8,339 research outputs found
SAT-based Analysis, (Re-)Configuration & Optimization in the Context of Automotive Product documentation
Es gibt einen steigenden Trend hin zu kundenindividueller Massenproduktion (mass customization), insbesondere im Bereich der Automobilkonfiguration. Kundenindividuelle Massenproduktion fĂŒhrt zu einem enormen Anstieg der KomplexitĂ€t. Es gibt Hunderte von Ausstattungsoptionen aus denen ein Kunde wĂ€hlen kann um sich sein persönliches Auto zusammenzustellen. Die Anzahl der unterschiedlichen konfigurierbaren Autos eines deutschen Premium-Herstellers liegt fĂŒr ein Fahrzeugmodell bei bis zu 10^80.
SAT-basierte Methoden haben sich zur Verifikation der StĂŒckliste (bill of materials) von Automobilkonfigurationen etabliert. Carsten Sinz hat Mitte der 90er im Bereich der SAT-basierten Verifikationsmethoden fĂŒr die Daimler AG Pionierarbeit geleistet. Darauf aufbauend wurde nach 2005 ein produktives Software System bei der Daimler AG installiert. SpĂ€ter folgten weitere deutsche Automobilhersteller und installierten ebenfalls SAT-basierte Systeme zur Verifikation ihrer StĂŒcklisten.
Die vorliegende Arbeit besteht aus zwei Hauptteilen. Der erste Teil beschĂ€ftigt sich mit der Entwicklung weiterer SAT-basierter Methoden fĂŒr Automobilkonfigurationen. Wir zeigen, dass sich SAT-basierte Methoden fĂŒr interaktive Automobilkonfiguration eignen. Wir behandeln unterschiedliche Aspekte der interaktiven Konfiguration. Darunter KonsistenzprĂŒfung, Generierung von Beispielen, ErklĂ€rungen und die Vermeidung von Fehlkonfigurationen. AuĂerdem entwickeln wir SAT-basierte Methoden zur Verifikation von dynamischen Zusammenbauten. Ein dynamischer Zusammenbau reprĂ€sentiert die chronologische Zusammenbau-Reihenfolge komplexer Teile.
Der zweite Teil beschĂ€ftigt sich mit der Optimierung von Automobilkonfigurationen. Wir erlĂ€utern und vergleichen unterschiedliche Optimierungsprobleme der Aussagenlogik sowie deren algorithmische LösungsansĂ€tze. Wir beschreiben AnwendungsfĂ€lle aus der Automobilkonfiguration und zeigen wie diese als aussagenlogisches Optimierungsproblem formalisiert werden können. Beispielsweise möchte man zu einer Menge an AusstattungswĂŒnschen ein Test-Fahrzeug mit minimaler ErgĂ€nzung weiterer Ausstattungen berechnen um Kosten zu sparen. DesWeiteren beschĂ€ftigen wir uns mit der Problemstellung eine kleinste Menge an Fahrzeugen zu berechnen um eine Testmenge abzudecken.
Im Rahmen dieser Arbeit haben wir einen Prototypen eines (Re-)Konfigurators, genannt AutoConfig, entwickelt. Unser (Re-)Konfigurator verwendet im Kern SAT-basierte Methoden und besitzt eine grafische BenutzeroberflĂ€che, welche interaktive Konfiguration erlaubt. AutoConfig kann mit Instanzen von drei groĂen deutschen Automobilherstellern umgehen, aber ist nicht alleine darauf beschrĂ€nkt. Mit Hilfe dieses Prototyps wollen wir die Anwendbarkeit unserer Methoden demonstrieren
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
Given the increasing popularity of customer service dialogue on Twitter,
analysis of conversation data is essential to understand trends in customer and
agent behavior for the purpose of automating customer service interactions. In
this work, we develop a novel taxonomy of fine-grained "dialogue acts"
frequently observed in customer service, showcasing acts that are more suited
to the domain than the more generic existing taxonomies. Using a sequential
SVM-HMM model, we model conversation flow, predicting the dialogue act of a
given turn in real-time. We characterize differences between customer and agent
behavior in Twitter customer service conversations, and investigate the effect
of testing our system on different customer service industries. Finally, we use
a data-driven approach to predict important conversation outcomes: customer
satisfaction, customer frustration, and overall problem resolution. We show
that the type and location of certain dialogue acts in a conversation have a
significant effect on the probability of desirable and undesirable outcomes,
and present actionable rules based on our findings. The patterns and rules we
derive can be used as guidelines for outcome-driven automated customer service
platforms.Comment: 13 pages, 6 figures, IUI 201
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
Optimization is commonly employed to determine the content of web pages, such
as to maximize conversions on landing pages or click-through rates on search
engine result pages. Often the layout of these pages can be decoupled into
several separate decisions. For example, the composition of a landing page may
involve deciding which image to show, which wording to use, what color
background to display, etc. Such optimization is a combinatorial problem over
an exponentially large decision space. Randomized experiments do not scale well
to this setting, and therefore, in practice, one is typically limited to
optimizing a single aspect of a web page at a time. This represents a missed
opportunity in both the speed of experimentation and the exploitation of
possible interactions between layout decisions.
