Rule based expert system using Bayesian networks under uncertainty. As a part of our final year presentation we built a rule based expert system for home automation.

1. Rule based expert system with Uncertainty
Management in Smart Homes
Team Members
• Diksha Kushwaha
• Abhay R Dixit
• Varshini Kevin
• Abhishek M Kori
Under the Guidance of
Prof. RASHMI S R
Asst. Professor, Dept of Computer Science and Engineering

2. Contents
•
•
•
•
•
•
•
•

3. INTRODUCTION
Rule-Based system
● A rule-based system is the domain-specific expert system that uses
rules to make deductions or choices.
● rule-based systems are used as a way to store and manipulate
knowledge to interpret information in a useful way
Uncertainty
● There is high chance that a system may not be able to gauge the
exact event occurring
● This is because the existing systems operate on simple logic and
sensors may not be able to read the exact conditions

4. OBJECTIVE AND PROBLEM
STATEMENT
• efficient reasoning
method designed to support the integration of the
reasoning capability with the probabilistic
representations which can support the
expressiveness of uncertainty
•

5. Existing State-of-the-Art
1. Samsung SmartThings Home Monitoring Kit:
● Secure your home without the monthly monitoring costs of a traditional
home security system
● Notifications let you know who’s coming and going
Monitors smoke and carbon monoxide levels to help keep your family safe
● Use your iOS or Android device to control lights, appliance and electronics
while on the go
2. Oplink Connected CMPOPG2204OPL01 Alarm Shield:
● Includes door and window sensors, motion sensor, remote controls and siren
● Arm or disarm the system via your smart phone
Enjoy free self-monitoring
3. Simplisafe2 Wireless Home Security System 8-piece Plus Package:
• Sensors come pre-programmed
• Independent cellular connection can’t be cut by intruders
• Mobile monitoring compatible with iOS and Android devices

6. PROPOSED SYSTEM
●
●

7. SYSTEM REQUIREMENTS AND
EXPECTED RESULTS
• Hardware Requirements
4 GB of Hard disk
512 MB of RAM
Raspberry Pi
Rain and moisture sensors
Server Motor
• Software Requirements
Video to Frame Converter
LabelMe (Image Annotator)
Eclipse IDE
● The system will be able to develope reasons for rule based expert system
during uncertain situations.
● System can come up with reasons and actions even during falkey network and
weak sensors

8. Components
Soil Moisture Sensor
Rain Sensor
Raspberry pi
Knowledge Base (Expert
System) + Bayesian network
Motor- open/close window

10. Smart home-bell system
Video
Video to
frame
converter
Frames
Frame
annotation
using LabelMe
Read XML
annotated file
in Java
Run Jess
Rule
Ring Bell

11. CONCLUSION
• The proposed system aims at implementing rule-based uncertainty
reasoning expert system in building smart homes. This project will
specifically focus on events-driven and rule-based uncertainty
reasoning framework in smart house
• The future smart homes work based on users' behavior, environment
under uncertainty and also with Machine learning technology. It will
be able to read your routine habits and cater actions depending on
users needs and previous history

12. ● Explicit Knowledge-based Reasoning for Visual Question Answering
Peng Wang ∗ , Qi Wu ∗ , Chunhua Shen, Anton van den Hengel, Anthony Dick
School of Computer Science, The University of Adelaide
● Integration of Rule based and Case based Reasoning System to Support Decision Making
S. Srinivasan Department of Computer Application PDM, College of Engineering,
Bahadurgarh, India. dss dce@yahoo.com
LuxmiVenna School of Computer & Engineering ITM, University Gurgaon,India luxmi.
verma@gmail.com
Varun Sapra Department of Computer Science Jagannath Institute of Management
Studies, New Delhi, India varun.
● Detecting Inconsistencies in Rule-Based Reasoning for Ambient Intelligence
Hamdi Aloulou Institut Mines-Télécom, CNRS LIRMM, UMR 5506, France
Email: hamdi.aloulou@lirmm.fr
Romain EndelinInstitut Mines-Télécom, CNRS LIRMM, UMR 5506, France
Email: romain.endelin@lirmm.fr
Mounir Mokhtari Institut Mines-Télécom, CNRS LIRMM, UMR 5506,
CNRS IPAL, UMI 2955, Singapore

13. THANK YOU