bluuebunny/crossref_metadata_2025 huggingface dataset:
Complexity of Fundamental Problems in Probabilistic Abstract Argumentation: Beyond Independence (Extended Abstract)
Authors: Fazzinga Bettina , Flesca Sergio , Furfaro Filippo
Published: July 2019

The complexity of the probabilistic counterparts of the verification and acceptance problems is investigated over probabilistic Abstract Argumentation Frameworks (prAAFs), in a setting more general
than the literature, where the complexity has been characterized only under independence between arguments/defeats.
The complexity of these problems is shown to depend on the semantics of the extensions, the way of encoding the prAAF, and the correlations between arguments/defeats.
In this regard, in order to study the impact of different correlations between arguments/defeats on the complexity, a new form of prAAF is introduced, called gen.
It is based on the well-known paradigm of world-set sets, and it allows the correlations to be easily distinguishable.
Shielded Base Contraction (Extended Abstract)
Authors: Garapa Marco , Fermé Eduardo , Reis Maurício D. L.
Published: July 2019

In this paper we study a kind of non-prioritized contraction operator on belief bases -known as shielded base contractions. We propose twenty different classes of shielded base contractions and obtain axiomatic characterizations for each one of them. Additionally we thoroughly investigate the interrelations (in the sense of inclusion) among all those classes.
Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Deep Reinforcement Learning Framework
Authors: Yang Yaodong , Hao Jianye , Zheng Yan , Yu Chao
Published: July 2019

Smart grids are contributing to the demand-side management by integrating electronic equipment, distributed energy generation and storage and advanced meters and controllers. With the increasing adoption of electric vehicles and distributed energy generation and storage systems, residential energy management is drawing more and more attention, which is regarded as being critical to demand-supply balancing and peak load reduction. In this paper, we focus on a microgrid scenario in which modern homes interact together under a large-scale setting to better optimize their electricity cost. We first make households form a group with an economic stimulus. Then we formulate the energy expense optimization problem of the household community as a multi-agent coordination problem and present an Entropy-Based Collective Multiagent Deep Reinforcement Learning (EB-C-MADRL) framework to address it. Experiments with various real-world data demonstrate that EB-C-MADRL can reduce both the long-term group power consumption cost and daily peak demand effectively compared with existing approaches.
Implicitly Coordinated Multi-Agent Path Finding under Destination Uncertainty: Success Guarantees and Computational Complexity (Extended Abstract)
Authors: Nebel Bernhard , Bolander Thomas , Engesser Thorsten , Mattmüller Robert
Published: July 2019

In multi-agent path finding, it is usually
assumed that planning is performed centrally and
that the destinations of the agents are common
knowledge. We will drop both assumptions and analyze under which conditions it can be guaranteed
that the agents reach their respective destinations
using implicitly coordinated plans without communication.
Teaching AI Agents Ethical Values Using Reinforcement Learning and Policy Orchestration
Authors: Noothigattu Ritesh , Bouneffouf Djallel , Mattei Nicholas , Chandra Rachita , Madan Piyush , Varshney Kush R. , Campbell Murray , Singh Moninder , Rossi Francesca
Published: July 2019

Autonomous cyber-physical agents play an increasingly large role in our lives. To ensure that they behave in ways aligned with the values of society, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society.
We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations and reinforcement learning to learn to maximize environmental rewards. A contextual bandit-based orchestrator then picks between the two policies: constraint-based and environment reward-based. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward-maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using Pac-Man and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.
On the Responsibility for Undecisiveness in Preferred and Stable Labellings in Abstract Argumentation (Extended Abstract)
Authors: Schulz Claudia , Toni Francesca
Published: July 2019

Different semantics of abstract Argumentation Frameworks (AFs) provide different levels of decisiveness for reasoning about the acceptability of conflicting arguments.The stable semantics is useful for applications requiring a high level of decisiveness, as it assigns to each argument the label "accepted" or the label "rejected". Unfortunately, stable labellings are not guaranteed to exist, thus raising the question as to which parts of AFs are responsible for the non-existence. In this paper, we address this question by investigating a more general question concerning preferred labellings (which may be less decisive than stable labellings but are always guaranteed to exist), namely why a given preferred labelling may not be stable and thus undecided on some arguments. In particular, (1) we give various characterisations of parts of an AF, based on the given preferred labelling, and (2) we show that these parts are indeed responsible for the undecisiveness if the preferred labelling is not stable. We then use these characterisations to explain the non-existence of stable labellings.
The Quest For "Always-On" Autonomous Mobile Robots
Authors: Biswas Joydeep
Published: July 2019

