GAT-GAN : A Graph-Attention-based Time-Series Generative Adversarial Network

Generative Adversarial Networks (GANs) have proven to be a powerful tool for generating realistic synthetic data. However, traditional GANs often struggle to capture complex relationships between features which results in generation of unrealistic multivariate time-series data. In this paper, we propose a Graph-Attention-based Generative Adversarial Network (GAT-GAN) that explicitly includes two graph-attention layers, one that learns temporal dependencies while the other captures spatial relationships. Unlike RNN-based GANs that struggle with modeling long sequences of data points, GAT-GAN generates long time-series data of high fidelity using an adversarially trained autoencoder architecture. Our empirical evaluations, using a variety of real-time-series datasets, show that our framework consistently outperforms state-of-the-art benchmarks based on \emph{Frechet Transformer distance} and \emph{Predictive score}, that characterizes (\emph{Fidelity, Diversity}) and \emph{predictive performance} respectively. Moreover, we introduce a Frechet Inception distance-like (FID) metric for time-series data called Frechet Transformer distance (FTD) score (lower is better), to evaluate the quality and variety of generated data. We also found that low FTD scores correspond to the best-performing downstream predictive experiments. Hence, FTD scores can be used as a standardized metric to evaluate synthetic time-series data.Comment: 9 pages, 1 figure, 3 tables, preprint under revie

Adaptive computer‐generated forces for simulator‐based training, Expert Systems with Applications

Simulator-based training is in constant pursuit of increasing level of realism. The transition from doctrine-driven computer-generated forces (CGF) to adaptive CGF represents one such effort. The use of doctrine-driven CGF is fraught with challenges such as modeling of complex expert knowledge and adapting to the trainees’ progress in real time. Therefore, this paper reports on how the use of adaptive CGF can overcome these challenges. Using a self-organizing neural network to implement the adaptive CGF, air combat maneuvering strategies are learned incrementally and generalized in real time. The state space and action space are extracted from the same hierarchical doctrine used by the rule-based CGF. In addition, this hierarchical doctrine is used to bootstrap the self-organizing neural network to improve learning efficiency and reduce model complexity. Two case studies are conducted. The first case study shows how adaptive CGF can converge to the effective air combat maneuvers against rule-based CGF. The subsequent case study replaces the rule-based CGF with human pilots as the opponent to the adaptive CGF. The results from these two case studies show how positive outcome from learning against rule-based CGF can differ markedly from learning against human subjects for the same tasks. With a better understanding of the existing constraints, an adaptive CGF that performs well against rule-based CGF and human subjects can be designed

Predicting indoor crowd density using column-structured deep neural network

National Research Foundation (NRF) Singapore under Corp Lab @ University scheme; Fujitsu Lt

Managing egress of crowd during infrastructure disruption

National Research Foundation (NRF) Singapore under Corp Lab @ University schem

Cognitive information systems for context-aware decision support

Although advancements in technology has allowed a large amount of data to be collected and stored, the task of turning this torrent of raw data into useful information for real time decision making is constantly exceeding our cognitive capacity. While modern Decision Support Systems (DSS) have started to adopt certain aspects of human cognition, such as Situation-Awareness (SA) and Context-Awareness (CA), there is an urgent need for a new breed of advanced information systems that incorporates a road range of cognitive capabilities, including awareness, pro-activeness, reasoning and learning. To address the above challenge, this thesis proposes a framework of cognitive information systems that integrates SA and a multi-agent based inference engine for Context-Aware Decision Support (CaDS). By modeling the situational and contextual factors in the environment explicitly, the system is designed to reduce the cognitive load of the users by providing a combination of functions, including event classification, action recommendation and proactive decision making. To enable learning capability, a self-organizing neural network known as the Fusion Architecture for Learning and Cognition (FALCON) is embedded into the CaDS framework. FALCON has the inherent ability to remain stable as it learns incrementally in real time. This is needed within the CaDS framework to continuously improve the prediction accuracy of the system. Experimental results are reported using a simulated Command and Control (C2) problem domain to illustrate how the CaDS framework is able to reduce the cognitive load of the users and improve the prediction accuracies for option generation. For tapping a variety of knowledge, this thesis presents a systematic procedure for integrating domain knowledge with Reinforcement Learning (RL) using FALCON. To exploit the inserted domain knowledge and the learned knowledge that are inherently distinct, the greedy exploitation and reward vigilance adaptation strategies are proposed to achieve maximal exploitation of the domain knowledge while retaining the flexibility of exploring new knowledge. Our experimental results based on a 1-v-1 PE problem domain have reported improvement to the efficiency of RL using this approach.Doctor of Philosophy (SCE