On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

Understanding of Visual Domains via the Lens of Natural Language

A joint understanding of vision and language can enable intelligent systems to perceive, act, and communicate with humans for a wide range of applications. For example, they can assist a human to navigate in an environment, edit the content of an image through natural language commands, or search through image collections using natural language queries. In this thesis, we aim to improve our understanding of visual domains through the lens of natural language. We specifically look into (1) images of categories within a fine-grained taxonomy such as species of birds or variants of aircraft, (2) images of textures that describe local color, shape, and patterns, and (3) regions in images that correspond to objects, materials, and textures. In one line of work, we investigate ways to discover a domain-specific language by asking annotators to describe visual differences between instances within a fine-grained taxonomy. We show that a system trained to describe these differences leads to an accurate and interpretable basis for categorization. In another line of work, we investigate the effectiveness of language and vision models for describing textures, a problem that, despite the ubiquity of textures, has not been sufficiently studied in the literature. Textures are diverse, yet their local nature allows for the description of appearance of a wide range of visual categories. The locality also allows us to systematically generate synthetic variations to investigate how disentangled visual representations are for properties such as shape, color, and figure-ground segmentation. Finally, instead of modeling an image as a whole, we design a system that allows descriptions of regions within an image. A challenge is to handle the long-tail distribution of names and appearances of concepts within natural scenes. We design a modular framework that integrates object detection, semantic segmentation, and contextual reasoning with language that leads to better performance. In addition to methods and analysis, we contribute datasets and benchmarks to evaluate the performance of models in each of these domains. The availability of large-scale pre-trained models for vision (e.g., ResNet) and language (e.g., BERT) have catalyzed improvements and novel applications in computer vision and natural language processing, but until recently similar models that could jointly reason about language and vision were not available. This has changed through the availability of models such as CLIP, which have been trained on a massive number of images with associated texts. Therefore, we analyze the effectiveness of CLIP-based representations for tasks posed in our earlier work. By comparing and contrasting these with domain-specific ones we presented in the earlier chapters, we shed some light on the nature of the learned representations and the biases they encode

FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation

FlightGoggles is a photorealistic sensor simulator for perception-driven robotic vehicles. The key contributions of FlightGoggles are twofold. First, FlightGoggles provides photorealistic exteroceptive sensor simulation using graphics assets generated with photogrammetry. Second, it provides the ability to combine (i) synthetic exteroceptive measurements generated in silico in real time and (ii) vehicle dynamics and proprioceptive measurements generated in motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of simulating a virtual-reality environment around autonomous vehicle(s). While a vehicle is in flight in the FlightGoggles virtual reality environment, exteroceptive sensors are rendered synthetically in real time while all complex extrinsic dynamics are generated organically through the natural interactions of the vehicle. The FlightGoggles framework allows for researchers to accelerate development by circumventing the need to estimate complex and hard-to-model interactions such as aerodynamics, motor mechanics, battery electrochemistry, and behavior of other agents. The ability to perform vehicle-in-the-loop experiments with photorealistic exteroceptive sensor simulation facilitates novel research directions involving, e.g., fast and agile autonomous flight in obstacle-rich environments, safe human interaction, and flexible sensor selection. FlightGoggles has been utilized as the main test for selecting nine teams that will advance in the AlphaPilot autonomous drone racing challenge. We survey approaches and results from the top AlphaPilot teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be found at https://flightgoggles.mit.edu. Revision includes description of new FlightGoggles features, such as a photogrammetric model of the MIT Stata Center, new rendering settings, and a Python AP

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges