28,510 research outputs found
A Proof Strategy Language and Proof Script Generation for Isabelle/HOL
We introduce a language, PSL, designed to capture high level proof strategies
in Isabelle/HOL. Given a strategy and a proof obligation, PSL's runtime system
generates and combines various tactics to explore a large search space with low
memory usage. Upon success, PSL generates an efficient proof script, which
bypasses a large part of the proof search. We also present PSL's monadic
interpreter to show that the underlying idea of PSL is transferable to other
ITPs.Comment: This paper has been submitted to CADE2
An Abstract Formal Basis for Digital Crowds
Crowdsourcing, together with its related approaches, has become very popular
in recent years. All crowdsourcing processes involve the participation of a
digital crowd, a large number of people that access a single Internet platform
or shared service. In this paper we explore the possibility of applying formal
methods, typically used for the verification of software and hardware systems,
in analysing the behaviour of a digital crowd. More precisely, we provide a
formal description language for specifying digital crowds. We represent digital
crowds in which the agents do not directly communicate with each other. We
further show how this specification can provide the basis for sophisticated
formal methods, in particular formal verification.Comment: 32 pages, 4 figure
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
A Graphical Language for Proof Strategies
Complex automated proof strategies are often difficult to extract, visualise,
modify, and debug. Traditional tactic languages, often based on stack-based
goal propagation, make it easy to write proofs that obscure the flow of goals
between tactics and are fragile to minor changes in input, proof structure or
changes to tactics themselves. Here, we address this by introducing a graphical
language called PSGraph for writing proof strategies. Strategies are
constructed visually by "wiring together" collections of tactics and evaluated
by propagating goal nodes through the diagram via graph rewriting. Tactic nodes
can have many output wires, and use a filtering procedure based on goal-types
(predicates describing the features of a goal) to decide where best to send
newly-generated sub-goals.
In addition to making the flow of goal information explicit, the graphical
language can fulfil the role of many tacticals using visual idioms like
branching, merging, and feedback loops. We argue that this language enables
development of more robust proof strategies and provide several examples, along
with a prototype implementation in Isabelle
Behavior Trees in Robotics and AI: An Introduction
A Behavior Tree (BT) is a way to structure the switching between different
tasks in an autonomous agent, such as a robot or a virtual entity in a computer
game. BTs are a very efficient way of creating complex systems that are both
modular and reactive. These properties are crucial in many applications, which
has led to the spread of BT from computer game programming to many branches of
AI and Robotics. In this book, we will first give an introduction to BTs, then
we describe how BTs relate to, and in many cases generalize, earlier switching
structures. These ideas are then used as a foundation for a set of efficient
and easy to use design principles. Properties such as safety, robustness, and
efficiency are important for an autonomous system, and we describe a set of
tools for formally analyzing these using a state space description of BTs. With
the new analysis tools, we can formalize the descriptions of how BTs generalize
earlier approaches. We also show the use of BTs in automated planning and
machine learning. Finally, we describe an extended set of tools to capture the
behavior of Stochastic BTs, where the outcomes of actions are described by
probabilities. These tools enable the computation of both success probabilities
and time to completion
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