Building ``always-on'' robots to be deployed over extended periods of time in real human environments is challenging for several reasons.
Some fundamental questions that arise in the process include:
1) How can the robot reconcile unexpected differences between its observations and its outdated map of the world?
2) How can we scalably test robots for long-term autonomy?
3) Can a robot learn to predict its own failures, and their corresponding causes?
4) When the robot fails and is unable to recover autonomously, can it utilize partially specified, approximate human corrections to overcome its failures?
We summarize our research towards addressing all of these questions.
We present 1) Episodic non-Markov Localization to maintain the belief of the robot's location while explicitly reasoning about unmapped observations; 2) a 1,000km challenge to test for long-term autonomy; 3) feature-based and learning-based approaches to predicting failures; and 4) human-in-the-loop SLAM to overcome robot mapping errors, and SMT-based robot transition repair to overcome state machine failures.
Integrating Learning with Game Theory for Societal Challenges
Authors: Fang Fei
Published: July 2019

Real-world problems often involve more than one decision makers, each with their own goals or preferences. While game theory is an established paradigm for reasoning strategic interactions between multiple decision-makers, its applicability in practice is often limited by the intractability of computing equilibria in large games, and the fact that the game parameters are sometimes unknown and the players are often not perfectly rational. On the other hand, machine learning and reinforcement learning have led to huge successes in various domains and can be leveraged to overcome the limitations of the game-theoretic analysis. In this paper, we introduce our work on integrating learning with computational game theory for addressing societal challenges such as security and sustainability.
Multiagent Decision Making and Learning in Urban Environments
Authors: Kumar Akshat
Published: July 2019

Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents---from self-driving cars, ships to aerial drones---that promise to radically improve productivity and safety. Achieving coordination among agents in such urban settings presents several algorithmic challenges---ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment. In addition, accurate domain models need to be learned from data that is often noisy and available only at an aggregate level. In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in large-scale urban multiagent systems.
From Data to Knowledge Engineering for Cybersecurity
Authors: Simari Gerardo I.
Published: July 2019

Data present in a wide array of platforms that are part of today's information systems lies at the foundation of many decision making processes, as we have now come to depend on social media, videos, news, forums, chats, ads, maps, and many other data sources for our daily lives. In this article, we first discuss how such data sources are involved in threats to systems' integrity, and then how they can be leveraged along with knowledge-based tools to tackle a set of challenges in the cybersecurity domain. Finally, we present a brief discussion of our roadmap for research and development in the near future to address the set of ever-evolving cyber threats that our systems face every day.
AI Planning for Enterprise: Putting Theory Into Practice
Authors: Sohrabi Shirin
Published: July 2019

In this paper, I overview a number of AI Planning applications for Enterprise and discuss a number of challenges in applying AI Planning in that setting. I will also summarize the progress made to date in addressing these challenges.
Domain-Dependent and Domain-Independent Problem Solving Techniques
Authors: Stern Roni
Published: July 2019

Heuristic search is a general problem-solving method. Some heuristic search algorithms, like the well-known A* algorithm, are domain-independent, in the sense that their knowledge of the problem at-hand is limited to the (1) initial state, (2) state transition operators and their costs, (3) goal-test function,
and (4) black-box heuristic function that estimates the value of a state.
Prominent examples are A* and Weighted A*.
Other heuristic search algorithms are domain-dependent, that is, customized to solve problems from a specific domain.
A well-known example is conflict-directed A*, which is specifically designed to solve model-based diagnosis problems.
In this paper, we review our recent advancements in both domain-independent and domain-dependent heuristic search, and outline several challenging open questions.
What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature
Authors: Wallace Byron C.
Published: July 2019

Ideally decisions regarding medical treatments would be informed by the totality of the available evidence. The best evidence we currently have is in published natural language articles describing the conduct and results of clinical trials. Because these are unstructured, it is difficult for domain experts (e.g., physicians) to sort through and appraise the evidence pertaining to a given clinical question. Natural language technologies have the potential to improve access to the evidence via semi-automated processing of the biomedical literature. In this brief paper I highlight work on developing tasks, corpora, and models to support semi-automated evidence retrieval and extraction. The aim is to design models that can consume articles describing clinical trials and automatically extract from these key clinical variables and findings, and estimate their reliability. Completely automating `machine reading' of evidence remains a distant aim given current technologies; the more immediate hope is to use such technologies to help domain experts access and make sense of unstructured biomedical evidence more efficiently, with the ultimate aim of improving patient care. Aside from their practical importance, these tasks pose core NLP challenges that directly motivate methodological innovation.
Strategyproof and Approximately Maxmin Fair Share Allocation of Chores
Authors: Aziz Haris , Li Bo , Wu Xiaowei
Published: July 2019

