Preface

BNAIC is the annual Benelux Conference on Artificial Intelligence. In 2017, the 29th edition of BNAIC was organized by the Institute of Artificial Intelligence and Cognitive Engineering (ALICE), University of Groningen, under the auspices of the Benelux Association for Artificial Intelligence (BNVKI) and the Dutch Research School for Information and Knowledge Systems (SIKS). BNAIC 2017 took place in Het Kasteel, Groningen, The Netherlands, on November 8–9, 2017. BNAIC 2017 included invited speakers, research presentations, posters, demonstrations, a deep learning workshop (organized by our sponsor NVIDIA) and a research and business session. Some 160 participants visited the conference

Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPO

In this paper, a novel racing environment for OpenAI Gym is introduced. This environment operates with continuous action- and state-spaces and requires agents to learn to control the acceleration and steering of a car while navigating a randomly generated racetrack. Different versions of two actor-critic learning algorithms are tested on this environment: Sampled Policy Gradient (SPG) and Proximal Policy Optimization (PPO). An extension of SPG is introduced that aims to improve learning performance by weighting action samples during the policy update step. The effect of using experience replay (ER) is also investigated. To this end, a modification to PPO is introduced that allows for training using old action samples by optimizing the actor in log space. Finally, a new technique for performing ER is tested that aims to improve learning speed without sacrificing performance by splitting the training into two parts, whereby networks are first trained using state transitions from the replay buffer, and then using only recent experiences. The results indicate that experience replay is not beneficial to PPO in continuous action spaces. The training of SPG seems to be more stable when actions are weighted. All versions of SPG outperform PPO when ER is used. The ER trick is effective at improving training speed on a computationally less intensive version of SPG.Comment: 12 pages, 9 figures. Code is available at https://github.com/mario-holubar/RacingR

Semantic Preserving Siamese Autoencoder for Binary Quantization of Word Embeddings

Word embeddings are used as building blocks for a wide range of natural language processing and information retrieval tasks. These embeddings are usually represented as continuous vectors, requiring significant memory capacity and computationally expensive similarity measures. In this study, we introduce a novel method for semantic hashing continuous vector representations into lower-dimensional Hamming space while explicitly preserving semantic information between words. This is achieved by introducing a Siamese autoencoder combined with a novel semantic preserving loss function. We show that our quantization model induces only a 4% loss of semantic information over continuous representations and outperforms the baseline models on several word similarity and sentence classification tasks. Finally, we show through cluster analysis that our method learns binary representations where individual bits hold interpretable semantic information. In conclusion, binary quantization of word embeddings significantly decreases time and space requirements while offering new possibilities through exploiting semantic information of individual bits in downstream information retrieval tasks