Scenic: A Language for Scenario Specification and Scene Generation

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.Comment: 41 pages, 36 figures. Full version of a PLDI 2019 paper (extending UC Berkeley EECS Department Tech Report No. UCB/EECS-2018-8

Model-driven engineering approach to design and implementation of robot control system

In this paper we apply a model-driven engineering approach to designing domain-specific solutions for robot control system development. We present a case study of the complete process, including identification of the domain meta-model, graphical notation definition and source code generation for subsumption architecture -- a well-known example of robot control architecture. Our goal is to show that both the definition of the robot-control architecture and its supporting tools fits well into the typical workflow of model-driven engineering development.Comment: Presented at DSLRob 2011 (arXiv:cs/1212.3308

A gentle transition from Java programming to Web Services using XML-RPC

Exposing students to leading edge vocational areas of relevance such as Web Services can be difficult. We show a lightweight approach by embedding a key component of Web Services within a Level 3 BSc module in Distributed Computing. We present a ready to use collection of lecture slides and student activities based on XML-RPC. In addition we show that this material addresses the central topics in the context of web services as identified by Draganova (2003)

Constraint specification by example in a Meta-CASE tool

CASE tools are very helpful to software engineers in different ways and in different phases of software development. However, they are not easy to specialise to meet the needs of particular application domains or particular software modelling requirements. Meta-CASE tools offer a way of providing such specialisation by enabling a designer to specify a tool which is then generated automatically. Constraints are often used in such meta-CASE tools as a technique for governing the syntax and semantics of model elements and the values of their attributes. However, although constraint definition is a difficult process it has attracted relatively little research attention. The PhD research described here presents an approach for improving the process of CASE tool constraint specification based on the notion of programming by example (or demonstration). The feasibility of the approach will be demonstrated via experiments with a prototype using the meta-CASE tool Diagram Editor Constraints System (DECS) as context