pwc-archive/papers-with-abstracts huggingface dataset:
Conflict Detection for Temporal Knowledge Graphs:A Fast Constraint Mining Algorithm and New Benchmarks
Authors: Jianhao Chen, Junyang Ren, Wentao Ding, Haoyuan Ouyang, Wei Hu, Yuzhong Qu
Date: 2023-12-18 00:00:00
Tasks: Management

Temporal facts, which are used to describe events that occur during specific time periods, have become a topic of increased interest in the field of knowledge graph (KG) research. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs. Previous studies rely on manually enumerated temporal constraints to detect conflicts, which are labor-intensive and may have granularity issues. To address this problem, we start from the common pattern of temporal facts and propose a pattern-based temporal constraint mining method, PaTeCon. Unlike previous studies, PaTeCon uses graph patterns and statistical information relevant to the given KG to automatically generate temporal constraints, without the need for human experts. In this paper, we illustrate how this method can be optimized to achieve significant speed improvement. We also annotate Wikidata and Freebase to build two new benchmarks for conflict detection. Extensive experiments demonstrate that our pattern-based automatic constraint mining approach is highly effective in generating valuable temporal constraints.
Learning Domain-Independent Heuristics for Grounded and Lifted Planning
Authors: Dillon Z. Chen, Sylvie Thiébaux, Felipe Trevizan
Date: 2023-12-18 00:00:00

We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular, to mitigate the issues caused by large grounded GNNs we present the first method for learning domain-independent heuristics with only the lifted representation of a planning task. We also provide a theoretical analysis of the expressiveness of our models, showing that some are more powerful than STRIPS-HGN, the only other existing model for learning domain-independent heuristics. Our experiments show that our heuristics generalise to much larger problems than those in the training set, vastly surpassing STRIPS-HGN heuristics.
An epistemic logic for modeling decisions in the context of incomplete knowledge
Authors: Đorđe Marković, Simon Vandevelde, Linde Vanbesien, Joost Vennekens, Marc Denecker
Date: 2023-12-18 00:00:00

Substantial efforts have been made in developing various Decision Modeling formalisms, both from industry and academia. A challenging problem is that of expressing decision knowledge in the context of incomplete knowledge. In such contexts, decisions depend on what is known or not known. We argue that none of the existing formalisms for modeling decisions are capable of correctly capturing the epistemic nature of such decisions, inevitably causing issues in situations of uncertainty. This paper presents a new language for modeling decisions with incomplete knowledge. It combines three principles: stratification, autoepistemic logic, and definitions. A knowledge base in this language is a hierarchy of epistemic theories, where each component theory may epistemically reason on the knowledge in lower theories, and decisions are made using definitions with epistemic conditions.
CDRH Seeks Public Comment: Digital Health Technologies for Detecting Prediabetes and Undiagnosed Type 2 Diabetes
Authors: Manuel Cossio
Date: 2023-12-18 00:00:00
Tasks: Disease Prediction

This document provides responses to the FDA's request for public comments (Docket No FDA 2023 N 4853) on the role of digital health technologies (DHTs) in detecting prediabetes and undiagnosed type 2 diabetes. It explores current DHT applications in prevention, detection, treatment and reversal of prediabetes, highlighting AI chatbots, online forums, wearables and mobile apps. The methods employed by DHTs to capture health signals like glucose, diet, symptoms and community insights are outlined. Key subpopulations that could benefit most from remote screening tools include rural residents, minority groups, high-risk individuals and those with limited healthcare access. Capturable high-impact risk factors encompass glycemic variability, cardiovascular parameters, respiratory health, blood biomarkers and patient reported symptoms. An array of non-invasive monitoring tools are discussed, although further research into their accuracy for diverse groups is warranted. Extensive health datasets providing immense opportunities for AI and ML based risk modeling are presented. Promising techniques leveraging EHRs, imaging, wearables and surveys to enhance screening through AI and ML algorithms are showcased. Analysis of social media and streaming data further allows disease prediction across populations. Ongoing innovation focused on inclusivity and accessibility is highlighted as pivotal in unlocking DHTs potential for transforming prediabetes and diabetes prevention and care.
Towards Fairness in Online Service with k Servers and its Application on Fair Food Delivery
Authors: Daman Deep Singh, Amit Kumar, Abhijnan Chakraborty
Date: 2023-12-18 00:00:00
Tasks: Fairness

