Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in uncertainty quantification and robust design applications in aerospace engineering, the calculation of expected values of such functions (e.g. performance measures) becomes important. However, MC techniques often suffer from high variance and slow convergence as the number of samples increases. In this paper we present Stacked Monte Carlo (StackMC), a new method for post-processing an existing set of MC samples to improve the associated integral estimate. StackMC is based on the supervised learning techniques of fitting functions and cross validation. It should reduce the variance of any type of Monte Carlo integral estimate (simple sampling, importance sampling, quasi-Monte Carlo, MCMC, etc.) without adding bias. We report on an extensive set of experiments confirming that the StackMC estimate of an integral is more accurate than both the associated unprocessed Monte Carlo estimate and an estimate based on a functional fit to the MC samples. These experiments run over a wide variety of integration spaces, numbers of sample points, dimensions, and fitting functions. In particular, we apply StackMC in estimating the expected value of the fuel burn metric of future commercial aircraft and in estimating sonic boom loudness measures. We compare the efficiency of StackMC with that of more standard methods and show that for negligible additional computational cost significant increases in accuracy are gained.

A prototype system is described whose core functionality is, based on propositional logic, the elimination of second-order operators, such as Boolean quantifiers and operators for projection, forgetting and circumscription. This approach allows to express many representational and computational tasks in knowledge representation - for example computation of abductive explanations and models with respect to logic programming semantics - in a uniform operational system, backed by a uniform classical semantic framework.

The aim of this paper is to announce the release of a novel system for abstract argumentation which is based on decomposition and dynamic programming. We provide first experimental evaluations to show the feasibility of this approach.

Abstract--In this work we present a quadratic programming approximation of the Semi-Supervised Support V ector Machine (S3VM) problem, namely approximate QP-S3VM, that can be efficiently solved using off the shelf optimization packages. We prove that this approximate formulation establishes a relation between the low density separation and the graph-based models of semi-supervised learning (SSL) which is important to develop a unifying framework for semi-supervised learning methods. Furthermore, we propose the novel idea of representing SSL problems as submodular set functions and use efficient sub-modular optimization algorithms to solve them. Using this new idea we develop a representation of the approximate QP-S3VM as a maximization of a submodular set function which makes it possible to optimize using efficient greedy algorithms. We demonstrate that the proposed methods are accurate and provide significant improvement in time complexity over the state of the art in the literature. The recent advances in information technology imposes serious challenges on traditional machine learning algorithms where classification models are trained using labeled samples. Data collection and storage nowadays has never been easier and therefore using such enormous volumes of data to infer reliable classification models is of utmost importance.

Two different conceptions of emergence are reconciled as two instances of the phenomenon of detection. In the process of comparing these two conceptions, we find that the notions of complexity and detection allow us to form a unified definition of emergence that clearly delineates the role of the observer.

The human intelligence lies in the algorithm, the nature of algorithm lies in the classification, and the classification is equal to outlier detection. A lot of algorithms have been proposed to detect outliers, meanwhile a lot of definitions. Unsatisfying point is that definitions seem vague, which makes the solution an ad hoc one. We analyzed the nature of outliers, and give two clear definitions. We then develop an efficient RDD algorithm, which converts outlier problem to pattern and degree problem. Furthermore, a collapse mechanism was introduced by IIR algorithm, which can be united seamlessly with the RDD algorithm and serve for the final decision. Both algorithms are originated from the study on general AI. The combined edition is named as Pe algorithm, which is the basis of the intelligent decision. Here we introduce longest k-turn subsequence problem and corresponding solution as an example to interpret the function of Pe algorithm in detecting curve-type outliers. We also give a comparison between IIR algorithm and Pe algorithm, where we can get a better understanding at both algorithms. A short discussion about intelligence is added to demonstrate the function of the Pe algorithm. Related experimental results indicate its robustness.

This article describes an exemplary robot exercise which was conducted in a class for mechatronics students. The goal of this exercise was to engage students in scientific thinking and reasoning, activities which do not always play an important role in their curriculum. The robotic platform presented here is simple in its construction and is customizable to the needs of the teacher. Therefore, it can be used for exercises in many different fields of science, not necessarily related to robotics. Here we present a situation where the robot is used like an alien creature from which we want to understand its behavior, resembling an ethological research activity. This robot exercise is suited for a wide range of courses, from general introduction to science, to hardware oriented lectures.

Abstract: Genetic algorithms are considered as an original way to solve problems, probably because of their generality and of their "blind" nature. But GAs are also unusual since the features of many implementations (among all that could be thought of) are principally led by the biological metaphor, while efficiency measurements intervene only afterwards. We propose here to examine the relevance of these biomimetic aspects, by pointing out some fundamental similarities and divergences between GAs and the genome of living beings shaped by natural selection. One of the main differences comes from the fact that GAs rely principally on the so-called implicit parallelism, while giving to the mutation/selection mechanism the second role. Such differences could suggest new ways of employing GAs on complex problems, using complex codings and starting from nearly homogeneous populations. In GAs, individuals are represented by their genome (most often a binary vector), and are evaluated so that only the best fitted ones have some chance to reproduce.

Abstract--In the paper arguments are given why the concept of static evaluation has the potential to be a useful extension to Monte Carlo tree search. A new concept of modeling static evaluation through a dynamical system is introduced and strengths and weaknesses are discussed. The general suitability of this approach is demonstrated. The concept of Monte-Carlo simulations applied to Go [1] combined with the UCT algorithm [2], [3], which is a tree search method based on Upper Confidence Bounds (UCB) (see e.g. The detailed tournament report [8] of the program MoGo playing against professional and amateur players reveals strengths and weaknesses of MoGo which are typical for programs that perform a Monte Carlo tree search (MCTS). Programs performing MCTS can utilize ever increasing computing power but in their pure form without extra Go knowledge the ratio log(increase in needed computing power) / (increase in strength) is too big to get to professional strength on large boards in the foreseeable future. Therefore in recent years Go knowledge has been incorporated either in form of heuristics, or pattern databases learned from professional games or from self-play. Although treesearch was naturally slowed down the playing strength increased further. With all of this tremendous progress of MCTS compared to the knowledge based era of computer Go summarized in [9], [10], [11], it needs good reasons to start work on a static evaluation function (SE) in Go. One indicator that more Go knowledge needs to be added is that, compared with human playing strength the playing level of current programs decreases as board size increases from 9 9 to 13 13 and then to 19 19. The principal difficulties of deriving knowledge and applying it become more relevant as knowledge is increasingly used in MCTS. Knowledge that is not 100% accurate reduces the scalability of the program when enough computing power is available for global search to replace increasingly the approximate Go knowledge which then becomes less useful or even less accurate than knowledge coming from search. It is difficult to combine knowledge on a high level if it comes from different sources, like from pattern and from local searches. It is one of the reasons of the originally surprising success of pure MCTS that it only uses knowledge from one source (statistics of simulations) without the need of merging different types of knowledge.

We consider a fuzzy linear system with crisp coefficient matrix and with an arbitrary fuzzy number in parametric form on the right-hand side. It is known that the well-known existence and uniqueness theorem of a strong fuzzy solution is equivalent to the following: The coefficient matrix is the product of a permutation matrix and a diagonal matrix. This means that this theorem can be applicable only for a special form of linear systems, namely, only when the system consists of equations, each of which has exactly one variable. We prove an existence and uniqueness theorem, which can be use on more general systems. The necessary and sufficient conditions of the theorem are dependent on both the coefficient matrix and the right-hand side. This theorem is a generalization of the well-known existence and uniqueness theorem for the strong solution.