We initiate the work on fair and strategyproof allocation of indivisible chores. The fairness concept we consider in this paper is maxmin share (MMS) fairness. We consider three previously studied models of information elicited from the agents: the ordinal model, the cardinal model, and the public ranking model in which the ordinal preferences are publicly known. We present both positive and negative results on the level of MMS approximation that can be guaranteed if we require the algorithm to be strategyproof. Our results uncover some interesting contrasts between the approximation ratios achieved for chores versus goods.
Complexity of Manipulating and Controlling Approval-Based Multiwinner Voting
Authors: Yang Yongjie
Published: July 2019

We study the complexity of several manipulation and control problems for six prevalent approval based multiwinner voting rules. We show that these rules generally resist the proposed strategic types. In addition, we also give fixed-parameter tractability results for these problems with respect to several natural parameters and derive polynomial-time algorithms for certain special cases.
Conditional Preference Network with Constraints and Uncertainty
Authors: Ahmed Sultan
Published: July 2019

In multi-attribute preference-based reasoning, the CP-net is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. This paper outlines three aspects of the CP-net. First, when a CP-net is involved with a set of hard constraints, solving the Constrained CP-net requires dominance testing which is a very expensive operation. We tackle this problem by extending the CP-net model such that dominance testing is not needed. Second, user's choices involve habitual behavior and genuine decision. The former is represented using preferences, while we introduce the notion of comfort to represent the latter. Then, we suggest an extension of the CP-net which can represent both preference and comfort. Third, preferences often come with noise and uncertainty. In this regard, we suggest the probabilistic extension of the Tradeoff-enhanced CP-net (TCP-net) model. The necessary semantics and usefulness of the extensions above are described. Finally, we outline some in-progress and future work.
Constraint Solving and Optimization Using Evolutionary Techniques
Authors: Bidar Mahdi , Mouhoub Malek
Published: July 2019

Constraint Solving and Optimization is very relevant in many real world applications including scheduling, planning, configuration, resource allocation and timetabling. Solving a constraint optimization problem consists of finding an assignment of values to variables that optimizes some defined objective functions, subject to a set of constraints imposed on the problem variables. Due to their high dimensional and exponential search spaces, classical methods are unpractical to tackle these problems. An appropriate alternative is to rely on metaheuristics. My thesis is concerned with investigating the applicability of the evolutionary algorithms when dealing with constraint optimization problems. In this regard, we propose two new optimization algorithms namely Mushroom Reproduction Optimization algorithm (MRO) and Focus Group Optimization algorithm (FGO) for solving such problems.
Visionary Security: Using Uncertain Real-Time Information in Signaling Games
Authors: Bondi Elizabeth
Published: July 2019

In important domains from natural resource conservation to public safety, real-time information is becoming increasingly important. Strategic deployment of security cameras and mobile sensors such as drones can provide real-time updates on illegal activities. To help plan for such strategic deployments of sensors and human patrollers, as well as warning signals to ward off adversaries, the defender-attacker security games framework can be used. Previous works do not consider the combined situation of uncertainty in real-time information in addition to strategically signaling to adversaries. In this thesis, we will not only address this gap, but also improve the overall security result by considering security game models and computer vision algorithms together.
AI in Recruiting. Multi-agent Systems Architecture for Ethical and Legal Auditing
Authors: Fernández Martínez María del Carmen , Fernández Alberto
Published: July 2019

Artificial Intelligence (AI) domain-specific applications may have different ethical and legal implications. One of the current questions of AI is the challenges behind the analysis of job video-interviews. There are pros and cons to using AI in recruitment processes, and potential consequences for candidates, companies and states. Furthermore, the deficit of regulation of these systems reinforces the need for external and neutral auditing of the types of analysis made in interviews. We, therefore, propose a Multi-agent system architecture for neutral auditing to guarantee an inclusive and accurate AI and to reduce the potential discrimination in the job market.
A Similarity Measurement Method Based on Graph Kernel for Disconnected Graphs
Authors: Gao Jun , Gao Jianliang
Published: July 2019

Disconnected graphs are very common in the real world. However, most existing methods for graph similarity focus on connected graph. In this paper, we propose an effective approach for measuring the similarity of disconnected graphs. By embedding connected subgraphs with graph kernel, we obtain the feature vectors in low dimensional space. Then, we match the subgraphs and weigh the similarity of matched subgraphs. Finally, an intuitive example shows the feasibility of the method.
Finding Justifications by Approximating Core for Large-scale Ontologies
Authors: Gao Mengyu , Ye Yuxin , Ouyang Dantong , Wang Bin
Published: July 2019

