Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the beh...

The International Commission on Radiological Protection recommends diagnostic reference levels (DRL) in each radiological examination for justification and optimization of patients' dose in medicine. The aim of our study was to propose the dose management system by utilizing dose information in diagnostic X-ray radiation dose structured report (Dose SR) in The Digital Imaging and Communications...

This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity. We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupa...

EasyMiner (easyminer.eu) is a web-based association rule mining software based on the LISp-Miner system. This paper presents a proof-of-concept workflow for learning business rules with EasyMiner from transactional data. The approved rules are exported to the Drools business rules engine in the DRL format. The main focus is the transformation of GUHA association rules to DRL.

In recent years a number of the genes that regulate muscle formation and maintenance in higher organisms have been identified. Studies employing invertebrate and vertebrate model organisms have revealed that many of the genes required for early mesoderm specification are highly conserved throughout evolution. Less is known about the molecules that mediate the steps subsequent to myogenesis, e. ...

Leaf architecture directly influences canopy structure, consequentially affecting yield. We discovered a maize (Zea mays) mutant with aberrant leaf architecture, which we named drooping leaf1 (drl1). Pleiotropic mutations in drl1 affect leaf length and width, leaf angle, and internode length and diameter. These phenotypes are enhanced by natural variation at the drl2 enhancer locus, including r...

This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks. We show that even a naively engineered attack successfully degrades the performance of DRL algorithm. We further improve the attack using gradient information of an engineered loss func...