From db30 at andrew.cmu.edu Wed Jan 2 16:52:10 2019 From: db30 at andrew.cmu.edu (db30 at andrew.cmu.edu) Date: Wed, 2 Jan 2019 16:52:10 -0500 Subject: [ACT-R-users] New ACT-R software Message-ID: <6A5BDE5FDC4265906368793B@actr6b.psy.cmu.edu> The ACT-R 7 software available from the Software page on the ACT-R website has been updated. The current version is now 7.12.10-<2762:2018-12-20>. This is the stable version of the prototype that was made available last year: More information can also be found in the slides from the 2017 and 2018 Workshops: This is a significant update to the software which is likely going to require updates to existing experiment/task code to be able to run it with this version. There is a link to a commented updating of the demo2 task from the tutorial on the software page as an example. For those interested in using the remote interface, in addition to the remote manual found in the docs directory and the Python code included with the tutorial, the examples/connections directory contains some very simple examples of connecting to the ACT-R interface server using: C, Java, Lisp, MATLAB, Node.js, Python, R, and Tcl/Tk. If you would like to continue using the previous version, 7.5-<2244:2017-07-11>, it will remain available from the old software page: If you have any questions or problems with the new version please let me know. Dan From pblouw at uwaterloo.ca Thu Jan 3 14:52:18 2019 From: pblouw at uwaterloo.ca (Peter Blouw) Date: Thu, 3 Jan 2019 14:52:18 -0500 Subject: [ACT-R-users] Call for applications - 2019 Nengo Summer School Message-ID: [All details about this school can be found online at https://www.nengo.ai/summerschool] The Centre for Theoretical Neuroscience at the University of Waterloo is excited to announce our 6th annual Nengo summer school on large-scale brain modelling and neuromorphic computing. This two-week school will teach participants to use the Nengo simulation package to build state-of-the-art cognitive and neural models to run both in simulation and on neuromorphic hardware. Summer school participants will be given on-site access to Loihi, Intel?s new neuromorphic research chip [1], and will learn to run high-level applications on Loihi using Nengo! More generally, Nengo provides users with a versatile and powerful environment for designing cognitive and neural systems, and has been used to build what is currently the world's largest functional brain model, Spaun [2], which includes spiking deep learning, reinforcement learning, adaptive motor control, and cognitive control networks. For a look at last year's summer school, check out this short video: https://www.youtube.com/watch?v=NwtYgBB2N6I We welcome applications from all interested graduate students, postdocs, professors, and industry professionals with a relevant background. [1] Davies, et al. (2018). Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro. Vol. 38 no. 1 pp. 82-99. [ https://ieeexplore.ieee.org/document/8259423] [2] Eliasmith, C., Stewart T. C., Choo X., Bekolay T., DeWolf T., Tang Y., Rasmussen, D. (2012). A large-scale model of the functioning brain. Science. Vol. 338 no. 6111 pp. 1202-1205. DOI: 10.1126/science.1225266. [ http://compneuro.uwaterloo.ca/files/publications/eliasmith.2012.pdf] ***Application Deadline: February 15, 2019*** Format: A combination of tutorials and project-based work. Participants are encouraged to bring their own ideas for projects, which may focus on testing hypotheses, modeling neural or cognitive data, implementing specific behavioural functions with neurons, expanding past models, or providing a proof-of-concept of various neural mechanisms. Hands-on tutorials, work on individual or group projects, and talks from invited faculty members will make up the bulk of day-to-day activities. A project demonstration event will be held on the last day of the school, with prizes for strong projects! Participants will have the opportunity to learn how to: - interface Nengo with neuromorphic hardware (e.g. Loihi, SpiNNaker) - build perceptual, motor, and cognitive models using spiking neurons - model anatomical, electrophysiological, cognitive, and behavioural data - use a variety of single cell models within a large-scale model - integrate machine learning methods into biologically oriented models - interface Nengo with cameras and robotic systems - implement modern nonlinear control methods in neural models - and much more? Date and Location: June 9th to June 21st, 2019 at the University of Waterloo, Ontario, Canada. Applications: Please visit http://www.nengo.ai/summerschool, where you can find more information regarding costs, travel, lodging, along with an application form listing required materials. If you have any