Here we focus on multivariate optimization of interactive web pages. We
formulate an approach where the possible interactions between different
components of the page are modeled explicitly. We apply bandit methodology to
explore the layout space efficiently and use hill-climbing to select optimal
content in realtime. Our algorithm also extends to contextualization and
personalization of layout selection. Simulation results show the suitability of
our approach to large decision spaces with strong interactions between content.
We further apply our algorithm to optimize a message that promotes adoption of
an Amazon service. After only a single week of online optimization, we saw a
21% conversion increase compared to the median layout. Our technique is
currently being deployed to optimize content across several locations at
Amazon.com.Comment: KDD'17 Audience Appreciation Awar
Strategic Project Portfolio Management by Predicting Project Performance and Estimating Strategic Fit
Candidate project selections are extremely crucial for infrastructure construction companies. First, they determine how well the planned strategy will be realized during the following years. If the selected projects do not align with the competences of the organization major losses can occur during the projectsâ execution phase. Second, participating in tendering competitions is costly manual labour and losing the bid directly increase the overhead costs of the organization. Still, contractors rarely utilize statistical methods to select projects that are more likely to be successful. In response to these two issues, a tool for project portfolio selection phase was developed based on existing literature about strategic fit estimation and project performance prediction.
One way to define the strategic fit of a project is to evaluate the alignment between the characteristics of a project to the strategic objectives of an organisation. Project performance on the other-hand can be measured with various financial, technical, production, risk or human-resource related criteria. Depending on which measure is highlighted, the likelihood of succeeding with regards to a performance measure can be predicted with numerous machine learning methods of which decision trees were used in this study. By combining the strategic fit and likelihood of success measures, a two-by-two matrix was formed. The matrix can be used to categorize the project opportunities into four categories, ignore, analyse, cash-in and focus, that can guide candidate project selections.
To test and demonstrate the performance of the matrix, the case companyâs CRM data was used to estimate strategic fit and likelihood of succeeding in tendering competitions. First, the projects were plotted on the matrix and their position and accuracy was analysed per quartile. Afterwards, the project selections were simulated and compared against the case companyâs real selections during a six-month period.
The first implication after plotting the projects on the matrix was that only a handful of projects were positioned in the focus category of the matrix, which indicates a discrepancy between the planned strategy and the competences of the case company in tendering competitions. Second, the tendering competition outcomes were easier to predict in the low strategic fit quartiles as the project selections in them were more accurate than in the high strategic fit categories. Finally, the matrix also quite accurately filtered the worst low strategic fit projects out from the market.
The simulation was done in two stages. First, by emphasizing the likelihood of success predictions the matrix increased the hit rate and average strategic fit of the selected project portfolio. When strategic fit values were emphasized on the other hand, the simulation did not yield useful results.
The study contributes to the project portfolio management literature by developing a practice-oriented tool that emphasizes the strategical and statistical perspectives of the candidate project selection phase
Salience and Market-aware Skill Extraction for Job Targeting
At LinkedIn, we want to create economic opportunity for everyone in the
global workforce. To make this happen, LinkedIn offers a reactive Job Search
system, and a proactive Jobs You May Be Interested In (JYMBII) system to match
the best candidates with their dream jobs. One of the most challenging tasks
for developing these systems is to properly extract important skill entities
from job postings and then target members with matched attributes. In this
work, we show that the commonly used text-based \emph{salience and
market-agnostic} skill extraction approach is sub-optimal because it only
considers skill mention and ignores the salient level of a skill and its market
dynamics, i.e., the market supply and demand influence on the importance of
skills. To address the above drawbacks, we present \model, our deployed
\emph{salience and market-aware} skill extraction system. The proposed \model
~shows promising results in improving the online performance of job
recommendation (JYMBII) ( job apply) and skill suggestions for job
posters ( suggestion rejection rate). Lastly, we present case studies to
show interesting insights that contrast traditional skill recognition method
and the proposed \model~from occupation, industry, country, and individual
skill levels. Based on the above promising results, we deployed the \model
~online to extract job targeting skills for all M job postings served at
LinkedIn.Comment: 9 pages, to appear in KDD202
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