From todd at cs.utexas.edu Mon Sep 26 13:45:29 2011 From: todd at cs.utexas.edu (Todd Hester) Date: Mon Sep 26 12:59:54 2011 Subject: [DevRob] Second CFP: Designing Intelligent Robots: Reintegrating AI Message-ID: Second Call For Papers Designing Intelligent Robots: Reintegrating AI AAAI Spring Symposium 2012 March 26th-28th, Stanford University -- Workshop Website -- http://people.csail.mit.edu/gdk/dir/ -- Workshop Description -- The goal of building intelligent robots has been a motivating problem for generations of AI researchers, going back at least as far as Shakey the robot in 1966. Creating such a robot is both the fully realized expression of the original impulse behind AI and an immensely rich source of research questions that address real-world problems. However, AI is fragmented field: well-developed and largely independent research communities exist for learning, planning, reasoning, language, perception and control. Since the challenges posted by each of these subfields are immense, most researchers have found it necessary to devote their careers to specializing in a single subfield. While immense progress has been made in each of these subfields in the last few decades, it remains unclear how they can be integrated to produce an intelligent robot. Unifying these disparate technologies will open up new avenues of research and create new application opportunities. Therefore, we believe that integration should be considered a valid research endeavor in its own right. This symposium aims to bring together a diverse and multidisciplinary group of researchers interested in the specific objective of designing intelligent robots. The goal of the symposium is to provide common ground for their diverse interests and thereby actively encourage the integration of various AI techniques. We also hope to foster an active discussion about setting a realistic and feasible medium-term objective for integrative research so that progress can be made. The symposium will include invited talks as well as a poster session with ample time for discussion. -- Confirmed Speakers -- Michael Beetz, TU Munich. Christoph Borst, DLR. Dieter Fox, University of Washington (tentative). Nick Hawes, University of Birmingham. Siddhartha Srinivasa, Carnegie Mellon. Marc Toussaint, FU Berlin. -- Call for Contributions -- We invite contributions describing research aimed at producing a functional, integrated robot system. *Papers that provide a high-level overview of existing work or summarize the results of an extended research program along these lines are most welcome, as are papers that integrate two usually distinct areas of research.* Interested participants may submit either full-length papers (up to 6 pages in AAAI format) or short papers/extended abstracts (2 pages) in PDF format to dir.aaai.ss12@gmail.com. -- Important Dates -- Submit manuscripts by: October 7, 2011 Acceptance notification: November 4, 2011 Camera-ready submission: January 20, 2012 Symposium: March 26-28, 2012 -- Organizers -- George Konidaris, Massachusetts Institute of Technology Byron Boots, Carnegie Mellon University Stephen Hart, GM Todd Hester, University of Texas Sarah Osentoski, Bosch Research and Technology Center David Wingate, Massachusetts Institute of Technology -- Contact ? For more information, please contact George Konidaris Email: gdk@csail.mit.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: http://emergent.brynmawr.edu/pipermail/devrob/attachments/20110926/833817d4/attachment.htm From Conrady_Applied_Science_LLC at mail.vresp.com Fri Sep 16 10:08:12 2011 From: Conrady_Applied_Science_LLC at mail.vresp.com (Conrady Applied Science, LLC) Date: Thu Nov 10 20:22:00 2011 Subject: [DevRob] @BayesianNetwork - 9/2011 Newsletter Message-ID: <5d9317bf0d-devrob=developmentalrobotics.org@mail.vresp.com> Click to view this email in a browser http://hosted.vresp.com/810297/5d9317bf0d/1478566911/7321495e85/ @BayesianNetwork - September 2011 Newsletter ---------------------------------------------------------------------------------- White Paper: Causal Inference and Direct Effects with Bayesian Networks http://cts.vresp.com/c/?ConradyAppliedScienc/5d9317bf0d/7321495e85/f0df114db7/utm_content=devrob%40developmentalrobotics.org&utm_source=VerticalResponse&utm_medium=Email&utm_term=Text%20Version%20-%20Link%201&utm_campaign=%40BayesianNetwork%20-%209%2F2011%20Newsletter While economists and social scientists have been using observational data for over a century for policy development, the business world has only recently been discovering the emerging potential of ?big data? and ?competing on analytics.? As these terms are becoming buzzwords, the observational nature of most ?big data? sources is often overlooked. While the mantra of ?correlation does not imply causation? remains frequently quoted as a general warning, many business analysts would not know under what specific conditions it can be acceptable to derive a causal interpretation from correlation in observational data. It is our objective to provide a framework that facilitates a more disciplined approach regarding causal inference while remaining accessible to (non-statistician) business analysts and transparent to executive decision makers. ---------------------------------------------------------------------------------- Upcoming Webinar: Causal Inference and Direct Effects in Marketing Mix Models, October 27, 12 noon CDT (GMT -05:00) http://cts.vresp.com/c/?ConradyAppliedScienc/5d9317bf0d/7321495e85/1a1988e1cd/utm_content=devrob%40developmentalrobotics.org&utm_source=VerticalResponse&utm_medium=Email&utm_term=Text%20Version%20-%20Link%202&utm_campaign=%40BayesianNetwork%20-%209%2F2011%20Newsletter The adage, ?I know I waste half of my advertising dollars...I just wish I knew which half?, reflects a