The k-SERVER problem is one of the most prominent problems in online algorithms with several variants and extensions. However, simplifying assumptions like instantaneous server movements and zero service time has hitherto limited its applicability to real-world problems. In this paper, we introduce a realistic generalization of k-SERVER without such assumptions - the k-FOOD problem, where requests with source-destination locations and an associated pickup time window arrive in an online fashion, and each has to be served by exactly one of the available k servers. The k-FOOD problem offers the versatility to model a variety of real-world use cases such as food delivery, ride sharing, and quick commerce. Moreover, motivated by the need for fairness in online platforms, we introduce the FAIR k-FOOD problem with the max-min objective. We establish that both k-FOOD and FAIR k-FOOD problems are strongly NP-hard and develop an optimal offline algorithm that arises naturally from a time-expanded flow network. Subsequently, we propose an online algorithm DOC4FOOD involving virtual movements of servers to the nearest request location. Experiments on a real-world food-delivery dataset, alongside synthetic datasets, establish the efficacy of the proposed algorithm against state-of-the-art fair food delivery algorithms.
Counting Reward Automata: Sample Efficient Reinforcement Learning Through the Exploitation of Reward Function Structure
Authors: Tristan Bester, Benjamin Rosman, Steven James, Geraud Nangue Tasse
Date: 2023-12-18 00:00:00

We present counting reward automata-a finite state machine variant capable of modelling any reward function expressible as a formal language. Unlike previous approaches, which are limited to the expression of tasks as regular languages, our framework allows for tasks described by unrestricted grammars. We prove that an agent equipped with such an abstract machine is able to solve a larger set of tasks than those utilising current approaches. We show that this increase in expressive power does not come at the cost of increased automaton complexity. A selection of learning algorithms are presented which exploit automaton structure to improve sample efficiency. We show that the state machines required in our formulation can be specified from natural language task descriptions using large language models. Empirical results demonstrate that our method outperforms competing approaches in terms of sample efficiency, automaton complexity, and task completion.
The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research
Authors: Konstantinos Voudouris, Ibrahim Alhas, Wout Schellaert, Matteo G. Mecattaf, Ben Slater, Matthew Crosby, Joel Holmes, John Burden, Niharika Chaubey, Niall Donnelly, Matishalin Patel, Marta Halina, José Hernández-Orallo, Lucy G. Cheke
Date: 2023-12-18 00:00:00
Tasks: Deep Reinforcement Learning

The Animal-AI Environment is a unique game-based research platform designed to facilitate collaboration between the artificial intelligence and comparative cognition research communities. In this paper, we present the latest version of the Animal-AI Environment, outlining several major features that make the game more engaging for humans and more complex for AI systems. These features include interactive buttons, reward dispensers, and player notifications, as well as an overhaul of the environment's graphics and processing for significant improvements in agent training time and quality of the human player experience. We provide detailed guidance on how to build computational and behavioural experiments with the Animal-AI Environment. We present results from a series of agents, including the state-of-the-art deep reinforcement learning agent Dreamer-v3, on newly designed tests and the Animal-AI Testbed of 900 tasks inspired by research in the field of comparative cognition. The Animal-AI Environment offers a new approach for modelling cognition in humans and non-human animals, and for building biologically inspired artificial intelligence.
Factored Online Planning in Many-Agent POMDPs
Authors: Maris F. L. Galesloot, Thiago D. Simão, Sebastian Junges, Nils Jansen
Date: 2023-12-18 00:00:00

In centralized multi-agent systems, often modeled as multi-agent partially observable Markov decision processes (MPOMDPs), the action and observation spaces grow exponentially with the number of agents, making the value and belief estimation of single-agent online planning ineffective. Prior work partially tackles value estimation by exploiting the inherent structure of multi-agent settings via so-called coordination graphs. Additionally, belief estimation methods have been improved by incorporating the likelihood of observations into the approximation. However, the challenges of value estimation and belief estimation have only been tackled individually, which prevents existing methods from scaling to settings with many agents. Therefore, we address these challenges simultaneously. First, we introduce weighted particle filtering to a sample-based online planner for MPOMDPs. Second, we present a scalable approximation of the belief. Third, we bring an approach that exploits the typical locality of agent interactions to novel online planning algorithms for MPOMDPs operating on a so-called sparse particle filter tree. Our experimental evaluation against several state-of-the-art baselines shows that our methods (1) are competitive in settings with only a few agents and (2) improve over the baselines in the presence of many agents.
Visualizing High-Dimensional Configuration Spaces: A Comprehensive Analytical Approach
Authors: Jorge Ocampo Jimenez, Wael Suleiman
Date: 2023-12-18 00:00:00
Tasks: Motion Planning

