Cyber Physical Systems (CPS) integrate computational intelligence with physical processes, improving efficiency of complex infrastructures. CPS subsystems are prone to faults arising from malfunctions, degradation, and wear and tear. Such faults pose a risk to maintaining system-level performance, particularly by interrupting the subsystem dependencies. This paper presents an online model-based fault-tolerant control framework for CPSs modeled as Finite State Automata (FSA). We introduce fault-tolerant strategies that use quantified FSA-based risk measures. These strategies enable proactive adjustment of the control policy such that solutions that may lead to control infeasibility under subsystem faults are prevented. Unlike approaches reliant on historical fault data, the proposed methods are suitable for systems with no prior fault records or with dynamically evolving requirements. The efficacy of these approaches is demonstrated through two case studies, highlighting their ability to maintain control feasibility in the presence of faults. Additionally, a comprehensive sensitivity analysis on randomly generated FSAs compares the fault-tolerance of the proposed controllers against cost-optimal control design. Results show that FSA-based risk quantification improves fault tolerance in online control.

Temporal logic specifications play an important role in a wide range of software analysis tasks, such as model checking, automated synthesis, program comprehension, and runtime monitoring. Given a set of positive and negative examples, specified as traces, LTL learning is the problem of synthesizing a specification, in linear temporal logic (LTL), that evaluates to true over the positive traces and false over the negative ones. In this paper, we propose a new type of LTL learning problem called constrained LTL learning, where the user, in addition to positive and negative examples, is given an option to specify one or more constraints over the properties of the LTL formula to be learned. We demonstrate that the ability to specify these additional constraints significantly increases the range of applications for LTL learning, and also allows efficient generation of LTL formulas that satisfy certain desirable properties (such as minimality). We propose an approach for solving the constrained LTL learning problem through an encoding in first-order relational logic and reduction to an instance of the maximal satisfiability (MaxSAT) problem. An experimental evaluation demonstrates that ATLAS, an implementation of our proposed approach, is able to solve new types of learning problems while performing better than or competitively with the state-of-the-art tools in LTL learning.

Abstract Cyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the Internet-of-Things (IoT), and smart cities. An important property of a CPS is tolerance; i.e., its ability to function safely under possible disturbances and uncertainties in the actual operation. In this paper, we introduce a new, expressive notion of tolerance that describes how well a controller is capable of satisfying a desired system requirement, specified using Signal Temporal Logic (STL), under possible deviations in the system. Based on this definition, we propose a novel analysis problem, called the tolerance falsification problem, which involves finding small deviations that result in a violation of the given requirement. We present a novel, two-layer simulation-based analysis framework and a novel search heuristic for finding small tolerance violations. To evaluate our approach, we construct a set of benchmark problems where system parameters can be configured to represent different types of uncertainties and disturbances in the system. Our evaluation shows that our falsification approach and heuristic can effectively find small tolerance violations.

Abstract A safety verification task involves verifying a system against a desired safety property under certain assumptions about the environment. However, these environmental assumptions may occasionally be violated due to modeling errors or faults. Ideally, the system guarantees its critical properties even under some of these violations, i.e., the system is robust against environmental deviations. This paper proposes a notion of robustness as an explicit, first-class property of a transition system that captures how robust it is against possible deviations in the environment. We modeled deviations as a set of transitions that may be added to the original environment. Our robustness notion then describes the safety envelope of this system, i.e., it captures all sets of extra environment transitions for which the system still guarantees a desired property. We show that being able to explicitly reason about robustness enables new types of system analysis and design tasks beyond the common verification problem stated above. We demonstrate the application of our framework on case studies involving a radiation therapy interface, an electronic voting machine, a fare collection protocol, and a medical pump device.

Modern software systems are deployed in a highly dynamic, uncertain environment. Ideally, a system that is robust should be capable of establishing its most critical requirements even in the presence of possible deviations in the environment. We propose a technique called behavioral robustification, which involves systematically and rigorously improving the robustness of a design against potential deviations. Given behavioral models of a system and its environment, along with a set of user-specified deviations, our robustification method produces a redesign that is capable of satisfying a desired property even when the environment exhibits those deviations. In particular, we describe how the robustification problem can be formulated as a multi-objective optimization problem, where the goal is to restrict the deviating environment from causing a violation of a desired property, while maximizing the amount of existing functionality and minimizing the cost of changes to the original design. We demonstrate the effectiveness of our approach on case studies involving the robustness of an electronic voting machine and safety-critical interfaces.

The feature interaction problem occurs when two or more independently developed components interact with each other in unanticipated ways, resulting in undesirable system behaviors. Feature interaction problems remain a challenge for emerging domains in cyber-physical systems (CPS), such as the Internet of Things and autonomous drones. Existing techniques for resolving feature interactions take a “winner-takes-all” approach, where one out of the conflicting features is selected as the most desirable one, and the rest are disabled. However, when multiple of the conflicting features fulfill important system requirements, being forced to select one of them can result in an undesirable system outcome. In this paper, we propose a new resolution approach that allows all of the conflicting features to continue to partially fulfill their requirements during the resolution process. In particular, our approach leverages the idea of adaptive requirement weakening, which involves one or more features temporarily weakening their level of performance in order to co-exist with the other features in a consistent manner. Given feature requirements specified in Signal Temporal Logic (STL), we propose an automated method and a runtime architecture for automatically weakening the requirements to resolve a conflict. We demonstrate our approach through case studies on feature interactions in autonomous drones

Self-adaptive systems typically operate in heterogeneous environments and need to optimize their behavior based on a variety of quality attributes to meet stakeholders' needs. During adaptation planning, these quality attributes are considered in the form of constraints, describing requirements that must be fulfilled, and utility functions, which are used to select an optimal plan among several alternatives. Up until now, most automated planning approaches are not designed to adapt quality attributes, their priorities, and their trade-offs at run time. Instead, both utility functions and constraints are commonly defined at design time. There exists a clear lack of run-time mechanisms that support their adaptation in response to changes in the environment or in stakeholders' preferences. In this paper, we present initial work that combines automated planning and adaptation of quality attributes to address this gap. The approach helps to semi-automatically adjust utility functions and constraints based on changes at run time. We present a preliminary experimental evaluation that indicates that our approach can provide plans with higher utility values while fulfilling changed or added constraints. We conclude this paper with our envisioned research outlook and plans for future empirical studies.