Bayesian Verification under Model Uncertainty

Machine learning enables systems to build and update domain models based on runtime observations. In this paper, we study statistical model checking and runtime verification for systems with this ability. Two challenges arise: (1) Models built from limited runtime data yield uncertainty to be dealt with. (2) There is no definition of satisfaction w.r.t. uncertain hypotheses. We propose such a definition of subjective satisfaction based on recently introduced satisfaction functions. We also propose the BV algorithm as a Bayesian solution to runtime verification of subjective satisfaction under model uncertainty. BV provides user-definable stochastic bounds for type I and II errors. We discuss empirical results from an example application to illustrate our ideas.Comment: Accepted at SEsCPS @ ICSE 201

Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms

The evolutionary edit distance between two individuals in a population, i.e., the amount of applications of any genetic operator it would take the evolutionary process to generate one individual starting from the other, seems like a promising estimate for the diversity between said individuals. We introduce genealogical diversity, i.e., estimating two individuals' degree of relatedness by analyzing large, unused parts of their genome, as a computationally efficient method to approximate that measure for diversity.Comment: Measuring and Promoting Diversity in Evolutionary Algorithms @ GECCO 201

Scalable Multiagent Coordination with Distributed Online Open Loop Planning

We propose distributed online open loop planning (DOOLP), a general framework for online multiagent coordination and decision making under uncertainty. DOOLP is based on online heuristic search in the space defined by a generative model of the domain dynamics, which is exploited by agents to simulate and evaluate the consequences of their potential choices. We also propose distributed online Thompson sampling (DOTS) as an effective instantiation of the DOOLP framework. DOTS models sequences of agent choices by concatenating a number of multiarmed bandits for each agent and uses Thompson sampling for dealing with action value uncertainty. The Bayesian approach underlying Thompson sampling allows to effectively model and estimate uncertainty about (a) own action values and (b) other agents' behavior. This approach yields a principled and statistically sound solution to the exploration-exploitation dilemma when exploring large search spaces with limited resources. We implemented DOTS in a smart factory case study with positive empirical results. We observed effective, robust and scalable planning and coordination capabilities even when only searching a fraction of the potential search space

Stacked Thompson Bandits

We introduce Stacked Thompson Bandits (STB) for efficiently generating plans that are likely to satisfy a given bounded temporal logic requirement. STB uses a simulation for evaluation of plans, and takes a Bayesian approach to using the resulting information to guide its search. In particular, we show that stacking multiarmed bandits and using Thompson sampling to guide the action selection process for each bandit enables STB to generate plans that satisfy requirements with a high probability while only searching a fraction of the search space.Comment: Accepted at SEsCPS @ ICSE 201

QoS-Aware Multi-Armed Bandits

Motivated by runtime verification of QoS requirements in self-adaptive and self-organizing systems that are able to reconfigure their structure and behavior in response to runtime data, we propose a QoS-aware variant of Thompson sampling for multi-armed bandits. It is applicable in settings where QoS satisfaction of an arm has to be ensured with high confidence efficiently, rather than finding the optimal arm while minimizing regret. Preliminary experimental results encourage further research in the field of QoS-aware decision making.Comment: Accepted at IEEE Workshop on Quality Assurance for Self-adaptive Self-organising Systems, FAS* 201

Life before the minster: the social dynamics of monastic foundation at Anglo-Saxon Lyminge, Kent

Anglo-Saxon monastic archaeology has been constrained by the limited scale of past investigations and their overriding emphasis on core buildings. This paper draws upon the results of an ongoing campaign of archaeological research that is redressing the balance through an ambitious programme of open-area excavation at Lyminge, Kent, the site of a royal double monastery founded in the seventh century ad. The results of five completed fieldwork seasons are assessed and contextualised in a narrative sequence emphasising the dynamic character of Lyminge as an Anglo-Saxon monastic settlement. In so doing, the study brings into sharp focus how early medieval monasteries were emplaced in the landscape, with specific reference to Anglo-Saxon Kent, a regional context offering key insights into how the process of monastic foundation redefined antecedent central places of long-standing politico-religious significance and social action

La couverture du crime par la presse : un portait fidèle ou déformé ?

The authors examined, through content analysis, some criticisms that have been levelled at the press in its coverage of crime. The propositions examined included the accusations that newspapers are preoccupied with violence and “street” crime, that they focus on the bizarre, are superficial in their reporting of crime, misinform the public about the characteristics of offenders and victims, and exhibit a conservative bias in their analysis. This study of a major Canadian daily newspaper revealed substantiation for some of these claims but failed to support others. Violent and street crimes received disproportionate coverage and very few articles contained an in-depth analysis of the roots of crime or the workings of the criminal justice system. The evidence was less clear or non-existent in relation to the claims that the press focus on nonroutine events, that they provide distorted images of offenders and victims and that they have a conservative bent. Commentant l'impact des masse-médias, Marshall McLuhan (1978) soulignait: To invade the private person, or to invade a group with teaching, with doctrines, with entertainment, all these are alike forms of violence. To assume the right to program the sensibilities or thoughts and fantasies of individuals or groups, has long been taken for granted as a viable form of personal or social action... Today, however, there is a new dimension in all of these activities. Electric media move information and people at the speed of light. It is this instant and total quality that constitutes the condition of mass man and the mass society (p. 212)

Approximate Approximation on a Quantum Annealer

Many problems of industrial interest are NP-complete, and quickly exhaust resources of computational devices with increasing input sizes. Quantum annealers (QA) are physical devices that aim at this class of problems by exploiting quantum mechanical properties of nature. However, they compete with efficient heuristics and probabilistic or randomised algorithms on classical machines that allow for finding approximate solutions to large NP-complete problems. While first implementations of QA have become commercially available, their practical benefits are far from fully explored. To the best of our knowledge, approximation techniques have not yet received substantial attention. In this paper, we explore how problems' approximate versions of varying degree can be systematically constructed for quantum annealer programs, and how this influences result quality or the handling of larger problem instances on given set of qubits. We illustrate various approximation techniques on both, simulations and real QA hardware, on different seminal problems, and interpret the results to contribute towards a better understanding of the real-world power and limitations of current-state and future quantum computing.Comment: Proceedings of the 17th ACM International Conference on Computing Frontiers (CF 2020