The representation of a Configuration Space C plays a vital role in accelerating the finding of a collision-free path for sampling-based motion planners where the majority of computation time is spent in collision checking of states. Traditionally, planners evaluate C's representations through limited evaluations of collision-free paths using the collision checker or by reducing the dimensionality of C for visualization. However, a collision checker may indicate high accuracy even when only a subset of the original C is represented; limiting the motion planner's ability to find paths comparable to those in the original C. Additionally, dealing with high-dimensional Cs is challenging, as qualitative evaluations become increasingly difficult in dimensions higher than three, where reduced-dimensional C evaluation may decrease accuracy in cluttered environments. In this paper, we present a novel approach for visualizing representations of high-dimensional Cs of manipulator robots in a 2D format. We provide a new tool for qualitative evaluation of high-dimensional Cs approximations without reducing the original dimension. This enhances our ability to compare the accuracy and coverage of two different high-dimensional Cs. Leveraging the kinematic chain of manipulator robots and human color perception, we show the efficacy of our method using a 7-degree-of-freedom CS of a manipulator robot. This visualization offers qualitative insights into the joint boundaries of the robot and the coverage of collision state combinations without reducing the dimensionality of the original data. To support our claim, we conduct a numerical evaluation of the proposed visualization.
Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector
Authors: An Lao, Qi Zhang, Chongyang Shi, Longbing Cao, Kun Yi, Liang Hu, Duoqian Miao
Date: 2023-12-18 00:00:00
Tasks: Contrastive Learning

Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media. Existing multimodal rumor detection has focused on mixing tokens among spatial and sequential locations for unimodal representation or fusing clues of rumor veracity across modalities. However, they suffer from less discriminative unimodal representation and are vulnerable to intricate location dependencies in the time-consuming fusion of spatial and sequential tokens. This work makes the first attempt at multimodal rumor detection in the frequency domain, which efficiently transforms spatial features into the frequency spectrum and obtains highly discriminative spectrum features for multimodal representation and fusion. A novel Frequency Spectrum Representation and fUsion network (FSRU) with dual contrastive learning reveals the frequency spectrum is more effective for multimodal representation and fusion, extracting the informative components for rumor detection. FSRU involves three novel mechanisms: utilizing the Fourier transform to convert features in the spatial domain to the frequency domain, the unimodal spectrum compression, and the cross-modal spectrum co-selection module in the frequency domain. Substantial experiments show that FSRU achieves satisfactory multimodal rumor detection performance.
Discovering Geo-dependent Stories by Combining Density-based Clustering and Thread-based Aggregation techniques
Authors: Héctor Cerezo-Costas, Ana Fernández Vilas, Manuela Martín-Vicente, Rebeca P. Díaz-Redondo
Date: 2023-12-18 00:00:00

Citizens are actively interacting with their surroundings, especially through social media. Not only do shared posts give important information about what is happening (from the users' perspective), but also the metadata linked to these posts offer relevant data, such as the GPS-location in Location-based Social Networks (LBSNs). In this paper we introduce a global analysis of the geo-tagged posts in social media which supports (i) the detection of unexpected behavior in the city and (ii) the analysis of the posts to infer what is happening. The former is obtained by applying density-based clustering techniques, whereas the latter is consequence of applying natural language processing. We have applied our methodology to a dataset obtained from Instagram activity in New York City for seven months obtaining promising results. The developed algorithms require very low resources, being able to analyze millions of data-points in commodity hardware in less than one hour without applying complex parallelization techniques. Furthermore, the solution can be easily adapted to other geo-tagged data sources without extra effort.
Decentralized traffic management of autonomous drones
Authors: Boldizsár Balázs, Tamás Vicsek, Gergő Somorjai, Tamás Nepusz, Gábor Vásárhelyi
Date: 2023-12-18 00:00:00
Tasks: Management

Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task - filled with conflicts - is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a circular area with a radius of 125 meters.
Perceptual Musical Features for Interpretable Audio Tagging
Authors: Vassilis Lyberatos, Spyridon Kantarelis, Edmund Dervakos, Giorgos Stamou
Date: 2023-12-18 00:00:00
Tasks: Audio Tagging, Interpretable Machine Learning, Music Tagging, TAG