questions about the school or the application process, please contact Peter Blouw (peter.blouw at appliedbrainresearch.com). The school is also partly supported by ONR and ABR, Inc. We look forward to hearing from you! -------------- next part -------------- An HTML attachment was scrubbed... URL: From amir.aly at em.ci.ritsumei.ac.jp Fri Jan 4 06:04:48 2019 From: amir.aly at em.ci.ritsumei.ac.jp (Amir Aly) Date: Fri, 4 Jan 2019 20:04:48 +0900 Subject: [ACT-R-users] SoAIR 2019 [Deadline Approaching]: JST-CREST / IEEE-RAS Spring School on "Social and Artificial Intelligence for User-Friendly Robots" in Japan Message-ID: CALL FOR APPLICATIONS **Apologies for cross posting ** We are pleased to call for applications for the *JST-CREST / IEEE-RAS* spring school on: "*Social and Artificial Intelligence for User-Friendly Robots*" * Which will be held from 17-24 March, 2019 in Shonan Village, Japan *after the *Human-Robot Interaction (HRI)* conference that will be held in the near South Korea. *Webpage: **https://inic8.bitbucket.io/SoAIR19/* *I. Aim and Scope *Autonomous and intelligent systems are progressively moving into spaces, which have previously been predominantly shaped by human agency. Unlike in the past where machines obediently served their human operators, machines now increasingly act without the intervention of a human. Artificial intelligence is meeting new challenges in the world, though human-like intelligence may still be a distant goal. Robots in factories are coming out of their cages. Autonomous cars are being tested on streets with regular human-driven cars. The private household is changing with the appearance of not only robotic vacuum cleaners, but also with the first-generation of social robots and smart devices. The challenges that face both the robotics and artificial intelligence communities are how the necessary intelligence for such new environments can be created as well as how to make artificial agents capable of not only solving tasks at hand but also considering social environments around them during interaction with human users so as to behave appropriately. Within the school, we plan to address the tension created by the balance between task-specific artificial intelligence and the demands of sociability required to function effectively in human-centered environments. ** *Who should apply?* We invite *Masters and PhD students* as well as *post-doctoral candidates and researchers from industry* with relevant research background to apply for this spring school. *Additional support would be available based on eligibility.* ** *This spring school is a Technical Education Program (TEP) endorsed and supported by JST-CREST / IEEE-RAS*. ** *The school aims at bridging the gap between social and cognitive Human-Robot Interaction (HRI), Artificial Intelligence (AI), and Autonomous Vehicles (AV) through high level talks, tutorials, and hands-on workshops (the program will be announced soon).* *II. Keynote Speakers: * 1. * Takayuki Nagai, *Osaka University, Japan 2. * Jun Tani *? Okinawa Institute of Science and Technology (OIST), Japan 3. *Daniele Magazzeni *? King's College London, UK 4. * Yukie Nagai *? National Institute of Information and Communications Technology (NICT), Japan 5. * Tetsuya Ogata* ? Waseda University, Japan 6. *Mohamed Chetouani *? University of Pierre and Marie Curie (UPMC), France 7. *Silvia Rossi* ? University of Naples, Italy 8. *Agnieszka Wykowska *? Italian Institute of Technology (IIT), Italy 9. *Tetsunari Inamura *? National Institute of Informatics (NII), Japan 10. *Amit Kumar Pandey *? SoftBank (Aldebaran) Robotics, France 11. *Mehul Bhatt* ? Orebro University, Sweden (Tutorial) 12. *Mohsen Kaboli ?* Bavarian Motor Works (BMW), Germany (Tutorial) 13. *Francesco Maurelli *? Jacobs University, Germany (Preparing Marie Curie funding proposal) 14. *Atsuko Nakatsuka* ? Japan Society for the Promotion of Science (JSPS), Japan (Preparing JSPS funding proposal) *III. Submission * The applications must include the following files (combined into one file). No other documents would be necessary (More information is available on the school's webpage). 1. *Curriculum vitae*: A CV detailing relevant aspects of the candidate's academic career that demonstrates her/his relevance to the school theme. Please consider including the list of publications. 2. *Research abstract*: A 200-word research abstract that the candidate intends to present during the school. 3. *Letter of recommendation*: A brief letter from the academic advisor or the employer of the candidate supporting her/his application. Please also indicate if additional support would be required to attend the school. *Application submission*: Please use the following EasyChair web link:* Application Submission .