Alternative Restart Strategies for CMA-ES

This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization

Clues About Bluffing in Clue: Is Conventional Wisdom Wise?

We have used the board game Clue as a pedagogical tool in our course on Artificial Intelligence to teach formal logic through the development of logic-based computational game-playing agents. The development of game-playing agents allows us to experimentally test many game-play strategies and we have encountered some surprising results that refine “conventional wisdom” for playing Clue. In this paper we consider the effect of the oft-used strategy wherein a player uses their own cards when making suggestions (i.e., “bluffing”) early in the game to mislead other players or to focus on acquiring a particular kind of knowledge. We begin with an intuitive argument against this strategy together with a quantitative probabilistic analysis of this strategy’s cost to a player that both suggest “bluffing” should be detrimental to winning the game. We then present our counter-intuitive simulation results from playing computational agents that “bluff” against those that do not that show “bluffing” to be beneficial. We conclude with a nuanced assessment of the cost and benefit of “bluffing” in Clue that shows the strategy, when used correctly, to be beneficial and, when used incorrectly, to be detrimental

Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems. CMA-ES is well known to be almost parameterless, meaning that only one hyper-parameter, the population size, is proposed to be tuned by the user. In this paper, we propose a principled approach called self-CMA-ES to achieve the online adaptation of CMA-ES hyper-parameters in order to improve its overall performance. Experimental results show that for larger-than-default population size, the default settings of hyper-parameters of CMA-ES are far from being optimal, and that self-CMA-ES allows for dynamically approaching optimal settings.Comment: 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014) (2014

Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care

We present an ensemble learning method that predicts large increases in the hours of home care received by citizens. The method is supervised, and uses different ensembles of either linear (logistic regression) or non-linear (random forests) classifiers. Experiments with data available from 2013 to 2017 for every citizen in Copenhagen receiving home care (27,775 citizens) show that prediction can achieve state of the art performance as reported in similar health related domains (AUC=0.715). We further find that competitive results can be obtained by using limited information for training, which is very useful when full records are not accessible or available. Smart city analytics does not necessarily require full city records. To our knowledge this preliminary study is the first to predict large increases in home care for smart city analytics

Thrust dynamometer Patent

Development of thrust dynamometer for measuring performance of jet and rocket engine

Torsion and Bending Properties of EdgeEndo Files

Introduction: One important step of root canal therapy is the process of cleaning and shaping each canal. This process involves using endodontic rotary files combined with chemical irrigants to remove pulpal remnants and infected dentin from the canal while eliminating pathogenic bacteria. It is essential to maintain proper canal anatomy while cleaning and shaping. The challenge for the practitioner is to select a rotary file system that will be flexible enough to maintain canal anatomy but strong enough to prevent breakage under normal use. File flexibility allows for better maintenance of canal anatomy. A file’s resistance to torsional fatigue reduces the chance of file breakage. The purpose of this study was to compare the torsion and bending properties of a brand new file system (EdgeFiles by EdgeEndo, Albuquerque, NM) marketed as being twice as strong but half the price compared to other marketed files Materials and Methods: Thirty files of each type were used. Ten different files systems were evaluated. Size 30 files of .04 taper EdgeFile X7, EdgeFile X5, EndoSequence (Brasseler), Vortex Blue (Dentsply), GT Series X (Dentsply), K3XF (SybronEndo), HyFlex CM (Coltene/Whaledent, Inc.), and .06 taper EdgeFile X3 (EdgeEndo), ProTaper Universal (Dentsply), ProTaper Gold (Dentsply). Testing was done with a torsiometer following ISO 3630-1. Twelve of each file type were evaluated for bending and 18 of each type were evaluated with torsion. Results were separated into 3 different groups due to differences in file design. Group 1 included X3, Protaper Universal, and Protaper Gold. Group 2 included X5 and GT series X. Group 3 included X7, EndoSequence, Vortex Blue, K3XF, and HyFlex CM. Results: In Group 1, X3 showed the most flexibility followed by ProTaper Gold then ProTaper Universal. For strength, ProTaper Gold had the highest resistance to torsion followed by ProTaper Universal then X3. In Group 2, X5 showed more flexibility while GTX had higher strength. In Group 3, HyFlex CM showed the most flexibility followed by X7, then EndoSequence, Vortex Blue, and finally K3XF. For strength, K3XF was highest. X7 and Vortex Blue had similar values which were higher than HyFlex CM followed by EndoSequence. Conclusion: An overall conclusion could be made that strength and flexibility have a relatively inverse relationship in each group. The stronger files tend to be less flexible and the more flexible files tend to be more susceptible to torsional failure. ProTaper Gold and X7 had the best combinations of strength and flexibility