Probabilistic Model Checking of Robots Deployed in Extreme Environments

Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans. This requires a high degree of operational autonomy under uncertain conditions, and poses new challenges for assuring the robot's safety and reliability. In this paper, we develop a framework for probabilistic model checking on a layered Markov model to verify the safety and reliability requirements of such robots, both at pre-mission stage and during runtime. Two novel estimators based on conservative Bayesian inference and imprecise probability model with sets of priors are introduced to learn the unknown transition parameters from operational data. We demonstrate our approach using data from a real-world deployment of unmanned underwater vehicles in extreme environments.Comment: Version accepted at the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, 201

Panel Smooth Transition Regression Models

We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of “extreme regimes”. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms' investment decisions in the presence of capital market imperfections.financial constraints; heterogeneous panel; invesatment; misspecification test; nonlinear modelling panel data; smooth transition model

Panel Smooth Transition Regression Models

We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of “extreme regimes”. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms’ investment decisions in the presence of capital market imperfections.financial constraints; heterogenous panel; investment; misspecification test; nonlinear modelling panel data; smooth transition models

Models for dependent extremes using stable mixtures

This paper unifies and extends results on a class of multivariate Extreme Value (EV) models studied by Hougaard, Crowder, and Tawn. In these models both unconditional and conditional distributions are EV, and all lower-dimensional marginals and maxima belong to the class. This leads to substantial economies of understanding, analysis and prediction. One interpretation of the models is as size mixtures of EV distributions, where the mixing is by positive stable distributions. A second interpretation is as exponential-stable location mixtures (for Gumbel) or as power-stable scale mixtures (for non-Gumbel EV distributions). A third interpretation is through a Peaks over Thresholds model with a positive stable intensity. The mixing variables are used as a modeling tool and for better understanding and model checking. We study extreme value analogues of components of variance models, and new time series, spatial, and continuous parameter models for extreme values. The results are applied to data from a pitting corrosion investigation

From maximum force to physics in 9 lines -- and implications for quantum gravity

A compact summary of present fundamental physics is given and evaluated. Its 9 lines contain both general relativity and the standard model of particle physics. Their precise agreement with experiments, in combination with their extreme simplicity and their internal consistency, suggest that there are no experimental effects beyond the two theories. The combined properties of the 9 lines also imply concrete suggestions for the search for a theory of quantum gravity. Finally, the 9 lines specify the only decisive tests that allow checking any specific proposal for such a theory.Comment: 10 pages, 1 tabl

How checking breeds doubt:reduced performance in a simple working memory task

A paradox of memory research is that repeated checking results in a decrease in memory certainty, memory vividness and confidence [van den Hout, M. A., & Kindt, M. (2003a). Phenomenological validity of an OCD-memory model and the remember/know distinction. Behaviour Research and Therapy, 41, 369–378; van den Hout, M. A., & Kindt, M. (2003b). Repeated checking causes memory distrust. Behaviour Research and Therapy, 41, 301–316]. Although these findings have been mainly attributed to changes in episodic long-term memory, it has been suggested [Shimamura, A. P. (2000). Toward a cognitive neuroscience of metacognition. Consciousness and Cognition, 9, 313–323] that representations in working memory could already suffer from detrimental checking. In two experiments we set out to test this hypothesis by employing a delayed-match-to-sample working memory task. Letters had to be remembered in their correct locations, a task that was designed to engage the episodic short-term buffer of working memory [Baddeley, A. D. (2000). The episodic buffer: a new component in working memory? Trends in Cognitive Sciences, 4, 417–423]. Of most importance, we introduced an intermediate distractor question that was prone to induce frustrating and unnecessary checking on trials where no correct answer was possible. Reaction times and confidence ratings on the actual memory test of these trials confirmed the success of this manipulation. Most importantly, high checkers [cf. VOCI; Thordarson, D. S., Radomsky, A. S., Rachman, S., Shafran, R, Sawchuk, C. N., & Hakstian, A. R. (2004). The Vancouver obsessional compulsive inventory (VOCI). Behaviour Research and Therapy, 42(11), 1289–1314] were less accurate than low checkers when frustrating checking was induced, especially if the experimental context actually emphasized the irrelevance of the misleading question. The clinical relevance of this result was substantiated by means of an extreme groups comparison across the two studies. The findings are discussed in the context of detrimental checking and lack of distractor inhibition as a way of weakening fragile bindings within the episodic short-term buffer of Baddeley's (2000) model. Clinical implications, limitations and future research are considered