* *IV. Important Dates * 1. Application submission: *12-January, 2019 * 2. Notification of acceptance: *16-January, 2019 * 3. Spring School: *17-24 March, 2019* *V. Organizers * 1. *Amir Aly *? Ritsumeikan University ? Japan 2. * Shashank Pathak *? Visteon Corporation, Germany 3. *Franziska Kirstein *? Blue Ocean Robotics, Denmark --------------------- *Amir Aly, Ph.D.* Senior Researcher Emergent Systems Laboratory College of Information Science and Engineering Ritsumeikan University 1-1-1 Noji Higashi, Kusatsu, Shiga 525-8577 Japan -------------- next part -------------- An HTML attachment was scrubbed... URL: From coty at cmu.edu Mon Jan 21 13:02:10 2019 From: coty at cmu.edu (Cleotilde Gonzalez) Date: Mon, 21 Jan 2019 18:02:10 +0000 Subject: [ACT-R-users] Post-Doctoral Fellow Position Available at Carnegie Mellon University In-Reply-To: <5cfb54fc27df448986f2da1617c027c6@cmu.edu> References: <5cfb54fc27df448986f2da1617c027c6@cmu.edu> Message-ID: <6ae3dd9527174bf6b8b3469b5636286e@cmu.edu> Dear Colleagues: Please see below and attached, an open Post-doctoral fellow position available in my lab. Please share with anybody that would be interested. Best Regards, Prof. Cleotilde Gonzalez Research Professor of Decision Science Founding Director of Dynamic Decision Making Laboratory Social and Decision Sciences Department Carnegie Mellon University 5000 Forbes Ave. Porter Hall 208 Pittsburgh, PA, 15213 phone: 412-268-6242 Fax: 412-268-6938 E-mail: coty at cmu.edu www.cmu.edu/ddmlab --- Post-doctoral fellow position Dynamic Decision Making Laboratory Department of Social and Decision Sciences Carnegie Mellon University (CMU) http://www.cmu.edu/ddmlab/ The Dynamic Decision Making Laboratory (DDMLab: http://www.cmu.edu/ddmlab/) at Carnegie Mellon University is seeking applications for a post-doctoral fellow position. The start date is flexible from May 1, 2019 to August 1, 2019. The position is for one year with possibility of renewal to a second year according to performance and availability of funds. We are looking for enthusiastic Ph.D. recent graduates who have passion for basic science and that want to make an impact in our society while working on important applied problems. The position is highly interdisciplinary, but we expect that applicants with backgrounds in Computer or Industrial Engineering as well as in Behavioral and Cognitive Sciences be the best fit to this position. The Post-doctoral fellow will support the design of experimental studies involving one or more (networked) participants; and will also need to be engaged in the development of computational models of individual and collective behavior. The position will involve collaborations with Prof. Gonzalez and with other well-recognized researchers in various institutions. Required qualifications: -- A Ph.D. (completed by start of employment) in cognitive or behavioral psychology, human factors engineering, or equivalent. Technical background is required; for example, a Ph.D. in social psychology with no other degree in Engineering or Computer Science would not be acceptable. -- Training in behavioral science research methods and statistical analyses. -- Experience with statistical software (preferably R, others acceptable). -- Experience with computational/cognitive modeling (e.g., reinforcement learning, ACT-R models, IBL models). -- Demonstrable writing abilities through publications and good communication skills. Desired qualifications: -- Experience with programming (preferably Python, others acceptable) -- Experience with web programming and design -- Experience working with interdisciplinary, collaborative teams, and managing research assistants. Duration: This is a full time research position with full benefits, for one year with a possibility of renewal for one more additional year conditional on performance and availability of funds. To apply: please send a letter of interest, curriculum vitae, relevant journal articles, and let referees send three letters of reference before March 1, 2019. Please send electronic documents (Word, Pdf) to: coty at cmu.edu. Applications will be reviewed as they arrive. The DDMLab is part of the Department of Social and Decision Sciences at Carnegie Mellon University, a research paradise. CMU is located in Pittsburgh, Pennsylvania, one of America's most livable cities. The city has a strong university presence with over a dozen colleges and campuses and a great cultural scene. Carnegie Mellon is an equal opportunity/affirmative action employer. For more information on our Equal Employment/Affirmative Action Policy and our Statement of Assurance, go to: http://www.cmu.edu/policies/documents/SoA.html -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Callforpostdoc-2019.pdf Type: application/pdf Size: 17721 bytes Desc: Callforpostdoc-2019.pdf URL: From springschool at rug.nl Mon Jan 21 07:44:03 2019 From: springschool at rug.nl (Spring School, FA) Date: Mon, 21 Jan 2019 13:44:03 +0100 Subject: [ACT-R-users] Error-driven learning at the Groningen Spring School on Cognitive Modeling Message-ID: *Update: New tutorial on Error-driven learning* Fourth Groningen Spring School on Cognitive Modeling *? ACT-R, Nengo, PRIMs, & Error-driven learning?