55,520 research outputs found
Modeling, Simulation and Emulation of Intelligent Domotic Environments
Intelligent Domotic Environments are a promising approach, based on semantic models and commercially off-the-shelf domotic technologies, to realize new intelligent buildings, but such complexity requires innovative design methodologies and tools for ensuring correctness. Suitable simulation and emulation approaches and tools must be adopted to allow designers to experiment with their ideas and to incrementally verify designed policies in a scenario where the environment is partly emulated and partly composed of real devices. This paper describes a framework, which exploits UML2.0 state diagrams for automatic generation of device simulators from ontology-based descriptions of domotic environments. The DogSim simulator may simulate a complete building automation system in software, or may be integrated in the Dog Gateway, allowing partial simulation of virtual devices alongside with real devices. Experiments on a real home show that the approach is feasible and can easily address both simulation and emulation requirement
Advances in Teaching & Learning Day Abstracts 2004
Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2004
A Rewriting-Logic-Based Technique for Modeling Thermal Systems
This paper presents a rewriting-logic-based modeling and analysis technique
for physical systems, with focus on thermal systems. The contributions of this
paper can be summarized as follows: (i) providing a framework for modeling and
executing physical systems, where both the physical components and their
physical interactions are treated as first-class citizens; (ii) showing how
heat transfer problems in thermal systems can be modeled in Real-Time Maude;
(iii) giving the implementation in Real-Time Maude of a basic numerical
technique for executing continuous behaviors in object-oriented hybrid systems;
and (iv) illustrating these techniques with a set of incremental case studies
using realistic physical parameters, with examples of simulation and model
checking analyses.Comment: In Proceedings RTRTS 2010, arXiv:1009.398
Counterfactual Causality from First Principles?
In this position paper we discuss three main shortcomings of existing
approaches to counterfactual causality from the computer science perspective,
and sketch lines of work to try and overcome these issues: (1) causality
definitions should be driven by a set of precisely specified requirements
rather than specific examples; (2) causality frameworks should support system
dynamics; (3) causality analysis should have a well-understood behavior in
presence of abstraction.Comment: In Proceedings CREST 2017, arXiv:1710.0277
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI
We demonstrate a unified approach to rigorous design of safety-critical
autonomous systems using the VerifAI toolkit for formal analysis of AI-based
systems. VerifAI provides an integrated toolchain for tasks spanning the design
process, including modeling, falsification, debugging, and ML component
retraining. We evaluate all of these applications in an industrial case study
on an experimental autonomous aircraft taxiing system developed by Boeing,
which uses a neural network to track the centerline of a runway. We define
runway scenarios using the Scenic probabilistic programming language, and use
them to drive tests in the X-Plane flight simulator. We first perform
falsification, automatically finding environment conditions causing the system
to violate its specification by deviating significantly from the centerline (or
even leaving the runway entirely). Next, we use counterexample analysis to
identify distinct failure cases, and confirm their root causes with specialized
testing. Finally, we use the results of falsification and debugging to retrain
the network, eliminating several failure cases and improving the overall
performance of the closed-loop system.Comment: Full version of a CAV 2020 pape
A Component-oriented Framework for Autonomous Agents
The design of a complex system warrants a compositional methodology, i.e.,
composing simple components to obtain a larger system that exhibits their
collective behavior in a meaningful way. We propose an automaton-based paradigm
for compositional design of such systems where an action is accompanied by one
or more preferences. At run-time, these preferences provide a natural fallback
mechanism for the component, while at design-time they can be used to reason
about the behavior of the component in an uncertain physical world. Using
structures that tell us how to compose preferences and actions, we can compose
formal representations of individual components or agents to obtain a
representation of the composed system. We extend Linear Temporal Logic with two
unary connectives that reflect the compositional structure of the actions, and
show how it can be used to diagnose undesired behavior by tracing the
falsification of a specification back to one or more culpable components
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