Finding justifications for an entailment is one of the major missions in the field of ontology research. Recent advances on finding justifications w.r.t. the light-weight description logics focused on encoding this problem into a propositional formula, and using SAT-based techniques to enumerate all MUSes (minimally unsatisfiable subformulas). It's necessary to import more optimized techniques into finding justifications as emergence of large-scale real-world ontologies. In this paper, we propose a new strategy which introduce local search(in short, LS) technique to compute the approximating core before extracting an exact MUS. Although it is based on a heuristic and LS, such technique is complete in the sense that it always delivers a MUS for any unsatisfiable SAT instance. Our method will find the justifications for large-scale ontologies more effectively.
Teaching Robots to Interact with Humans in a Smart Environment
Authors: Goel Shivam
Published: July 2019

Robotics in healthcare has recently emerged, backed by the recent advances in the field of machine learning and robotics. Researchers are focusing on training robots for interacting with elderly adults. This research primarily focuses on engineering more efficient robots that can learn from their mistakes, thereby aiding in better human-robot interaction. In this work, we propose a method in which a robot learns to navigate itself to the individual in need. The robotic agents' learning algorithm will be capable of navigating in an unknown environment. The robot's primary objective is to locate human in a house, and upon finding the human, the goal is to interact with them while complementing their pose and gaze. We propose an end to end learning strategy, which uses a recurrent neural network architecture in combination with Q-learning to train an optimal policy. The idea can be a contribution to better human-robot interaction.
Vision beyond Pixels: Visual Reasoning via Blocksworld Abstractions
Authors: Gokhale Tejas
Published: July 2019

Deep neural networks trained in an end-to-end fashion have brought about exceptional advances in computer vision, especially in computational perception. We go beyond perception and seek to enable vision modules to reason about perceived visual entities such as scenes, objects and actions. We introduce a challenging visual reasoning task, Image-Based Event Sequencing (IES) and compile the first IES dataset, Blocksworld Image Reasoning Dataset (BIRD). Motivated by the blocksworld concept, we propose a modular approach supported by literature in cognitive psychology and children's development. We decompose the problem into two stages - visual perception and event sequencing, and show that our approach can be extended to natural images without re-training.
A Unified Mathematical Approach for Foraging and Construction Systems in a 1,000,000 Robot Swarm
Authors: Harwell John
Published: July 2019

Automation in construction is possible with systems designed using the swarm robotic principles
of scalability, flexibility, robustness, and emergence. We derive quantitative measurements of
these principles in 10,000 robot swarms as a first
step in achieving this goal. We summarize our recent task allocation work in the context of an object gathering task and demonstrate its feasibility
in the context of automated construction tasks. We
present a trajectory to extend our current task allocation methodology using stochastic processes in
order to present a unified approach to task allocation in swarm-robotic construction
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?
Authors: Hocquette Céline
Published: July 2019

World-class human players have been outperformed in a number of complex two person games such as Go by Deep Reinforcement Learning systems GO. However, several drawbacks can be identified for these systems: 1) The data efficiency is unclear given they appear to require far more training games to achieve such performance than any human player might experience in a lifetime. 2) These systems are not easily interpretable as they provide limited explanation about how decisions are made. 3) These systems do not provide transferability of the learned strategies to other games. We study in this work how an explicit logical representation can overcome these limitations and introduce a new logical system called MIGO designed for learning two player game optimal strategies. It benefits from a strong inductive bias which provides the capability to learn efficiently from a few examples of games played. Additionally, MIGO's learned rules are relatively easy to comprehend, and are demonstrated to achieve significant transfer learning.
On the Tree Representations of Dichotomous Preferences
Authors: Yang Yongjie
Published: July 2019

We study numerous restricted domains of dichotomous preferences with respect to some tree structures. Particularly, we study the relationships among these domains and the ones proposed by Elkind and Lackner [2015]. We also show that recognizing all the restricted domains proposed in this paper is polynomial-time solvable. Finally, we explore the complexity of winner determination for several important approval-based multiwinner voting rules when restricted to these domains.
Unsupervised Multi-view Learning
Authors: Huang Ling
Published: July 2019

