Formally Verified Compositional Algorithms for Factored Transition Systems

Artificial Intelligence (AI) planning and model checking are two disciplines that found wide practical applications. It is often the case that a problem in those two fields concerns a transition system whose behaviour can be encoded in a digraph that models the system's state space. However, due to the very large size of state spaces of realistic systems, they are compactly represented as propositionally factored transition systems. These representations have the advantage of being exponentially smaller than the state space of the represented system. Many problems in AI~planning and model checking involve questions about state spaces, which correspond to graph theoretic questions on digraphs modelling the state spaces. However, existing techniques to answer those graph theoretic questions effectively require, in the worst case, constructing the digraph that models the state space, by expanding the propositionally factored representation of the syste\ m. This is not practical, if not impossible, in many cases because of the state space size compared to the factored representation. One common approach that is used to avoid constructing the state space is the compositional approach, where only smaller abstractions of the system at hand are processed and the given problem (e.g. reachability) is solved for them. Then, a solution for the problem on the concrete system is derived from the solutions of the problem on the abstract systems. The motivation of this approach is that, in the worst case, one need only construct the state spaces of the abstractions which can be exponentially smaller than the state space of the concrete system. We study the application of the compositional approach to two fundamental problems on transition systems: upper-bounding the topological properties (e.g. the largest distance between any two states, i.e. the diameter) of the state spa\ ce, and computing reachability between states. We provide new compositional algorithms to solve both problems by exploiting different structures of the given system. In addition to the use of an existing abstraction (usually referred to as projection) based on removing state space variables, we develop two new abstractions for use within our compositional algorithms. One of the new abstractions is also based on state variables, while the other is based on assignments to state variables. We theoretically and experimentally show that our new compositional algorithms improve the state-of-the-art in solving both problems, upper-bounding state space topological parameters and reachability. We designed the algorithms as well as formally verified them with the aid of an interactive theorem prover. This is the first application that we are aware of, for such a theorem prover based methodology to the design of new algorithms in either AI~planning or model checking

Universal Gauss-Thakur sums and L-series

In this paper we study the behavior of the function omega of Anderson-Thakur evaluated at the elements of the algebraic closure of the finite field with q elements F_q. Indeed, this function has quite a remarkable relation to explicit class field theory for the field K=F_q(T). We will see that these values, together with the values of its divided derivatives, generate the maximal abelian extension of K which is tamely ramified at infinity. We will also see that omega is, in a way that we will explain in detail, an universal Gauss-Thakur sum. We will then use these results to show the existence of functional relations for a class of L-series introduced by the second author. Our results will be finally applied to obtain a new class of congruences for Bernoulli-Carlitz fractions, and an analytic conjecture is stated, implying an interesting behavior of such fractions modulo prime ideals of A=F_q[T].Comment: Corrected several typos and an error in the proof of Proposition 21 Section 3. Improved the general presentation of the pape

A Study of Readiness for Transportation Electrification and Automation Focusing on Safety and Future Adoption

Transportation electrification and automation are growing societal trends and considered promising pathways to enhance the safety, mobility, efficiency, and sustainability of the surface transportation system. At this early stage of transportation electrification and automation, one of the most critical issues is whether and to what extent people are willing to adopt electric vehicle (EV) and automated vehicle (AV) technologies in the future. Another critical issue, especially concerning transportation automation, is how to thoroughly ensure the safety of automated driving performance to resolve safety concerns about AVs, which is one of the key challenges to AV adoption. In this regard, the dissertation aims to provide new knowledge and deep insights regarding the readiness for transportation electrification and automation in terms of safety and future adoption by investigating how different types of travelers are willing to embrace EV and AV technologies and what safety-related challenges the automated driving systems are facing. First, the dissertation systematically analyzes how individuals become inclined to use AV-based travel options and adopt alternative fuel vehicles (AFVs). For this, an “AV inclination index” is developed to quantify individual travelers’ inclination toward AV-based travel options encompassing owning an AV, using AV ride-hailing services, and using Shared AV (SAV) ride-hailing services. Importantly, the dissertation reveals a meaningful relationship between the “AV inclination index” and AFV adoption. Considering that the commercial sector has the potential to adopt a considerable amount of EVs in the future, the dissertation explores commercial light-duty fleet owners’ intention to adopt different types of EVs. Paying attention to early adopters’ experiences and perspectives, the dissertation investigates BEV owners’ satisfaction and willingness to repurchase a BEV in the future. Given that the safety of AVs is one of the critical factors associated with individual travelers’ willingness to use AVs in the future, the dissertation performs an exhaustive analysis of crashes involving AVs tested on public roads to provide a better understanding of AV safety performance. Based on the findings from each chapter, the dissertation provides the vehicle and transportation industries, engineers, planners, and policymakers with practical implications for a smooth transition to transportation electrification and automation

Providing and assessing intelligible explanations in autonomous driving

Intelligent vehicles with automated driving functionalities provide many benefits, but also instigate serious concerns around human safety and trust. While the automotive industry has devoted enormous resources to realising vehicle autonomy, there exist uncertainties as to whether the technology would be widely adopted by society. Autonomous vehicles (AVs) are complex systems, and in challenging driving scenarios, they are likely to make decisions that could be confusing to end-users. As a way to bridge the gap between this technology and end-users, the provision of explanations is generally being put forward. While explanations are considered to be helpful, this thesis argues that explanations must also be intelligible (as obligated by the GDPR Article 12) to the intended stakeholders, and should make causal attributions in order to foster confidence and trust in end-users. Moreover, the methods for generating these explanations should be transparent for easy audit. To substantiate this argument, the thesis proceeds in four steps: First, we adopted a mixed method approach (in a user study $N=101$) to elicit passengers' requirements for effective explainability in diverse autonomous driving scenarios. Second, we explored different representations, data structures and driving data annotation schemes to facilitate intelligible explanation generation and general explainability research in autonomous driving. Third, we developed transparent algorithms for posthoc explanation generation. These algorithms were tested within a collision risk assessment case study and an AV navigation case study, using the Lyft Level5 dataset and our new SAX dataset---a dataset that we have introduced for AV explainability research. Fourth, we deployed these algorithms in an immersive physical simulation environment and assessed (in a lab study $N=39$) the impact of the generated explanations on passengers' perceived safety while varying the prediction accuracy of an AV's perception system and the specificity of the explanations. The thesis concludes by providing recommendations needed for the realisation of more effective explainable autonomous driving, and provides a future research agenda

5PM: Secure Pattern Matching

In this paper we consider the problem of secure pattern matching that allows single-character wildcards and substring matching in the malicious (stand-alone) setting. Our protocol, called 5PM, is executed between two parties: Server, holding a text of length $n$, and Client, holding a pattern of length $m$ to be matched against the text, where our notion of matching is more general and includes non-binary alphabets, non-binary Hamming distance and non-binary substring matching. 5PM is the first secure expressive pattern matching protocol designed to optimize round complexity by carefully specifying the entire protocol round by round. In the malicious model, 5PM requires $O((m+n)k^2)$ bandwidth and $O(m+n)$ encryptions, where $m$ is the pattern length and $n$ is the text length. Further, 5PM can hide pattern size with no asymptotic additional costs in either computation or bandwidth. Finally, 5PM requires only two rounds of communication in the honest-but-curious model and eight rounds in the malicious model. Our techniques reduce pattern matching and generalized Hamming distance problems to a novel linear algebra formulation that allows for generic solutions based on any additively homomorphic encryption. We believe our efficient algebraic techniques are of independent interest