Hedonic Coalition Formation for Task Allocation with Heterogeneous Robots

Tasks in the real world are complex in nature and often require multiple robots to collaborate in order to be accomplished. However, multiple robots with the same set of sensors working together might not be the optimal solution. In many cases a task might require different sensory inputs and outputs. However, allocating a large variety of sensors on each robot is not a cost-effective solution. As such, robots with different attributes must be considered. In this thesis we study the coalition formation problem for task allocation with multiple heterogeneous (equipped with a different set of sensors) robots. The proposed solution is implemented utilizing a Hedonic Coalition Formation strategy, rooted in game theory, coupled with bipartite graph matching. Our proposed algorithm aims to minimize the total cost of the formed coalitions and to maximize the matching between the required and the allocated types of robots to the tasks. Simulation results show that it produces near-optimal solutions (up to 94%) in a negligible amount of time (0:19 ms. with 100 robots and 10 tasks)

Context-Aware Sparse Deep Coordination Graphs

Learning sparse coordination graphs adaptive to the coordination dynamics among agents is a long-standing problem in cooperative multi-agent learning. This paper studies this problem and proposes a novel method using the variance of payoff functions to construct context-aware sparse coordination topologies. We theoretically consolidate our method by proving that the smaller the variance of payoff functions is, the less likely action selection will change after removing the corresponding edge. Moreover, we propose to learn action representations to effectively reduce the influence of payoff functions' estimation errors on graph construction. To empirically evaluate our method, we present the Multi-Agent COordination (MACO) benchmark by collecting classic coordination problems in the literature, increasing their difficulty, and classifying them into different types. We carry out a case study and experiments on the MACO and StarCraft II micromanagement benchmark to demonstrate the dynamics of sparse graph learning, the influence of graph sparseness, and the learning performance of our method

Modeling Scholar Profile in Expert Recommendation based on Multi-Layered Bibliographic Graph

A recommendation system requires the profile of researchers which called here as Scholar Profile for suggestions based on expertise. This dissertation contributes on modeling unbiased scholar profile for more objective expertise evidence that consider interest changes and less focused on citations. Interest changes lead to diverse topics and make the expertise levels on topics differ. Scholar profile is expected to capture expertise in terms of productivity aspect which often signified from the volume of publications and citations. We include researcher behavior in publishing articles to avoid misleading citation. Therefore, the expertise levels of researchers on topics is influenced by interest evolution, productivity, dynamicity, and behavior extracted from bibliographic data of published scholarly articles. As this dissertation output, the scholar profile model employed within a recommendation system for recommending productive researchers who provide academic guidance. The scholar profile is generated from multi layers of bibliographic data, such as layers of author, topic, and relations between those layers to represent academic social network. There is no predefined information of topics in a cold-start situation, such that procedures of topic mapping are necessary. Then, features of productivity, dynamicity and behavior of researchers within those layers are taken from some observed years to accommodate the behavior aspect. We experimented with AMiner dataset often used in the following bibliographic data related studies to empirically investigate: (a) topic mapping strategies to obtain interest of researchers, (b) feature extraction model for productivity, dynamicity, and behavior aspects based on the mapped topics, and (c) expertise rank that considers interest changes and less focused on citations from the scholar profile. Ensuring the validity results, our experiments worked on standard expert list of AMiner researchers. We selected Natural Language Processing and Information Extraction (NLP-IE) domains because of their familiarity and interrelated context to make it easier for introducing cases of interest changes. Using the mapped topics, we also made minor contributions on transformation procedures for visualizing researchers on maps of Scopus subjects and investigating the possibilities of conflict of interest