Unsupervised multi-view learning is a hot research topic. The main challenge lies in how to integrate information from different views to enhance the unsupervised learning performance. In this paper, we present our research works on multi-view data clustering and multi-view network community detection respectively. The main contributions are summarized by emphasizing the challenges we have addressed. In addition, the ongoing work and the future work are briefly presented.
Intelligent Agent for Assessing and Guiding Rehabilitation Exercises
Authors: Lee Min Hun
Published: July 2019

Individualized rehabilitation sessions with a therapists are important to improve physical ability of a patient with neurological and musculoskeletal disorders. However, in-home rehabilitation regimens are often prescribed due to therapist’s limited availability. During in-home rehabilitation, patients might become confused whether they correctly follow rehabilitation regimens. To address this problem, this research aims to develop a Socially Assistive Robot that can interactively accommodate expert’s domain knowledge into a data-driven model to assess exercise performance and guide desirable joint positions for improvement.
Optimizing Interactive Systems with Data-Driven Objectives
Authors: Li Ziming
Published: July 2019

Effective optimization is essential for interactive systems to provide a satisfactory user experience. However, it is often challenging to find an objective to optimize for. Generally, such objectives are manually crafted and rarely capture complex user needs in an accurate manner. We propose to infer the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. It is promising if we model the objectives directly from the user interactions which we use to optimize interactive systems, which will improve user experience and dynamically reacts to user actions.
Split Q Learning: Reinforcement Learning with Two-Stream Rewards
Authors: Lin Baihan , Bouneffouf Djallel , Cecchi Guillermo
Published: July 2019

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.
Event Prediction in Complex Social Graphs using One-Dimensional Convolutional Neural Network
Authors: Molokwu Bonaventure
Published: July 2019

Social network graphs possess apparent and latent knowledge about their respective actors and links which may be exploited, using effective and efficient techniques, for predicting events within the social graphs. Understanding the intrinsic relationship patterns among spatial social actors and their respective properties are crucial factors to be taken into consideration in event prediction within social networks. My research work proposes a unique approach for predicting events in social networks by learning the context of each actor/vertex using neighboring actors in a given social graph with the goal of generating vector-space embeddings for each vertex. Our methodology introduces a pre-convolution layer which is essentially a set of feature-extraction operations aimed at reducing the graph's dimensionality to aid knowledge extraction from its complex structure. Consequently, the low-dimensional node embeddings are introduced as input features to a one-dimensional ConvNet model for event prediction about the given social graph. Training and evaluation of this proposed approach have been done on datasets (compiled: November, 2017) extracted from real world social networks with respect to 3 European countries. Each dataset comprises an average of 280,000 links and 48,000 actors.
Towards Architecture-Agnostic Neural Transfer: a Knowledge-Enhanced Approach
Authors: Quinn Seán , Mileo Alessandra
Published: July 2019

The ability to enhance deep representations with prior knowledge is receiving a lot of attention from the AI community as a key enabler to improve the way modern Artificial Neural Networks (ANN) learn. In this paper we introduce our approach to this task, which comprises of a knowledge extraction algorithm, a knowledge injection algorithm and a common intermediate knowledge representation as an alternative to traditional neural transfer. As a result of this research, we envisage a knowledge-enhanced ANN, which will be able to learn, characterise and reuse knowledge extracted from the learning process, thus enabling more robust architecture-agnostic neural transfer, greater explainability and further integration of neural and symbolic approaches to learning.
Technical, Hard and Explainable Question Answering (THE-QA)
Authors: Sampat Shailaja
Published: July 2019

The ability of an agent to rationally answer questions about a given task is the key measure of its intelligence. While we have obtained phenomenal performance over various language and vision tasks separately, 'Technical, Hard and Explainable Question Answering' (THE-QA) is a new challenging corpus which addresses them jointly. THE-QA is a question answering task involving diagram understanding and reading comprehension. We plan to establish benchmarks over this new corpus using deep learning models guided by knowledge representation methods. The proposed approach will envisage detailed semantic parsing of technical figures and text, which is robust against diverse formats. It will be aided by knowledge acquisition and reasoning module that categorizes different knowledge types, identify sources to acquire that knowledge and perform reasoning to answer the questions correctly. THE-QA data will present a strong challenge to the community for future research and will bridge the gap between state-of-the-art Artificial Intelligence (AI) and 'Human-level' AI.
Preference Elicitation and Explanation in Iterative Planning
Authors: Sanneman Lindsay
Published: July 2019