In the age of music streaming platforms, the task of automatically tagging music audio has garnered significant attention, driving researchers to devise methods aimed at enhancing performance metrics on standard datasets. Most recent approaches rely on deep neural networks, which, despite their impressive performance, possess opacity, making it challenging to elucidate their output for a given input. While the issue of interpretability has been emphasized in other fields like medicine, it has not received attention in music-related tasks. In this study, we explored the relevance of interpretability in the context of automatic music tagging. We constructed a workflow that incorporates three different information extraction techniques: a) leveraging symbolic knowledge, b) utilizing auxiliary deep neural networks, and c) employing signal processing to extract perceptual features from audio files. These features were subsequently used to train an interpretable machine-learning model for tag prediction. We conducted experiments on two datasets, namely the MTG-Jamendo dataset and the GTZAN dataset. Our method surpassed the performance of baseline models in both tasks and, in certain instances, demonstrated competitiveness with the current state-of-the-art. We conclude that there are use cases where the deterioration in performance is outweighed by the value of interpretability.
Optimised Storage for Datalog Reasoning
Authors: Xinyue Zhang, Pan Hu, Yavor Nenov, Ian Horrocks
Date: 2023-12-18 00:00:00

Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts. However, storing all materialised facts may be infeasible in practice, especially when the rules are complex and the given set of facts is large. We observe that for certain combinations of rules, there exist data structures that compactly represent the reasoning result and can be efficiently queried when necessary. In this paper, we present a general framework that allows for the integration of such optimised storage schemes with standard materialisation algorithms. Moreover, we devise optimised storage schemes targeting at transitive rules and union rules, two types of (combination of) rules that commonly occur in practice. Our experimental evaluation shows that our approach significantly improves memory consumption, sometimes by orders of magnitude, while remaining competitive in terms of query answering time.
Solving the swing-up and balance task for the Acrobot and Pendubot with SAC
Authors: Chi Zhang, Akhil Sathuluri, Markus Zimmermann
Date: 2023-12-18 00:00:00
Tasks: Acrobot, Position, reinforcement-learning, Reinforcement Learning (RL)

We present a solution of the swing-up and balance task for the pendubot and acrobot for the participation in the AI Olympics competition at IJCAI 2023. Our solution is based on the Soft Actor Crtic (SAC) reinforcement learning (RL) algorithm for training a policy for the swing-up and entering the region of attraction of a linear quadratic regulator(LQR) controller for stabilizing the double pendulum at the top position. Our controller achieves competitive scores in performance and robustness for both, pendubot and acrobot, problem scenarios.
Agent Assessment of Others Through the Lens of Self
Authors: Jasmine A. Berry
Date: 2023-12-18 00:00:00
Tasks: Computational Efficiency, Position

The maturation of cognition, from introspection to understanding others, has long been a hallmark of human development. This position paper posits that for AI systems to truly emulate or approach human-like interactions, especially within multifaceted environments populated with diverse agents, they must first achieve an in-depth and nuanced understanding of self. Drawing parallels with the human developmental trajectory from self-awareness to mentalizing (also called theory of mind), the paper argues that the quality of an autonomous agent's introspective capabilities of self are crucial in mirroring quality human-like understandings of other agents. While counterarguments emphasize practicality, computational efficiency, and ethical concerns, this position proposes a development approach, blending algorithmic considerations of self-referential processing. Ultimately, the vision set forth is not merely of machines that compute but of entities that introspect, empathize, and understand, harmonizing with the complex compositions of human cognition.
Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations
Authors: Zilin Wang, Haolin Zhuang, Lu Li, Yinmin Zhang, Junjie Zhong, Jun Chen, Yu Yang, Boshi Tang, Zhiyong Wu
Date: 2023-12-18 00:00:00

This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models. Current models often generate monotonous and simplistic dance sequences that misalign with human preferences because they lack exploration capabilities. The E3D2 framework involves a reward model trained from automatically-ranked dance demonstrations, which then guides the reinforcement learning process. This approach encourages the agent to explore and generate high quality and diverse dance movement sequences. The soundness of the reward model is both theoretically and experimentally validated. Empirical experiments demonstrate the effectiveness of E3D2 on the AIST++ dataset. Project Page: https://sites.google.com/view/e3d2.
Gacs-Korner Common Information Variational Autoencoder
Authors: Michael Kleinman, Alessandro Achille, Stefano Soatto, Jonathan Kao
Date: 2022-05-24 00:00:00
Proceeding: NeurIPS 2023 11

We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique to each. Our notion of common information is defined by an optimization problem over a family of functions and recovers the G\'acs-K\"orner common information as a special case. Importantly, our notion can be approximated empirically using samples from the underlying data distribution. We then provide a method to partition and quantify the common and unique information using a simple modification of a traditional variational auto-encoder. Empirically, we demonstrate that our formulation allows us to learn semantically meaningful common and unique factors of variation even on high-dimensional data such as images and videos. Moreover, on datasets where ground-truth latent factors are known, we show that we can accurately quantify the common information between the random variables.