A domain decomposition strategy to efficiently solve structures containing repeated patterns

This paper presents a strategy for the computation of structures with repeated patterns based on domain decomposition and block Krylov solvers. It can be seen as a special variant of the FETI method. We propose using the presence of repeated domains in the problem to compute the solution by minimizing the interface error on several directions simultaneously. The method not only drastically decreases the size of the problems to solve but also accelerates the convergence of interface problem for nearly no additional computational cost and minimizes expensive memory accesses. The numerical performances are illustrated on some thermal and elastic academic problems

Generating Property-Directed Potential Invariants By Backward Analysis

This paper addresses the issue of lemma generation in a k-induction-based formal analysis of transition systems, in the linear real/integer arithmetic fragment. A backward analysis, powered by quantifier elimination, is used to output preimages of the negation of the proof objective, viewed as unauthorized states, or gray states. Two heuristics are proposed to take advantage of this source of information. First, a thorough exploration of the possible partitionings of the gray state space discovers new relations between state variables, representing potential invariants. Second, an inexact exploration regroups and over-approximates disjoint areas of the gray state space, also to discover new relations between state variables. k-induction is used to isolate the invariants and check if they strengthen the proof objective. These heuristics can be used on the first preimage of the backward exploration, and each time a new one is output, refining the information on the gray states. In our context of critical avionics embedded systems, we show that our approach is able to outperform other academic or commercial tools on examples of interest in our application field. The method is introduced and motivated through two main examples, one of which was provided by Rockwell Collins, in a collaborative formal verification framework.Comment: In Proceedings FTSCS 2012, arXiv:1212.657

From creativity to innovation:Understanding and improving the evaluation and selection of ideas in educational settings

Creativity gives students the ability to generate novel and useful ideas for complex problems, but generating creative ideas alone is not enough to solve complex problems. It requires that students are able to evaluate their own and others’ ideas, select ideas to develop further, and abandon those that are unlikely to be successful. However, prior research has shown that students do not recognize creative ideas and have a tendency to reject highly creative ideas, and are more likely to select ideas that are consistent with social norms, and easy to understand. The aim of this thesis is to investigate whether the evaluation and selection of ideas can be improved in educational settings. The research found that students’ ability to evaluate creativity in products depends strongly on their discipline. Furthermore, by exposing students to the task before evaluating others’ ideas they become better able to recognize the creative and original ideas. However, students discard original ideas immediately when they know that they have to implement those ideas

Organizing communication and introspection in a multi-agent blocksworld scenario

The implementation of a simple blocksworld-scenario simulation-program is described. The blocksworld is modeled according to the multi-agent paradigm of distributed artificial intelligence. Each block is viewed as an agent. The agents have capabilities like to move, to communicate, to plan or to gain a small amount of introspective knowledge which are necessary to transform the initial scene of a problem into the goal scene. The structure of the system is oriented along the ideas of the specification of RATMAN described in (BMS91). RATMAN was reduced to its two central modules and their concepts were implemented with means as simple as possible. The result was a system, that allows to experimentally develop concepts for communication, planning and introspection, that are (for this simple toy-domain) sufficient to solve the problems without any global problem solver, but by the cooperative behavior in the society of agents

COLAB : a hybrid knowledge representation and compilation laboratory

Knowledge bases for real-world domains such as mechanical engineering require expressive and efficient representation and processing tools. We pursue a declarative-compilative approach to knowledge engineering. While Horn logic (as implemented in PROLOG) is well-suited for representing relational clauses, other kinds of declarative knowledge call for hybrid extensions: functional dependencies and higher-order knowledge should be modeled directly. Forward (bottom-up) reasoning should be integrated with backward (top-down) reasoning. Constraint propagation should be used wherever possible instead of search-intensive resolution. Taxonomic knowledge should be classified into an intuitive subsumption hierarchy. Our LISP-based tools provide direct translators of these declarative representations into abstract machines such as an extended Warren Abstract Machine (WAM) and specialized inference engines that are interfaced to each other. More importantly, we provide source-to-source transformers between various knowledge types, both for user convenience and machine efficiency. These formalisms with their translators and transformers have been developed as part of COLAB, a compilation laboratory for studying what we call, respectively, "vertical\u27; and "horizontal\u27; compilation of knowledge, as well as for exploring the synergetic collaboration of the knowledge representation formalisms. A case study in the realm of mechanical engineering has been an important driving force behind the development of COLAB. It will be used as the source of examples throughout the paper when discussing the enhanced formalisms, the hybrid representation architecture, and the compilers

An Agent-Based Simulation API for Speculative PDES Runtime Environments

Agent-Based Modeling and Simulation (ABMS) is an effective paradigm to model systems exhibiting complex interactions, also with the goal of studying the emergent behavior of these systems. While ABMS has been effectively used in many disciplines, many successful models are still run only sequentially. Relying on simple and easy-to-use languages such as NetLogo limits the possibility to benefit from more effective runtime paradigms, such as speculative Parallel Discrete Event Simulation (PDES). In this paper, we discuss a semantically-rich API allowing to implement Agent-Based Models in a simple and effective way. We also describe the critical points which should be taken into account to implement this API in a speculative PDES environment, to scale up simulations on distributed massively-parallel clusters. We present an experimental assessment showing how our proposal allows to implement complicated interactions with a reduced complexity, while delivering a non-negligible performance increase