GRK 2340

Graduiertenkolleg "Computational Cognition"

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Using Deep Reinforcement Learning to Teach a Machine a World Understanding

Viviane Clay is working on several techniques to try and teach a machine a semantic world understanding in a largely unsupervised fashion.

Supervisors: Peter König, Kai-Uwe Kühnberger, Gordon Pipa

Self-organised grammar learning with a plastic recurrent network

A major goal of Sophie Lehfeldts PhD project is to train a recurrent
neural network to learn grammatical structures as found in natural language in a self-
organised fashion.

Supervisors: Gordon Pipa & Jutta Mueller

Project Westdrive: large scale VR Foundation for immersive Experiments on Human Computer Interactions

Would you trust a robot to drive your car? Maximilian Wächter's goal is to gain insights in human trust building behavior and ultimately lower reservations regarding this technology. For this he developed a large scaled, highly realistic VR simulation with AI controlled cars as an eye-tracking experiment.

Supervisors: Peter König, Gordon Pipa

Language emergence in artificial agents

Xenia Ohmer develops computational models of language learning and emergence in artificial agents. Firstly, she uses these models to gain insights on the role of pragmatic reasoning in human language learning, and secondly, she tries to integrate pragmatic reasoning mechanisms into artificial agents designed for language learning or communication.

Supervisors: Michael Franke, Peter König

Probabilistic Modeling of rational communication with conditionals

Britta Grusdt studies the interpretation of the little word “if”, as it is
an excellent showcase of the context-dependence of language understanding and logical

Supervisors: Michael Franke, Mingya Liu

VR environment to study context dependent visual perception

How does attention influence our visual perception depending on the task? To answer this Marc Vidal de Palol uses novel experimental methodology by combining techniques: VR, Eye-tracking, and EEG.

Supervisors: Gordon Pipa & Peter König

Incorporating motion into PeriNet - a computational model for central and peripheral vision

This project helps to advance our understanding of the human visual system and to develop efficient, biologically plausible end-to-end computational models for vision. With the PeriNet computational model Hristofor Lukanov addresses the problem of the split in the peripheral and central vision.

Supervisors: Gordon Pipa & Peter König

The semantics, pragmatics, and acquisition of polarity items

Juliane Schwab studies positive and negative polarity items in natural language. Her project contributes to our understanding of the processing and learning mechanisms at the interface of syntax, semantics, and pragmatics.

Supervisors: Mingya Liu, Jutta Mueller

Semi-supervised Conceptors and Conceptor Logic

Conceptors were introduced by H. Jaeger in 2014 as a mathematical formalism to derive and manipulate internal representations of concepts in neural networks and reintroduce them into the network dynamics. Georg Schroeter explores further the theoretical foundations and possible applications of Conceptors to both recurrent and feed-forward neural network architectures.

Supervisors: Kai-Uwe Kühnberger & Gordon Pipa