GoalRecognitionAndPlanning

Code for Thesis:

Hierarchical Task Recognition and Planning in Smart Homes with Partial Observability

To cite this work:

Option 1: Dan Wang (2017). Hierarchical Task Recognition and Planning in Smart Homes with Partial Observability. UWSpace. http://hdl.handle.net/10012/12001

Option 2: Wang, D. and Hoey, J., 2017, November. Hierarchical Task Recognition and Planning in Smart Homes with Partial Observability. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 439-452). Springer, Cham.

Author: Dan Wang danwangkoala@gmail.com (May 2016 - June 2017)

Supervised by Prof. Jesse Hoey (https://cs.uwaterloo.ca/~jhoey/)

Association: Computer Science, University of Waterloo.

Research purposes only. Any commerical uses strictly forbidden.

Code is provided without any guarantees.

Research sponsored by AGEWELL Networks of Centers of Excellence (NCE).

Run

Entrance file: ../HTN-GRP-PO/main.py

Knowledge Base

Folder: ../KnowledgeBase/

Include:

    knowledge base
    
    missing sensor set up files (folder /missing_sensor)
    
    sensor reliability set up files (folder /sensor_reliability)
    
    initial real state set up (realState.json)
    
    belief state set up (state.json)

To under stand the format of method and operator in knowledge base refer "../HTN-GRP-PO/Interface Specification part III.md"

Test Cases

Folder: ../TestCases

Include:

    single goal correct steps:          case 1-3
    
    multiple goals with shared steps:   case 4
    
    multiple goals correct steps:       case 5-6
    
    single goal wrong steps:            case 7-10
    
    multiple goals wrong steps:         case 11-12

Performance Evaluation

Folder: ../PerformanceEvaluation

Include:

    The desired step output for each test cases
    
    the script that is used to compute the output accuracy