Preetham Reddy

Preetham Reddy

Analog Engineer by profession. B.E. (Hons.) in EEE from BITS Pilani

Posts by Collection

portfolio

publications

Towards Continuous Monitoring of Environment under Uncertainty: A Fuzzy Granular Decision Tree Approach

Published in Joint Proceedings of the 3rd Modelling Symposium (ModSym), Developmental Aspects of Intelligent Adaptive Systems (DIAS), and Educational Data Mining Practices in Indian Academia (EDUDM) co-located with 10th Innovations in Software Engineering (ISEC 2017), Jaipur, India,, 2017

Smart monitoring of environment has been an essential area of research where decision-making process is inevitable. Reliability of the whole system depends on the stability and consistency of its decision-making unit. Real-time decision making is another challenge in the field on which the research community has been focusing on improving the performance of the underlying models. The underlying models are usually the learning models, that act as a smart engine after being sufficiently trained for the process. In this paper, we propose to use a decision tree model that has the capability of handling uncertainty in the acquired data from the environment. The resulting model is called as Fuzzy Granular Decision Tree (FGDT). Series of evaluation of FGDT shows that the model is stable and powerful for the presently considered problem.

Recommended citation: Reddy, Preetham N., Dambekodi, Sahith N., and Dash, Tirtharaj. "Towards Continuous Monitoring of Environment under Uncertainty: A Fuzzy Granular Decision Tree Approach" Innovations in Software Engineering (ISEC 2017), Jaipur, India . http://PNR-1.github.io/files/2017_DT.pdf

Adversarial neural networks for playing hide-and-search board game Scotland Yard

Published in Neural Computing and Applications, 2018

This paper investigates the design of game playing agents, which should automatically play an asymmetric hide-and-search-based board game with imperfect information, called Scotland Yard. Neural network approaches have been developed to make the agents behave human-like in the sense that they would assess the game environment in a way a human would assess it. Specifically, a thorough investigation has been conducted on the application of adversarial neural network combined with Q-learning for designing the game playing agents in the game. The searchers, called detectives and the hider, called Mister X (Mr. X) have been modeled as neural network agents, which play the game of Scotland Yard. Though it is a type of two-player (or, two-sided) game, all the five detectives must cooperate to capture the hider to win the game. A special kind of feature space has been designed for both detectives and Mr. X that would aid the process of cooperation among the detectives. Rigorous experiments have been conducted, and the performance in each experiment has been noted. The evidence from the obtained results demonstrates that the designed neural agents could show promising performance in terms of learning the game, cooperating, and making efforts to win the game.

Recommended citation: Dash, Tirtharaj, Dambekodi, Sahith N, Reddy, Preetham N, and Abraham, Ajith. "Adversarial neural networks for playing hide-and-search board game Scotland Yard." Neural Computing and Applications 2018. https://link.springer.com/article/10.1007%2Fs00521-018-3701-0

talks

teaching

Reinforcement Learning

Instructor, Center for Technical Education, BITS Pilani, Goa, 2017

  • Introduction to RL covering various topics like
    • Q-Learning
    • MDP
    • Policy Search
    • Credit Assignment Problems
    • Bellman Equation
    • DQN’s

Microelectronic Circuits

Lab Instructor for Undergraduate Course, BITS Pilani, Department of Electrical and Electronics Engineering, 2018

  • Conducted sessions to introduce ~300 sophomores to Cadence Virtuoso Simulation Tools
  • Sessions comprised of AC, DC and Transient Simulations for simple circuits such as
    • Single-Stage Amplifiers
    • Current Mirrors
    • Differential Pairs with Active Loads and Current Source Loads

Analog and Digital VLSI Design

Lab Instructor for Undergraduate Course, BITS Pilani, Department of Electrical and Electronics Engineering, 2018

  • Conducted Sessions on design and simulation of 2-stage compensated Op-Amps in Cadence Virtuoso.