century-old uncertainty about the effectiveness of marketing instruments. More formally, one could describe this quandary as a domain with an unknown (or ill-understood) structure. While ?big data?, especially in the field of marketing, is expected to rapidly yield ?actionable business insights,? we need to recognize that there are many steps to traverse to achieve this goal. One crucial element is the formal transition from observational inference to causal inference. In this webinar, Dr. Lionel Jouffe and Stefan Conrady will demonstrate the benefits of employing Bayesian networks as a robust framework to make the leap from observational data to causal reasoning. Register Here: http://cts.vresp.com/c/?ConradyAppliedScienc/5d9317bf0d/7321495e85/9992ff8966 ---------------------------------------------------------------------------------- BayesiaLab Training in Atlanta http://cts.vresp.com/c/?ConradyAppliedScienc/5d9317bf0d/7321495e85/f6bf83cdf7/utm_content=devrob%40developmentalrobotics.org&utm_source=VerticalResponse&utm_medium=Email&utm_term=Text%20Version%20-%20Link%204&utm_campaign=%40BayesianNetwork%20-%209%2F2011%20Newsletter Dr. Lionel Jouffe, Bayesia's co-founder and CEO, is scheduled to return to the U.S. in October 2011 to host a three-day training seminar on Bayesian Networks and introduces all the innovative features of the latest release of BayesiaLab. Dr. Jouffe will cover all the fundamentals of Bayesian Networks, so no prior knowledge is required other than a basic familiarity with mathematical and statistical concepts. ---------------------------------------------------------------------------------- ACM Data Mining Camp http://cts.vresp.com/c/?ConradyAppliedScienc/5d9317bf0d/7321495e85/4e924bf870 On October 15, the ACM San Francisco Bay Area Chapter will host the Data Mining Camp at the eBay campus in San Jose, CA. As in the previous year, Conrady Applied Science and BayesiaLab will once again support this event as a Gold Sponsor. Last year's Data Mining Camp was a hugely successful event with nearly 400 participants. For anyone interested in machine learning and knowledge discovery, this event is a must. ---------------------------------------------------------------------------------- White Paper: Knowledge Discovery in the Stock Market http://cts.vresp.com/c/?ConradyAppliedScienc/5d9317bf0d/7321495e85/957b83fbd7/utm_content=devrob%40developmentalrobotics.org&utm_source=VerticalResponse&utm_medium=Email&utm_term=Text%20Version%20-%20Link%206&utm_campaign=%40BayesianNetwork%20-%209%2F2011%20Newsletter We utilize the unsupervised and supervised learning algorithms of the BayesiaLab software package to automatically generate Bayesian networks from daily stock returns over a six-year period. We will examine 459 stocks from the S&P 500 index, for which observations are available over the entire timeframe. In addition to generating human-readable and interpretable structures, we want to illustrate how we can immediately use machine-learned Bayesian networks as ?computable knowledge? for automated inference and prediction. Our objective is to gain both a qualitative and quantitative understanding of the stock market by using Bayesian networks. In the quantitative context, we will also show how BayesiaLab can reliably carry out inference with multiple pieces of uncertain and even conflicting evidence. The inherent ability of Bayesian networks to perform computations under uncertainty makes them highly suitable for a wide range of real-world applications. ---------------------------------------------------------------------------------- BayesiaLab 5.0 Professional http://cts.vresp.com/c/?ConradyAppliedScienc/5d9317bf0d/7321495e85/54aff16bda/utm_content=devrob%40developmentalrobotics.org&utm_source=VerticalResponse&utm_medium=Email&utm_term=Text%20Version%20-%20Link%207&utm_campaign=%40BayesianNetwork%20-%209%2F2011%20Newsletter BayesiaLab is a powerful desktop application (Windows/Mac/Unix) for knowledge discovery, data mining, analytics, predictive modeling and simulation - all based on the paradigm of Bayesian networks. Bayesian networks have become a very powerful tool for deep understanding of very complex, high-dimensional problem domains, ranging from biomedical research to marketing science. BayesiaLab is the world's only comprehensive software package for learning, editing and analyzing Bayesian networks. It provides perhaps the easiest way to practically apply artificial intelligence tools, thus transforming and, more importantly, massively accelerating research workflows. ---------------------------------------------------------------------------------- Conrady Applied Science, LLC North American Sales and Consulting Partner of Bayesia S.A.S. +1-888-386-8383 info@conradyscience.com www.conradyscience.com twitter.com/bayesiannetwork facebook.com/conradyscience ______________________________________________________________________ If you no longer wish to receive these emails, please reply to this message with "Unsubscribe" in the subject line or simply click on the following link: http://cts.vresp.com/u?5d9317bf0d/7321495e85/mlpftw ______________________________________________________________________ Click below to forward this email to a friend: http://oi.vresp.com/f2af/v4/send_to_friend.html?ch=5d9317bf0d&lid=1478566911&ldh=7321495e85 ______________________________________________________________________ This message was sent by Conrady Applied Science, LLC using VerticalResponse Conrady Applied Science, LLC 312 Hamlet's End Way Franklin, Tennessee 37067 US Read the VerticalResponse for Group Edition marketing policy: http://www.verticalresponse.com/content/pm_policy.html -------------- next part -------------- An HTML attachment was scrubbed... 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