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data, underpinning various tasks including node classification and link prediction. However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes. This necessitates the emerging field of imbalanced learning on graphs, which aims to correct these data distribution skews for more accurate and representative learning outcomes. In this survey, we embark on a comprehensive review of the literature on imbalanced learning on graphs. We begin by providing a definitive understanding of the concept and related terminologies, establishing a strong foundational understanding for readers. Following this, we propose two comprehensive taxonomies: (1) the problem taxonomy, which describes the forms of imbalance we consider, the associated tasks, and potential solutions; (2) the technique taxonomy, which details key strategies for addressing these imbalances, and aids readers in their method selection process. Finally, we suggest prospective future directions for both problems and techniques within the sphere of imbalanced learning on graphs, fostering further innovation in this critical area.Comment: The collection of awesome literature on imbalanced learning on graphs: https://github.com/Xtra-Computing/Awesome-Literature-ILoG

Reinforcement Learning-based Optimization of Multiple Access in Wireless Networks

In this thesis, we study the problem of Multiple Access (MA) in wireless networks and design adaptive solutions based on Reinforcement Learning (RL). We analyze the importance of MA in the current communications scenery, where bandwidth-hungry applications emerge due to the co-evolution of technological progress and societal needs, and explain that improvements brought by new standards cannot overcome the problem of resource scarcity. We focus on resource-constrained networks, where devices have restricted hardware-capabilities, there is no centralized point of control and coordination is prohibited or limited. The protocols that we optimize follow a Random Access (RA) approach, where sensing the common medium prior to transmission is not possible. We begin with the study of time access and provide two reinforcement learning algorithms for optimizing Irregular Repetition Slotted ALOHA (IRSA), a state-of-the-art RA protocol. First, we focus on ensuring low complexity and propose a Q-learning variant where learners act independently and converge quickly. We, then, design an algorithm in the area of coordinated learning and focus on deriving convergence guarantees for learning while minimizing the complexity of coordination. We provide simulations that showcase how coordination can help achieve a fine balance, in terms of complexity and performance, between fully decentralized and centralized solutions. In addition to time access, we study channel access, a problem that has recently attracted significant attention in cognitive radio. We design learning algorithms in the framework of Multi-player Multi-armed Bandits (MMABs), both for static and dynamic settings, where devices arrive at different time steps. Our focus is on deriving theoretical guarantees and ensuring that performance scales well with the size of the network. Our works constitute an important step towards addressing the challenges that the properties of decentralization and partial observability, inherent in resource-constrained networks, pose for RL algorithms

Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems

Fault diagnosis of sensors, devices, and equipment is an important topic in the nuclear industry for effective and continuous operation of nuclear power plants. All the fault diagnostic approaches depend critically on the sensors that measure important process variables. Whenever a process encounters a fault, the effect of the fault is propagated to some or all the process variables. The ability of the sensor network to detect and isolate failure modes and anomalous conditions is crucial for the effectiveness of a fault detection and isolation (FDI) system. However, the emphasis of most fault diagnostic approaches found in the literature is primarily on the procedures for performing FDI using a given set of sensors. Little attention has been given to actual sensor allocation for achieving the efficient FDI performance. This dissertation presents a graph-based approach that serves as a solution for the optimization of sensor placement to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. This would potentially facilitate an automated sensor allocation procedure. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data, and to fit a hyper-plane to the data. The fault directions for different fault scenarios are obtained from the prediction errors, and fault isolation is then accomplished using new projections on these fault directions. The effectiveness of the use of an optimal sensor set versus a reduced set for fault detection and isolation is demonstrated using this technique. Among a variety of desalination technologies, the multi-stage flash (MSF) processes contribute substantially to the desalinating capacity in the world. In this dissertation, both steady-state and dynamic simulation models of a MSF desalination plant are developed. The dynamic MSF model is coupled with a previously developed International Reactor Innovative and Secure (IRIS) model in the SIMULINK environment. The developed sensor placement design and fault diagnostic methods are illustrated with application to the coupled nuclear desalination system. The results demonstrate the effectiveness of the newly developed integrated approach to performance monitoring and fault diagnosis with optimized sensor placement for large industrial systems