Generating Diagnoses for Probabilistic Model Checking Using Causality

One of the most major advantages of Model checking over other formal methods of verification, its ability to generate an error trace in case of a specification falsified in the model. We call this trace a counterexample. However, understanding the counterexample is not that easy task, because model checker generates usually multiple counterexamples of long length, what makes the analysis of counterexample time-consuming as well as costly task. Therefore, counterexamples should be small and as indicative as possible to be understood. In probabilistic model checking (PMC) counterexample generation has a quantitative aspect.  The counterexample in PMC is a set of paths in which a path formula holds, and their accumulative probability mass violates the probability bound. In this paper, we address the complementary task of counterexample generation which is the counterexample diagnosis in PMC. We propose an aided-diagnostic method for probabilistic counterexamples based on the notion of causality and responsibility. Given a counterexample for a Probabilistic CTL (PCTL) formula that doesn’t hold over Discreet-Time-Markov-Chain (DTMC) model, this method guides the user to the most responsible causes in the counterexample.</p

A Final Determination of the Complexity of Current Formulations of Model-Based Diagnosis (Or Maybe Not Final?)

There are three parts to this paper. First, I present what I hope is a conclusive, worst-case, complexity analysis of two well-known formulations of the Minimal Diagnosis problem — those of [Reiter 87] and [Reggia et al., 85]. I then show that Reiter\u27s conflict-sets solution to the problem decomposes the single exponential problem into two problems, each exponential, that need be solved sequentially. From a worst case perspective, this only amounts to a factor of two, in which case I see no reason to prefer it over a simple generate-and-test approach. This is only emphasized with the results of the third part of the paper. Here I argue for a different perspective on algorithms, that of expected, rather than worst-case performance. From that point of view, a sequence of two exponential algorithms has lesser probability to finish early than a single such algorithm. I show that the straightforward generate-and-test approach may in fact be somewhat attractive as it has high probability to conclude in a polynomial time, given a random problem instance

Beyond Covariation: Cues to Causal Structure

Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected theses. First, people represent causal knowledge qualitatively, in terms of causal structure; quantitative knowledge is derivative. Second, people use a variety of cues to infer causal structure aside from statistical data (e.g. temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm, refute, or elaborate the model. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models, and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis

Debugging performance anomalies in real-world databases is challenging. Causal inference techniques enable qualitative and quantitative root cause analysis of performance downgrade. Nevertheless, causality analysis is practically challenging, particularly due to limited observability. Recently, chaos engineering has been applied to test complex real-world software systems. Chaos frameworks like Chaos Mesh mutate a set of chaos variables to inject catastrophic events (e.g., network slowdowns) to "stress" software systems. The systems under chaos stress are then tested using methods like differential testing to check if they retain their normal functionality (e.g., SQL query output is always correct under stress). Despite its ubiquity in the industry, chaos engineering is now employed mostly to aid software testing rather for performance debugging. This paper identifies novel usage of chaos engineering on helping developers diagnose performance anomalies in databases. Our presented framework, PERFCE, comprises an offline phase and an online phase. The offline phase learns the statistical models of the target database system, whilst the online phase diagnoses the root cause of monitored performance anomalies on the fly. During the offline phase, PERFCE leverages both passive observations and proactive chaos experiments to constitute accurate causal graphs and structural equation models (SEMs). When observing performance anomalies during the online phase, causal graphs enable qualitative root cause identification (e.g., high CPU usage) and SEMs enable quantitative counterfactual analysis (e.g., determining "when CPU usage is reduced to 45\%, performance returns to normal"). PERFCE notably outperforms prior works on common synthetic datasets, and our evaluation on real-world databases, MySQL and TiDB, shows that PERFCE is highly accurate and moderately expensive

Research on Fault Diagnosis Based on Dynamic causality diagram and Fuzzy Reasoning Fusion Method

With the progress of urbanization, the demand for elevators has upgraded from safe operation to comfortable, efficient, and all-round demand. The abnormal operation of the elevator is difficult to diagnose due to the complexity of the fault. This paper proposes a fault diagnosis method based on dynamic causality diagram and fuzzy reasoning. The dynamic causality diagram is extended, the intermediate module nodes are added, the description of the intermediate process of the elevator control system is solved, and the complete expression of knowledge is realized. The control timing of the elevator operation is introduced into the network structure of the dynamic causality diagram, which enhances the dynamic characteristics of the network. The causal cycle logic of the dynamic causality diagram is used to represent input and output signals and faults in elevator control systems. In the update of fuzzy rules, the real-time of fuzzy reasoning is enhanced, the search space of fuzzy rule matching is reduced, and the efficiency is improved. This paper combines actual field measurements and experimental data for fault diagnosis. Finally, the simulation, diagnosis and maintenance decision of the fault are realized, and an intelligent solution for elevator fault diagnosis is further proposed

Editorial

Dear readers, in front of you is the first issue of Volume 21 of CIT. Journal of Computing and Information Technology, which has been prepared under supervision of the new Editorial Board. After the handing over of the duty, the new members who have taken editorial position are (in alphabetic order) Maria Victoria Bueno Delgado, Eugenio Di Sciascio, Andreas Holzinger, Shifeng Liu, Zongmin Ma, Aniket Mahanti, Tanja Mitrovic, Richard Picking, Richard Torkar, Runtong Zhang and Hong Zhao, while Mincong Tang took the Assistant Editor position. I would like to point out that a number of them started the cooperation with CIT in 2012 already, thus enabling the improvement of its workflow

Estimating the family bias to autism: a bayesian approach

Autism is an age- and sex-related lifelong neurodevelopmental condition characterized pri marily by persistent deficits in core domains such as social communication. It is estimated that ≈ 2% of children have some ASD trait. The autism etiology is mainly due to inherited genetic factors (>80%). The importance of early diagnosis and interventions motivated several studies involving groups at high risk for ASD, those with a greater predisposition to the disorder. Such studies are characterized by evaluating some characteristics of the individual itself or the family members of diagnosed individuals, mainly aiming to predict a future diagnosis or recurrence rates. One of the primary goals of Artificial Intelligence is to create artificial agents capable of intelligent behaviors, such as prediction problems. Prediction problems usually involve reasoning with uncertainty due to some information deficiency, in which the data may be imprecise or incorrect. Such solutions may seek the application of probabilistic methods to construct inference models. In this thesis, we will discuss the development of probabilistic networks capable of estimating the risk of autism among the family members given some evidence (e.g., other family members with ASD). In particular, the main novel contributions of this thesis are as follows: the proposal of some estimates regarding parents with ASD generating children with ASD; the highlight ing regarding the decrease in the ASD prevalence sex ratio among males and females when genetic factors are taken into account; the corroboration and quantification of past evidence that the clustering of ASD in families is primarily due to genetic factors; the computation of some estimates regarding the risk of ASD for parents, grandparents, and siblings; an estimate regarding the number of ASD cases in a family sufficient to attribute the ASD occurrences to the genetic inheritance; the assessment of some estimates for males and females individuals given evidence in grandparents, aunts-or-uncles, nieces-or nephews and cousins; and the proposition of some estimates indicating risk ranges for ASD by genetic similarity