Planning for complex scenarios, particularly in which large teams of humans with distributed expertise and varying preferences share a set of resources, poses a number of challenges including integrating distributed information and accounting for context-dependent preferences and constraints. We see three key pieces to solving the problem of introducing autonomous assistance through a mixed-initiative planning system in these scenarios: preference elicitation, integrating preferences into planning, and providing tailored explanations back to the humans in the loop. The process of preference elicitation, planning, and explanation can be integrated as an iterative process by which teams can efficiently converge on the ideal schedule. Linear Temporal Logic (LTL) is a common language, readily understandable by both planners and humans, that provides a natural link between the three components of the iterative planning problem, facilitating both elicitation of expressive preferences and intelligible explanations of the system's decision-making processes. Outputs of each of the preference elicitation, planning, and explanation pieces can be expressed as LTL specifications and used as inputs to each next step in the process. We propose to explore preference elicitation, planning, and explanation using LTL specifications and the integration of these pieces into an iterative process.
Intelligent Querying in Camera Networks for Efficient Target Tracking
Authors: Sharma Anil
Published: July 2019

Visual analytics applications often rely on target tracking across a network of cameras for inference and prediction. A network of cameras generates immense amount of video data and processing it for tracking a target is highly computationally expensive. Related works typically use data association and visual re-identification techniques to match target templates across multiple cameras. In this thesis, I propose to formulate this scheduling problem as a Markov Decision Process (MDP) and present a reinforcement learning based solution to schedule cameras by selecting one where the target is most likely to appear next. The proposed approach can be learned directly from data and doesn't require any information of the camera network topology. NLPR MCT and DukeMTMC datasets are used to show that the proposed policy significantly reduces the number of frames to be processed for tracking and identifies the camera schedule with high accuracy as compared to the related approaches. Finally, I will be formulating an end-to-end pipeline for target tracking that will learn a policy to find the camera schedule and to track the target in the individual camera frames of the schedule.
Safe and Sample-Efficient Reinforcement Learning Algorithms for Factored Environments
Authors: Simão Thiago D.
Published: July 2019

Reinforcement Learning (RL) deals with problems that can be modeled as a Markov Decision Process (MDP) where the transition function is unknown. In situations where an arbitrary policy pi is already in execution and the experiences with the environment were recorded in a batch D, an RL algorithm can use D to compute a new policy pi'. However, the policy computed by traditional RL algorithms might have worse performance compared to pi. Our goal is to develop safe RL algorithms, where the agent has a high confidence that the performance of pi' is better than the performance of pi given D. To develop sample-efficient and safe RL algorithms we combine ideas from exploration strategies in RL with a safe policy improvement method.
The Price of Governance: A Middle Ground Solution to Coordination in Organizational Control
Authors: Yu Chao , Tan Guozhen
Published: July 2019

Achieving coordination is crucial in organizational control. This paper investigates a middle ground solution between decentralized interactions and centralized administrations for coordinating agents beyond inefficient behavior. We first propose the price of governance (PoG) to evaluate how such a middle ground solution performs in terms of effectiveness and cost. We then propose a hierarchical supervision framework to explicitly model the PoG, and define step by step how to realize the core principle of the framework and compute the optimal PoG for a control problem. Two illustrative case studies are carried out to exemplify the applications of the proposed framework and its methodology. Results show that the hierarchical supervision framework is capable of promoting coordination among agents while bounding administrative cost to a minimum in different kinds of organizational control problems.
Deep Learning with Relational Logic Representations
Authors: Sourek Gustav
Published: July 2019

Despite their significant success, all the existing deep neural architectures based on static computational graphs processing fixed tensor representations necessarily face fundamental limitations when presented with dynamically sized and structured data. Examples of these are sparse multi-relational structures present everywhere from biological networks and complex knowledge hyper-graphs to logical theories. Likewise, given the cryptic nature of generalization and representation learning in neural networks, potential integration with the sheer amounts of existing symbolic abstractions present in human knowledge remains highly problematic. Here, we argue that these abilities, naturally present in symbolic approaches based on the expressive power of relational logic, are necessary to be adopted for further progress of neural networks, and present a well founded learning framework for integration of deep and symbolic approaches based on the lifted modelling paradigm.
Matching with Constraints
Authors: Sun Zhaohong
Published: July 2019

In recent years, a number of new challenges have been observed in the application of matching theory. One of the most pressing problems concerns how to allocate refugees to hosts safely and in a timely manner. Currently, this placement is implemented on an ad hoc basis where the preferences of both refugees and hosts are not taken into account. Another important realization is that real-life matching markets are often subject to various distributional constraints. For example, there has been increased attention to school choice models that take account of affirmative action and diversity concerns. The objective of this research is to design efficient algorithms while satisfying desirable properties for these new emerging problems.
Cautious Rule-Based Collective Inference
Authors: Svatos Martin
Published: July 2019

