DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explainability in the field of visual analytics

Visualizations for an Explainable Planning Agent

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator (appeared in AAAI 2017 Fall Symposium on Human-Agent Groups

Let’s augment the future together!:Augmented reality troubleshooting support for IT/OT rolling stock failures

The railway industry is moving to a socio-technological system that relies on computer-controlled and human-machine interfaces. Opportunities arise for creating new services and commercial business cases by using technological innovations and traffic management systems. The convergence of Information Technology (IT) with Operational Technology (OT) is critical for cost-effective and reliable railway operations. However, this convergence introduces complexities, leading to more intricate rolling stock system failures. Hence, operators necessitate assistance in their troubleshooting and maintenance strategy to simplify the decision-making and action-taking processes. Augmented Reality (AR) emerges as a pivotal tool for troubleshooting within this context. AR enhances the operator’s ability to visualize, contextualize, and understand complex data by overlaying real-time and virtual information onto physical objects. AR supports the identification of IT/OT rolling stock system failures, offers troubleshooting directions, and streamlines maintenance procedures, ultimately enhancing decision-making and action-taking processes. This thesis investigates how AR can support operators in navigating troubleshooting and maintenance challenges posed by IT/OT rolling stock system failures in the railway industry

RT-MOVICAB-IDS: Addressing real-time intrusion detection

This study presents a novel Hybrid Intelligent Intrusion Detection System (IDS) known as RT-MOVICAB-IDS that incorporates temporal control. One of its main goals is to facilitate real-time Intrusion Detection, as accurate and swift responses are crucial in this field, especially if automatic abortion mechanisms are running. The formulation of this hybrid IDS combines Artificial Neural Networks (ANN) and Case-Based Reasoning (CBR) within a Multi-Agent System (MAS) to detect intrusions in dynamic computer networks. Temporal restrictions are imposed on this IDS, in order to perform real/execution time processing and assure system response predictability. Therefore, a dynamic real-time multi-agent architecture for IDS is proposed in this study, allowing the addition of predictable agents (both reactive and deliberative). In particular, two of the deliberative agents deployed in this system incorporate temporal-bounded CBR. This upgraded CBR is based on an anytime approximation, which allows the adaptation of this Artificial Intelligence paradigm to real-time requirements. Experimental results using real data sets are presented which validate the performance of this novel hybrid IDSMinisterio de Economía y Competitividad (TIN2010-21272-C02-01, TIN2009-13839-C03-01), Ministerio de Ciencia e Innovación (CIT-020000-2008-2, CIT-020000-2009-12

Visualization of AI Systems in Virtual Reality: A Comprehensive Review

This study provides a comprehensive review of the utilization of Virtual Reality (VR) for visualizing Artificial Intelligence (AI) systems, drawing on 18 selected studies. The results illuminate a complex interplay of tools, methods, and approaches, notably the prominence of VR engines like Unreal Engine and Unity. However, despite these tools, a universal solution for effective AI visualization remains elusive, reflecting the unique strengths and limitations of each technique. We observed the application of VR for AI visualization across multiple domains, despite challenges such as high data complexity and cognitive load. Moreover, it briefly discusses the emerging ethical considerations pertaining to the broad integration of these technologies. Despite these challenges, the field shows significant potential, emphasizing the need for dedicated research efforts to unlock the full potential of these immersive technologies. This review, therefore, outlines a roadmap for future research, encouraging innovation in visualization techniques, addressing identified challenges, and considering the ethical implications of VR and AI convergence.Comment: 19 page

New technologies for urban designers: the VENUE project

In this report, we first outline the basic idea of VENUE. This involves developing digital tools froma foundation of geographic information systems (GIS) software which we then apply to urbandesign, a subject area and profession which has little tradition in using such tools. Our project wasto develop two types of tool, namely functional analysis based on embedding models of movementin local environments into GIS based on ideas from the field of space syntax; and secondlyfashioning these ideas in a wider digital context in which the entire range of GIS technologies werebrought to bear at the local scale. By local scale, we mean the representation of urban environmentsfrom about 1: 500 to around 1: 2500

Visualization in spatial modeling

This chapter deals with issues arising from a central theme in contemporary computer modeling - visualization. We first tie visualization to varieties of modeling along the continuum from iconic to symbolic and then focus on the notion that our models are so intrinsically complex that there are many different types of visualization that might be developed in their understanding and implementation. This focuses the debate on the very way of 'doing science' in that patterns and processes of any complexity can be better understood through visualizing the data, the simulations, and the outcomes that such models generate. As we have grown more sensitive to the problem of complexity in all systems, we are more aware that the twin goals of parsimony and verifiability which have dominated scientific theory since the 'Enlightenment' are up for grabs: good theories and models must 'look right' despite what our statistics and causal logics tell us. Visualization is the cutting edge of this new way of thinking about science but its styles vary enormously with context. Here we define three varieties: visualization of complicated systems to make things simple or at least explicable, which is the role of pedagogy; visualization to explore unanticipated outcomes and to refine processes that interact in unanticipated ways; and visualization to enable end users with no prior understanding of the science but a deep understanding of the problem to engage in using models for prediction, prescription, and control. We illustrate these themes with a model of an agricultural market which is the basis of modern urban economics - the von Thünen model of land rent and density; a model of urban development based on interacting spatial and temporal processes of land development - the DUEM model; and a pedestrian model of human movement at the fine scale where control of such movements to meet standards of public safety is intrinsically part of the model about which the controllers know intimately. © Springer-Verlag Berlin Heidelberg 2006