Using natural language to aid task specification in sequential decision making problems

Building intelligent agents that can help humans accomplish everyday tasks, such as a personal robot at home or a robot in a work environment, is a long-standing goal of artificial intelligence. One of the requirements for such general-purpose agents is the ability to teach them new tasks or skills relatively easily. Common approaches to teaching agents new skills include reinforcement learning (RL) and imitation learning (IL). However, specifying the task to the learning agent, i.e. designing effective reward functions for reinforcement learning and providing demonstrations for imitation learning, are often cumbersome and time-consuming. Further, designing reward functions and providing a set of demonstrations that sufficiently disambiguates the desired task may not be particularly accessible for end users without a technical background. In this dissertation, we explore using natural language as an auxiliary signal to aid task specification, which reduces the burden on the end user. To make reward design easier, we propose a novel framework that is used to generate language-based rewards in addition to the extrinsic rewards from the environment for faster policy training using RL. We show that using our framework, very simple extrinsic rewards along with a natural language description of the task are sufficient to teach new tasks to the learning agent. To ameliorate the problem of providing demonstrations, we propose a new setting that enables an agent to learn a new task without demonstrations in an IL setting, given a demonstration from a related task and a natural language description of the difference between the desired task and the demonstrated task. The techniques we develop for this setting would enable teaching multiple related tasks to learning agents by providing a small set of demonstrations and several natural language descriptions, thereby reducing the burden of providing demonstrations for each task. The primary contributions of this dissertation include novel problem settings, benchmarks, and algorithms that allow using natural language as an auxiliary modality for task specification in RL and IL. We believe this dissertation will serve as a foundation for future research along these lines, to make progress toward having intelligent agents that can conveniently be taught new tasks by end users.Computer Science

Estimation & comparison of salivary glucose with blood glucose in diabetic individuals

Aim: Saliva play a diagnostic tool for oral and systematic diseases has multiple advantages over other body fluids especially . The aim of this study was to explore the potential of salivary glucose as a marker in diagnosis and monitoring of diabetes mellitus using glucoseoxidase method, and as a non-invasive method replacing an invasive blood glucose estimation method. Materials and methods: Fasting blood and unstimulated whole saliva were collected from 50 controls, 50 newly diagnosed diabetics, and 50 diabetics under treatment. Blood and salivary glucose were analyzed in the samples by glucose-oxidase method. Results: The mean level of salivary glucose was reported to be 0.53 ± 0.4mg/dl in controls, 1.14 ± 1.55mg/dl in newly diagnosed diabetics, and 1.22 mg/dl ± 1.99 in diabetics under treatment. Conclusion: The mean level of salivary glucose in diabetics was significantly higher than that in non-diabetics. A positive, linear and significant, yet weak correlation between salivary and blood glucose suggests some potential for saliva as a marker in diagnosis and monitoring of diabetes mellitus

Digitized radiovisiographic analysis of dental pulp of permanent mandibular first molar and second premolar for age estimation using tooth coronal index method

Background: Teeth have become a valuable index to estimate age of an individual in forensic odontology. The advent of radiovisiography (RVG) has led to accurate calculation of dental age, which may be due to more precise RVG images than other radiographic techniques. Objectives: The study aimed at estimating the age of an individual from mandibular premolar and molar through tooth coronal index (TCI) measured from digital intraoral radiographic images (RVG). Materials and Methods: Using RVG 176 periapical radiographs of mandibular second premolar and first molar of individuals of either sex aged 20–70 years residing in Chhattisgarh were taken by paralleling angle technique for the study. The RVG images of selected teeth were analyzed and height of the crown, i.e., coronal height and the height of the coronal pulp cavity, i.e., coronal pulp cavity height of each tooth were measured in millimeters using KODAK software to calculate TCI. The real age of a subject was compared with TCI of tooth and the acquired data was subjected to Pearson's correlation test. Bland and Altman regression analysis was carried out to estimate limit of agreement between the two measurements (real and calculated age). Results: Negative correlation was observed between the real age and TCI of mandibular first molar (r = −0.149, P = 0.166) and second premolar (r = −0.20, P = 0.061). The difference between real age and calculated age for premolar ranged from − 38.11 to 23.51 years (mean difference 7.30) and for first molar it was from − 34.82 to 25.22 years (mean difference 4.799), which suggested acceptable agreement. Conclusion: TCI method provides accurate estimation of age from RVG images of teeth. RVG is convenient to use, has low radiation dose, and produces sharper images than other imaging methods