Collective inference is a popular approach for solving tasks as knowledge graph completion within the statistical relational learning field. There are many existing solutions for this task, however, each of them is subjected to some limitation, either by restriction to only some learning settings, lacking interpretability of the model or theoretical test error bounds. We propose an approach based on cautious inference process which uses first-order rules and provides PAC-style bounds.
The Design of Human Oversight in Autonomous Weapon Systems
Authors: Verdiesen Ilse
Published: July 2019

Autonomous Weapon Systems (AWS) can be defined as weapons systems equipped with Artificial Intelligence (AI). They are an emerging technology and are increasingly deployed on the battlefield. In the societal debate on Autonomous Weapon Systems, the concept of Meaningful Human Control (MHC) is often mentioned as requirement, but MHC will not suffice as requirement to minimize unintended consequences of Autonomous Weapon Systems, because the definition of ‘control’ implies that one has the power to influence or direct the course of events or the ability to
manage a machine. The characteristics autonomy, interactivity and adaptability of AI  in Autonomous Weapon Systems inherently imply that control in strict sense is not possible. Therefore, a different approach is needed to minimize unintended consequences of AWS. Several scholars are describing the concept of Human Oversight in Autonomous Weapon Systems and AI in general. Just recently Taddeo and Floridi (2018) describe that human oversight procedures are necessary to minimize unintended consequences and to compensate unfair impacts of AI. In my PhD project, I will analyse the concepts that are needed to define, model, evaluate and ensure human oversight in Autonomous Weapons and design a technical architecture to implement this.
A Compliance Checking Framework for DNN Models
Authors: Verma Sunny , Wang Chen , Zhu Liming , Liu Wei
Published: July 2019

Growing awareness towards ethical use of machine learning (ML) models has created a surge for the development of fair models. Existing work in this regard assumes the presence of sensitive attributes in the data and hence can build classifiers whose decisions remain agnostic to such attributes. However, in the real world settings, the end-user of the ML model is unaware of the training data; besides, building custom models is not always feasible. Moreover, utilizing a pre-trained model with high accuracy on certain dataset can not be assumed to be fair. Unknown biases in the training data are the true culprit for unfair models (i.e., disparate performance for groups in the dataset). In this preliminary research, we propose a different lens for building fair models by enabling the user with tools to discover blind spots and biases in a pre-trained model and augment them with corrective measures.
Adversarial Machine Learning with Double Oracle
Authors: Wang Kai
Published: July 2019

We aim to improve the general adversarial machine learning solution by introducing the double oracle idea from game theory, which is commonly used to solve a sequential zero-sum game, where the adversarial machine learning problem can be formulated as a zero-sum minimax problem between learner and attacker.
AI at the Margins: Data, Decisions, and Inclusive Social Impact
Authors: Wilder Bryan
Published: July 2019

Artificial intelligence holds tremendous promise to improve human well-being. However, AI techniques are typically developed for the benefit of those with access to technological and financial resources. A critical but understudied question is how AI can benefit marginalized communities who lack such resources. Governments and communities worldwide use a range of interventions to tackle social problems such as homelessness and disease, improving access to opportunity for underserved populations. My research develops machine learning and optimization methods to empower such interventions, which are almost always deployed with limited resources and limited information. Maximizing impact in this context requires algorithmic approaches which span the full pipeline from data to decisions. My dissertation presents a set of both technical and application-oriented contributions towards this goal.
Self-Organizing Incremental Neural Networks for Continual Learning
Authors: Wiwatcharakoses Chayut , Berrar Daniel
Published: July 2019

Continual learning systems can adapt to new tasks, changes in data distributions, and new information that becomes incrementally available over time. The key challenge for such systems is how to mitigate catastrophic forgetting, i.e., how to prevent the loss of previously learned knowledge when new tasks need to be solved. In our research, we investigate self-organizing incremental neural networks (SOINN) for continual learning from both stationary and non-stationary data. We have developed a new algorithm, SOINN+, that learns to forget irrelevant nodes and edges and is robust to noise.
Evolutionary Learning of Existential Rules
Authors: Wu Lianlong
Published: July 2019

Declarative rules such as Prolog and Datalog are common formalisms to express expert knowledge and are used in a number of systems. Since developing such rules is time-consuming and requires scarce expert knowledge, it is essential to develop algorithms for learning such rules. We address the problem of learning existential rules, a richer class of rules which found applications in many use-cases such as Semantic Web and Web Data Extraction. In particular, we concentrate on developing evolutionary learning algorithms for rule learning.
Entity Alignment for Cross-lingual Knowledge Graph with Graph Convolutional Networks
Authors: Xiong Fan , Gao Jianliang
Published: July 2019

