25,012 research outputs found
James J. Kaput (1942ā2005) imagineer and futurologist of mathematics education
Jim Kaput lived a full life in mathematics education and we have many reasons to be grateful to him, not only for his vision of the use of technology in mathematics, but also for his fundamental humanity. This paper considers the origins of his ābig ideasā as he lived through the most amazing innovations in technology that have changed our lives more in a generation than in many centuries before. His vision continues as is exemplified by the collected papers in this tribute to his life and work
On Partially Controlled Multi-Agent Systems
Motivated by the control theoretic distinction between controllable and
uncontrollable events, we distinguish between two types of agents within a
multi-agent system: controllable agents, which are directly controlled by the
system's designer, and uncontrollable agents, which are not under the
designer's direct control. We refer to such systems as partially controlled
multi-agent systems, and we investigate how one might influence the behavior of
the uncontrolled agents through appropriate design of the controlled agents. In
particular, we wish to understand which problems are naturally described in
these terms, what methods can be applied to influence the uncontrollable
agents, the effectiveness of such methods, and whether similar methods work
across different domains. Using a game-theoretic framework, this paper studies
the design of partially controlled multi-agent systems in two contexts: in one
context, the uncontrollable agents are expected utility maximizers, while in
the other they are reinforcement learners. We suggest different techniques for
controlling agents' behavior in each domain, assess their success, and examine
their relationship.Comment: See http://www.jair.org/ for any accompanying file
ada: An R Package for Stochastic Boosting
Boosting is an iterative algorithm that combines simple classification rules with "mediocre" performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.
A Case Study in Coordination Programming: Performance Evaluation of S-Net vs Intel's Concurrent Collections
We present a programming methodology and runtime performance case study
comparing the declarative data flow coordination language S-Net with Intel's
Concurrent Collections (CnC). As a coordination language S-Net achieves a
near-complete separation of concerns between sequential software components
implemented in a separate algorithmic language and their parallel orchestration
in an asynchronous data flow streaming network. We investigate the merits of
S-Net and CnC with the help of a relevant and non-trivial linear algebra
problem: tiled Cholesky decomposition. We describe two alternative S-Net
implementations of tiled Cholesky factorization and compare them with two CnC
implementations, one with explicit performance tuning and one without, that
have previously been used to illustrate Intel CnC. Our experiments on a 48-core
machine demonstrate that S-Net manages to outperform CnC on this problem.Comment: 9 pages, 8 figures, 1 table, accepted for PLC 2014 worksho
A gentle transition from Java programming to Web Services using XML-RPC
Exposing students to leading edge vocational areas of relevance such as Web Services can be difficult. We show a lightweight approach by embedding a key component of Web Services within a Level 3 BSc module in Distributed Computing. We present a ready to use collection of lecture slides and student activities based on XML-RPC. In
addition we show that this material addresses the central topics in the context of web services as identified by Draganova (2003)
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