Investigating diagrammatic reasoning with deep neural networks

Diagrams in mechanised reasoning systems are typically en- coded into symbolic representations that can be easily processed with rule-based expert systems. This relies on human experts to define the framework of diagram-to-symbol mapping and the set of rules to reason with the symbols. We present a new method of using Deep artificial Neu- ral Networks (DNN) to learn continuous, vector-form representations of diagrams without any human input, and entirely from datasets of dia- grammatic reasoning problems. Based on this DNN, we developed a novel reasoning system, Euler-Net, to solve syllogisms with Euler diagrams. Euler-Net takes two Euler diagrams representing the premises in a syl- logism as input, and outputs either a categorical (subset, intersection or disjoint) or diagrammatic conclusion (generating an Euler diagram rep- resenting the conclusion) to the syllogism. Euler-Net can achieve 99.5% accuracy for generating syllogism conclusion. We analyse the learned representations of the diagrams, and show that meaningful information can be extracted from such neural representations. We propose that our framework can be applied to other types of diagrams, especially the ones we don’t know how to formalise symbolically. Furthermore, we propose to investigate the relation between our artificial DNN and human neural circuitry when performing diagrammatic reasoning

A Comparison of Accuracy between A New Commercial ELISA Test, GenediaTM Test and Other Commercial ELISA Tests for Serological Diagnosis of Helicobacter pylori Infection in Korea

Background/Aims : A new commercial enzyme linked immunosorbent assay (ELISA) test using Korean Helicobacter pylori (H. pylori) as an antigen, GenediaTM test, was compared to other serologic tests for H. pylori infection. Methods: Among two hundred seventy three subjects, H. pylori-positive group was consisted of 132 patients (50 peptic ulcer diseases, 52 chronic gastritis, and 30 gastric cancers) and H. pylori-negative group was consisted of 141 patients (121 adults and 20 pediatric patients). Endoscopic antral biopsy specimens were obtained for microscopy and rapid urease test (CLOTM test). We also performed GenediaTM IgG, IgA ELISA, G.A.P IgG, IgA ELISA, and Cobas-core IgG EIA. H. pylori infection was defermined when H. pylori was detected histologically or the results of CLOTM tests were positive. Results : The sensitivities and specificities of the serologic tests were 96.2% and 46.1% in GenediaTM IgG, 91.7% and 52.5% in GenediaTM IgA, 81.8% and 46.8% in G.A.P IgG, 25.0% and 85.1% in G.A.P IgA, 96.9% and 38.6% in Cobas-core test, respectively. In H. pylori-negative pediatric patients, the specificity of the tests was 80% in GenediaTM IgG, 95% in GenediaTM IgA, 60% in G.A.P. IgG, 100% in G.A.P IgA, and 75% in Cobas-core test. Conclusions: In Korea, GenediaTM test was comparable or superior to general serologic tests used for diagnosing H. pylori infection. However, it is necessary to improve the specificity of the GenediaTM test. (Kor J Gastroenterol 2000;36:20 - 28)ope

Multiagent cooperation and competition with deep reinforcement learning

<div><p>Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.</p></div