Graph convolutional network (GCN) is a promising approach that has recently been used to resolve knowledge graph alignment. In this paper, we propose a new method to entity alignment for cross-lingual knowledge graph. In the method, we design a scheme of attribute embedding for GCN training. Furthermore, GCN model utilizes the attribute embedding and structure embedding to abstract graph features simultaneously. Our preliminary experiments show that the proposed method outperforms the state-of-the-art GCN-based method.
Decentralized Optimization with Edge Sampling
Authors: Zhang Chi , Li Qianxiao , Zhao Peilin
Published: July 2019

In this paper, we propose a decentralized distributed algorithm with stochastic communication among nodes, building on a sampling method called "edge sampling''. Such a sampling algorithm allows us to avoid the heavy peer-to-peer communication cost when combining neighboring weights on dense networks while still maintains a comparable convergence rate. In particular, we quantitatively analyze its theoretical convergence properties, as well as the optimal sampling rate over the underlying network. When compared with previous methods, our solution is shown to be unbiased, communication-efficient and suffers from lower sampling variances. These theoretical findings are validated by both numerical experiments on the mixing rates of Markov Chains and distributed machine learning problems.
Personal Context Recognition via Skeptical Learning
Authors: Zhang Wanyi
Published: July 2019

In personal context recognition many solutions rely on supervised learning that uses sensor data collected from the users' mobile devices. However, the recognition performance is significantly affected by the annotations’ quality. The problem lies in the fact that the annotator in such scenarios is usually the user herself which is not an expert and thus provides a significant amount of incorrect labels, while existing solutions can only tolerate a small fraction of mislabels. Our solution is Skeptical Learning, a framework for interactive machine learning where the machine uses all its available knowledge to check the correctness of its own and the user labeling. This allows to have a uniform confidence measure to be used when a contradiction arises that applies to both the annotator and the machine. The criteria of success is an improvement of the final recognition accuracy with respect to traditional supervised approaches.
OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models
Authors: Arias Manuel , Pérez-Martín Jorge , Luque Manuel , Díez Francisco J.
Published: July 2019

OpenMarkov is a Java open-source tool for creating and evaluating probabilistic graphical models, including Bayesian networks, influence diagrams, and some Markov models. With more than 100,000 lines of code, it offers some features for interactive learning, explanation of reasoning, and cost-effectiveness analysis, which are not available in any other tool. OpenMarkov has been used at universities, research centers, and large companies in more than 30 countries on four continents. Several models, some of them for real-world medical applications, built with OpenMarkov, are publicly available on Internet.
Explainable Deep Neural Networks for Multivariate Time Series Predictions
Authors: Assaf Roy , Schumann Anika
Published: July 2019

We demonstrate that CNN deep neural networks can not only be used for making predictions based on multivariate time series data, but also for explaining these predictions.
This is important for a number of applications where predictions are the basis for decisions and actions. Hence, confidence in the prediction result is crucial.
We design a two stage convolutional neural network architecture which uses particular kernel sizes.
This allows us to utilise gradient based techniques for generating saliency maps for both the time dimension and the features.
These are then used for explaining which features during which time interval are responsible for a given prediction, as well as explaining during which time intervals was the joint contribution of all features most important for that prediction.
We demonstrate our approach for predicting the average energy production of photovoltaic power plants and for explaining these predictions.
Multi-Agent Path Finding on Ozobots
Authors: Barták Roman , Krasičenko Ivan , Švancara Jiří
Published: July 2019

Multi-agent path finding (MAPF) is the problem to find collision-free paths for a set of agents (mobile robots) moving on a graph. There exists several abstract models describing the problem with various types of constraints. The demo presents software to evaluate the abstract models when the plans are executed on Ozobots, small mobile robots developed for teaching programming. The software allows users to design the grid-like maps, to specify initial and goal locations of robots, to generate plans using various abstract models implemented in the Picat programming language, to simulate and to visualise execution of these plans, and to translate the plans to command sequences for Ozobots.
Hintikka's World: Scalable Higher-order Knowledge
Authors: Charrier Tristan , Gamblin Sébastien , Niveau Alexandre , Schwarzentruber François
Published: July 2019

Hintikka's World is a graphical and pedagogical tool that shows how artificial agents can reason about higher-order knowledge. In this demonstration paper, we present the implementation of symbolic models in Hintikka's World. They enable the tool to scale, by helping it to face the state explosion, which makes it possible to provide examples featuring real card games, such as Hanabi.