The 11th ICCL summer school “Bridging the Gap between Human and Automated Reasoning” is a platform for knowledge transfer within the rapidly increasing research communities in the field of “Computational Logic”, i.e. logic based Artificial Intelligence, and “Human Reasoning”, i.e. Cognitive Science. We will offer introductory courses covering the fundamentals of cognitive science, logic and reasoning, courses at advanced levels, as well as applied courses and workshops dedicated to specialized topics and the state of the art. Among others, the lecturers will be Ruth Byrne, Emmanuelle-Anna Dietz Saldanha, Ulrich Furbach, Sarah Gaggl, Steffen Hölldobler und Marco Ragni. Furthermore, there will be a social program, which includes a Dresden city tour, an excursion to Pirna and to the saxon switzerland, a visit to the green vault in the Dresden Royal Palace and a gala dinner.
The summer school is supported by the German Academic Exchange Service (DAAD) and therefore, a limited number of grants for students and university employees will be available, which includes a waiver for the participation fee.
The deadline for applications is April 30, 2017.
Note that the 10. ICCL summer school has won the Dresden Congress Award in 2016.
You can find more information about the summer school here:
and register here:
First Workshop on Forgetting in Artificial Intelligence
Held at KI 2017
September 25/26, 2017
Though forgetting is usually a nuisance in everyday life, it is an important
feature for human life. On the one hand, forgetting superfluous information
facilitates the task at hand. On the other hand, it is an integral part of
basic cognitive processes, like generalization, abstraction, and learning.
In this context, forgetting is the deliberate act to abolish unnecessary
knowledge possibly conserving knowledge on a higher level. Recently, the
term “machine unlearning” has been coined for the first beneficial use
described above. In this workshop the general topic of beneficial forgetting
shall be explored from different viewpoints.
The workshop aims to bring together researchers from AI, Machine Learning,
Cognitive Science, and other disciplines who are interested in understanding
how artificial systems can profit from forgetting and how beneficial
forgetting in humans can be influenced. Topics of interest include, but are
not limited to the following:
– Forgetting in knowledge management systems
– Agents with knowledge limitations
– Unlearning hypotheses in active or incremental learning
– Supporting humans to beneficially forget
– Cognitive models using or displaying beneficial forgetting processes
——- Paper Submission ——-
We invite papers, which have to be in English and formatted according to the
Springer LNCS style. Papers may report on new research that makes a
substantial contribution to the field, but also on research in progress.
Papers may have up to 8 pages (including references). Shorter papers are
Submission will be by email in electronic form as pdf only. Submissions
should be sent until June 18 to michael.siebers (at) uni-bamberg.de
All papers will be subject to blind peer review based on the standard
criteria of relevance, significance of results, originality of ideas,
soundness, and quality of the presentation. All accepted papers will be
published in online proceedings, and will be presented at the conference.
At least one author of each accepted paper must register for the KI
conference and present the contribution.
——– Important Dates ——–
Paper Submissions Due: June 11, 2017
Acceptance Notification: July 24, 2017
Camera-ready Version Due: August 20, 2017
Workshop: September 25 or 26, 2017
——– Main Organizers ——–
Michael Siebers, Cognitive Systems Group, University of Bamberg
Christian Jilek, Smart Data & Knowledge Services Department, DFKI GmbH
——- Program Committee ——-
Christoph Beierle, FernUniversität in Hagen, Germany
Francesco Gallo, EURIX Group, Italy
Mark A. Greenwood, University of Sheffield, UK
José Hernandez-Orallo, Universitat Politècnica de València, Spain
Nattiya Kanhabua, Aalborg University, Denmark
Gabriele Kern-Isberner, TU Dortmund, Germany
Fernando Martinez, Universitat Politècnica de València, Spain
Heiko Maus, German Research Center for Artificial Intelligence (DFKI), Germany
Vasileios Mezaris, Centre for Research and Technology Hellas, Greece
Marco Ragni, Albert-Ludwigs-Universität Freiburg, Germany
Nele Rußwinkel, TU Berlin, Germany
Sven Schwarz, German Research Center for Artificial Intelligence (DFKI), Germany
Tobias Tempel, Universität Trier, Germany
Ingo J. Timm, Universität Trier, Germany
Maria Wolters, University of Edinburgh, UK
Register by February 15 to avoid late fee!
Groningen Spring School on Cognitive Modeling
– ACT-R, Nengo, PRIMs, & Accumulator Models –
Date: April 3-7, 2017
Location: Groningen, the Netherlands
Fee: € 250 (late fee + €50 after February 15)
More information and registration: www.ai.rug.nl/springschool
We would like to invite you to the 2017 Groningen Spring School on Cognitive Modeling. As last year, the Spring School will cover four different modeling paradigms: ACT-R, Nengo, PRIMs, and Accumulator models. It thereby offers a unique opportunity to learn the relative strengths and weaknesses of these approaches. Each day will consist of four theory lectures, one on each paradigm. Each modeling paradigm also includes hands-on assignments. Although students are free to chose the number of lectures they attend, we recommend you to sign up for lectures on two of the modeling paradigms, and complete the tutorial units for one of the paradigms. At the end of each day there will be a plenary research talk, to show how these different approaches to modeling are applied.
The Spring School will be concluded with a keynote lecture and a conference dinner. We are excited to announce that Sander Bohte has accepted our invitation and will be the keynote speaker.
Admission is limited, so register soon!
Teachers: Jelmer Borst, Hedderik van Rijn, Katja Mehlhorn (University of Groningen)
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.
Teacher: Terry Stewart (University of Waterloo)
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.
Teacher: Niels Taatgen (University of Groningen)
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.
Teacher: Marieke van Vugt, Don van Ravenzwaaij (University of Groningen), & Martijn Mulder (University of Amsterdam)
Decisions can be described in terms of a process of evidence accumulation, modeled with a drift diffusion mechanism. The advantage of redescribing the behavioral data with an accumulator model is that those can be decomposed into more easily-interpretable cognitive mechanisms such as speed-accuracy trade-off or quality of attention. In this course, you will learn about the basic mechanisms of drift diffusion models and apply it to your own dataset (if you bring one). You will also see some applications of accumulator models in the context of neuroscience and individual differences.
Sorry, this entry is only available in Deutsch.
Sorry, this entry is only available in Deutsch.