Teamwork

Description

The teamwork grade is composed of two assignments. The first teamwork evaluation, administered shortly after the Design Review phase, consists of feedback questions designed to help the ECE 445 Staff better understand how each student's group is progressing towards the final demo. If all questions are answered completely and thoughtfully, the student will be awarded 5 points for completion of the assignment. No partial credit will be awarded for late submissions. The survey may be completed on Compass2g.

The second teamwork evaluation is a subjective score that will be awarded at the end of the semester according to the criteria below. Partner evaluations may be completed on Compass2g at the end of the semester to help determine this score. Responses to both surveys are confidential and will not be disclosed to the other teammates in the student's group.

Requirements and Grading

Each student in a group will be evaluated on the following criteria:

Submission and Deadlines

The teamwork evaluation forms should be completed on Compass2g by the deadlines listed on the Course Calendar. Teamwork evaluation sheets will be taken into account when teamwork grades are assigned. However, these scores will not fully determine the teamwork grade.

Intelligent Texas Hold 'Em Robot

Xuming Chen, Jingshu Li, Yiwei Wang, Tong Xu

Featured Project

## Problem

Due to the severe pandemic of COVID-19, people around the world have to keep a safe social distance and to avoid big parties. As one of famous Poker games in the western world, the Texas Hold’em is also influenced by the pandemic and tends to turn to online game platform, which, unfortunately, brings much less real excites and fun to its players. We hope to develop a product to assist Poker players to get rid of the limit of time and space, trying to let them enjoy card games just as before the pandemic.

## Solution Overview

Our solution is to develop an Intelligent Texas Hold’em robot, which can make decisions in real Texas poker games. The robot is expected to play as an independent real player and make decisions in game. It means the robot should be capable of getting the information of public cards and hole cards and making the best possible decisions for betting to get as many chips as possible.

## Solution Components

-A Decision Model Based on Multilayer Neural Network

-A Texas Hold'em simulation model which based on traditional probabilistic models used for generating training data which are used for training the decision model

-A module of computer vision enabling game AI to recognize different faces and suits of cards and to identify the game situation on the table.

-A manipulation robot hand which is able to pick, hold and rotate cards.

-Several Cameras helping to movement of robot hand and the location of cards.

## Criterion for Success

- Training a decision model for betting using deep learning techniques (mainly reinforcement learning).

- Using cv technology to transform the information of public cards and hole cards and the chips of other players to valid input to the decision-making model.

- Using speech recognition technology to recognize other players’ actions for betting as valid input to the decision model.

Using the PTZ to realize the movement of the cameras which are used to capture the information of pokers and chips.

- Finish the mechanical design of an interactive robot, which includes actions like draw cards, move cards to camera, move chips and so on. Utilize MCU to control the robot.

Project Videos