to such models. First, we show how WMC can be used to solve (propositional) MDNs. Then, we show how this can be extended to handle a first-order model — the Markov Logic Decision Network (MLDN). WMC offers two central benefits: it is a very simple and very efficient technique. This is particularly true for the first-order case, where the WMC approach is simpler conceptually, and, in many cases, more effective computationally than the existing methods for solving MLDNs via first-order variable elimination, or via propositionalization. We demonstrate the above empirically.
Using Sliding Windows to Generate Action Abstractions in Extensive-Form Games
Authors: Hawkin John , Holte Robert , Szafron Duane
Published: June 2022

In extensive-form games with a large number of actions, careful abstraction of the action space is critically important to performance. In this paper we extend previous work on action abstraction using no-limit poker games as our test domains. We show that in such games it is no longer necessary to choose, a priori, one specific range of possible bet sizes. We introduce an algorithm that adjusts the range of bet sizes considered for each bet individually in an iterative fashion. This flexibility results in a substantially improved game value in no-limit Leduc poker. When applied to no-limit Texas Hold'em our algorithm produces an action abstraction that is about one third the size of a state of the art hand-crafted action abstraction, yet has a better overall game value.
A Theoretical Framework of the Graph Shift Algorithm
Authors: Fan Xuhui , Cao Longbing
Published: June 2022

Since no theoretical foundations for proving the convergence of Graph Shift Algorithm have been reported, we provide a generic framework consisting of three key GS components to fit the Zangwill’s convergence theorem. We show that the sequence set generated by the GS procedures always terminates at a local maximum, or at worst, contains a subsequence which converges to a local maximum of the similarity measure function. What is more, a theoretical framework is proposed to apply our proof to a more general case.
A Market-Based Coordination Mechanism for Resource Planning Under Uncertainty
Authors: Hosseini Hadi , Hoey Jesse , Cohen Robin
Published: June 2022

Multiagent Resource Allocation (MARA) distributes a set of resources among a set of intelligent agents in order to respect the preferences of the agents and to maximize some measure of global utility, which may include minimizing total costs or maximizing total return. We are interested in MARA solutions that provide optimal or close-to-optimal allocation of resources in terms of maximizing a global welfare function with low communication and computation cost, with respect to the priority of agents, and temporal dependencies between resources. We propose an MDP approach for resource planning in multiagent environments. Our approach formulates internal preference modeling and success of each individual agent as a single MDP and then to optimize global utility, we apply a market-based solution to coordinate these decentralized MDPs.
Seven Challenges in Parallel SAT Solving
Authors: Hamadi Youssef , Wintersteiger Christoph
Published: June 2022

This paper provides a broad overview of the situation in the area of Parallel Search with a specific focus on Parallel SAT Solving. A set of challenges to researchers is presented which, we believe, must be met to ensure the practical applicability of Parallel SAT Solvers in the future. All these challenges are described informally, but put into perspective with related research results, and a (subjective) grading of difficulty for each of them is provided.
Goal Recognition with Markov Logic Networks for Player-Adaptive Games
Authors: Ha Eun , Rowe Jonathan , Mott Bradford , Lester James
Published: June 2022

Goal recognition in digital games involves inferring players’ goals from observed sequences of low-level player actions. Goal recognition models support player-adaptive digital games, which dynamically augment game events in response to player choices for a range of applications, including entertainment, training, and education. However, digital games pose significant challenges for goal recognition, such as exploratory actions and ill-defined goals. This paper presents a goal recognition framework based on Markov logic networks (MLNs). The model’s parameters are directly learned from a corpus that was collected from player interactions with a non-linear educational game. An empirical evaluation demonstrates that the MLN goal recognition framework accurately predicts players’ goals in a game environment with exploratory actions and ill-defined goals.
Optimal Manipulation of Voting Rules
Authors: Obraztsova Svetlana , Elkind Edith
Published: June 2022

Complexity of voting manipulation is a prominent research topic in computational social choice. The voting manipulation literature usually assumes that the manipulator is only concerned with improving the outcome of the election from her perspective. However, in practice, the manipulator may also be reluctant to lie, i.e., she may have a preference for submitting a vote that does not deviate too much from her true ranking of the candidates. In this paper, we study the complexity of finding a manipulative vote that achieves the manipulator's goal yet is as close as possible to her true preference order. We analyze this problem for three natural notions of closeness, namely, swap distance, footrule distance, and maximum displacement distance, and a variety of voting rules, such as scoring rules, Bucklin, Copeland, and Maximin. For all three distances, we obtain polynomial-time algorithms for all scoring rules and Bucklin and hardness results for Copeland and Maximin.
Relative Attributes for Enhanced Human-Machine Communication
Authors: Parikh Devi , Kovashka Adriana , Parkash Amar , Grauman Kristen
Published: June 2022

We propose to model relative attributes that capture the relationships between images and objects in terms of human-nameable visual properties. For example, the models can capture that animal A is 'furrier' than animal B, or image X is 'brighter' than image B. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We show how these relative attribute predictions enable a variety of novel applications, including zero-shot learning from relative comparisons, automatic image description, image search with interactive feedback, and active learning of discriminative classifiers. We overview results demonstrating these applications with images of faces and natural scenes. Overall, we find that relative attributes enhance the precision of communication between humans and computer vision algorithms, providing the richer language needed to fluidly "teach" a system about visual concepts.