2,592 research outputs found
Revisiting the Core Ontology and Problem in Requirements Engineering
In their seminal paper in the ACM Transactions on Software Engineering and
Methodology, Zave and Jackson established a core ontology for Requirements
Engineering (RE) and used it to formulate the "requirements problem", thereby
defining what it means to successfully complete RE. Given that stakeholders of
the system-to-be communicate the information needed to perform RE, we show that
Zave and Jackson's ontology is incomplete. It does not cover all types of basic
concerns that the stakeholders communicate. These include beliefs, desires,
intentions, and attitudes. In response, we propose a core ontology that covers
these concerns and is grounded in sound conceptual foundations resting on a
foundational ontology. The new core ontology for RE leads to a new formulation
of the requirements problem that extends Zave and Jackson's formulation. We
thereby establish new standards for what minimum information should be
represented in RE languages and new criteria for determining whether RE has
been successfully completed.Comment: Appears in the proceedings of the 16th IEEE International
Requirements Engineering Conference, 2008 (RE'08). Best paper awar
Concurrent bandits and cognitive radio networks
We consider the problem of multiple users targeting the arms of a single
multi-armed stochastic bandit. The motivation for this problem comes from
cognitive radio networks, where selfish users need to coexist without any side
communication between them, implicit cooperation or common control. Even the
number of users may be unknown and can vary as users join or leave the network.
We propose an algorithm that combines an -greedy learning rule with a
collision avoidance mechanism. We analyze its regret with respect to the
system-wide optimum and show that sub-linear regret can be obtained in this
setting. Experiments show dramatic improvement compared to other algorithms for
this setting
An efficient algorithm for learning with semi-bandit feedback
We consider the problem of online combinatorial optimization under
semi-bandit feedback. The goal of the learner is to sequentially select its
actions from a combinatorial decision set so as to minimize its cumulative
loss. We propose a learning algorithm for this problem based on combining the
Follow-the-Perturbed-Leader (FPL) prediction method with a novel loss
estimation procedure called Geometric Resampling (GR). Contrary to previous
solutions, the resulting algorithm can be efficiently implemented for any
decision set where efficient offline combinatorial optimization is possible at
all. Assuming that the elements of the decision set can be described with
d-dimensional binary vectors with at most m non-zero entries, we show that the
expected regret of our algorithm after T rounds is O(m sqrt(dT log d)). As a
side result, we also improve the best known regret bounds for FPL in the full
information setting to O(m^(3/2) sqrt(T log d)), gaining a factor of sqrt(d/m)
over previous bounds for this algorithm.Comment: submitted to ALT 201
Uso do resĂduo da produção de Beddingia siricidicola para a produção de inoculante de Trichoderma viride.
Organizado por Patricia PĂłvoa de Mattos, Celso Garcia Auer, Rejane Stumpf Sberze, Katia Regina Pichelli e Paulo CĂ©sar Botosso
Magnetization reversal times in the 2D Ising model
We present a theoretical framework which is generally applicable to the study
of time scales of activated processes in systems with Brownian type dynamics.
This framework is applied to a prototype system: magnetization reversal times
in the 2D Ising model. Direct simulation results for the magnetization reversal
times, spanning more than five orders of magnitude, are compared with
theoretical predictions; the two agree in most cases within 20%.Comment: 9 pages, 8 figure
Incentivizing Exploration with Heterogeneous Value of Money
Recently, Frazier et al. proposed a natural model for crowdsourced
exploration of different a priori unknown options: a principal is interested in
the long-term welfare of a population of agents who arrive one by one in a
multi-armed bandit setting. However, each agent is myopic, so in order to
incentivize him to explore options with better long-term prospects, the
principal must offer the agent money. Frazier et al. showed that a simple class
of policies called time-expanded are optimal in the worst case, and
characterized their budget-reward tradeoff.
The previous work assumed that all agents are equally and uniformly
susceptible to financial incentives. In reality, agents may have different
utility for money. We therefore extend the model of Frazier et al. to allow
agents that have heterogeneous and non-linear utilities for money. The
principal is informed of the agent's tradeoff via a signal that could be more
or less informative.
Our main result is to show that a convex program can be used to derive a
signal-dependent time-expanded policy which achieves the best possible
Lagrangian reward in the worst case. The worst-case guarantee is matched by
so-called "Diamonds in the Rough" instances; the proof that the guarantees
match is based on showing that two different convex programs have the same
optimal solution for these specific instances. These results also extend to the
budgeted case as in Frazier et al. We also show that the optimal policy is
monotone with respect to information, i.e., the approximation ratio of the
optimal policy improves as the signals become more informative.Comment: WINE 201
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