* Date: April 8-12, 2019 Location: Groningen, the Netherlands Fee: ? 250 (late fee after February 15 will be ? 300) More information and registration: *www.cognitive-modeling.com/springschool * As briefly announced earlier, this year we will be offering a new tutorial on error-driven learning during the spring school. Error-driven learning (also called discrimination learning) allows to simulate the time course of learning. It can be applied for all domains in cognitive science, but is especially useful for modeling language processing and language learning. More information about this simple and elegant approach can now be found on our website . As in previous years, the Spring School will also cover the ACT-R, Nengo, and PRIMs paradigms. A preliminary version of the program can now be found on our website. The *early registration deadline ends on February 15*, so make sure to sign up before then. Please let us know if you have any questions or check out our website for more information. Best regards, the spring school team Please feel free to forward the information to anyone who might be interested in the Spring School. ______________ *Error-driven learning* Teachers: Jacolien van Rij and Doroth?e Hoppe (University of Groningen) Error-driven learning (also called discrimination learning) allows to simulate the time course of learning. It is based on the Rescorla-Wagner model (Rescorla & Wagner, 1972) for animal cognition, which assumes that learning is driven by expectation error, instead of behaviorist association (Rescorla, 1988). The equations formulated by Rescorla and Wagner have been used to investigate different aspects of cognition, including language acquisition (e.g., Hsu, Chater, and Vit?nyi, 2011; St. Clair, Monaghan, and Ramscar, 2009), second language learning (Ellis, 2006), and reading of complex words (Baayen et al, 2011). Although error-driven learning can be applied for all domains in cognitive science, in this course we will focus on how it could be used for modeling language processing and language learning. *ACT-R* Teachers: Jelmer Borst & Katja Mehlhorn (University of Groningen) Website: http://act-r.psy.cmu.edu. ACT-R is a high-level cognitive theory and simulation system for developing cognitive models for tasks that vary from simple reaction time experiments to driving a car, learning algebra, and air traffic control. ACT-R can be used to develop process models of a task at a symbolic level. Participants will follow a compressed five-day version of the traditional summer school curriculum. We will also cover the connection between ACT-R and fMRI. *Nengo* Teacher: Terry Stewart (University of Waterloo) Website: http://www.nengo.ca Nengo is a toolkit for converting high-level cognitive theories into low-level spiking neuron implementations. In this way, aspects of model performance such as response accuracy and reaction times emerge as a consequence of neural parameters such as the neurotransmitter time constants. It has been used to model adaptive motor control, visual attention, serial list memory, reinforcement learning, Tower of Hanoi, and fluid intelligence. Participants will learn to construct these kinds of models, starting with generic tasks like representing values and positions, and ending with full production-like systems. There will also be special emphasis on extracting various forms of data out of a model, such that it can be compared to experimental data. *PRIMs* Teacher: Niels Taatgen (University of Groningen) Website: http://www.ai.rug.nl/~niels/actransfer.html How do people handle and prioritize multiple tasks? How can we learn something in the context of one task, and partially benefit from it in another task? The goal of PRIMs is to cross the artificial boundary that most cognitive architectures have imposed on themselves by studying single tasks. It has mechanisms to model transfer of cognitive skills, and the competition between multiple goals. In the tutorial we will look at how PRIMs can model phenomena of cognitive transfer and cognitive training, and how multiple goals compete for priority in models of distraction. -------------- next part -------------- An HTML attachment was scrubbed... URL: