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1
SEAMLESS SOFTWARE DEVELOPMENT FOR SYSTEMS BASED ON BAYESIAN NETWORKS An agricultural pest control system example I.M. del Águila, J. del Sagrado, S. Túnez and F.J. Orellana Dpt. Languages and Computation. University of Almería (Spain)
ICSOFT 2010
Index 2
/ 23
1. Introduction 2. Process model 3. Case study: pest control 4. Conclusions 5. Future works
1. Introduction 3
/ 23
Development of software systems Based on knowledge + Not based on knowledge
Approaches Acuña et Al, 1999 Alonso et Al, 2000 Águila et Al, 2006 ……………………….
Descriptive proposals
Models
Activities
Languages Documental Structure Artefacts
1. Introduction 4
/ 23
Knowledge Based Systems (KBS) Requires a methodology to ensure well-defined Knowledge-models
Bayesian Networks (BNs)
Current Approaches For BNs
Represent expert knowledge on an uncertain domain
How to build the knowledge model
BN Model
?
1. Introduction / 23
Who Defines Rules
Software Product
Integration
5
1. Introduction 6
/ 23
MAIN GOAL
Define a process model Non-knowledge-based part Knowledge-based part Bayesian Network
1 N 1
M
Generates N Uses
N
Index 7
/ 23
1. Introduction 2. Process model 3. Case study: pest control 4. Conclusions 5. Future works
VALIDATION TESTING Check if the BN meets the specifications
BN Model
2. Process Model. SSS 13
/ 23
Result
Modelling Languages Interface class
Model Conceptual view of the software system.
Interactions between system and actors
Entity class
Data persistence
Control class
Sequence, transaction, calculations or controls
Knowledge class
Inference tasks implemented in the software
Index 14
/ 23
1. Introduction 2. Process model 3. Case study: pest control 4. Conclusions 5. Future works
3. Case study: pest control 15
/ 23
Integrated Production Quality standard
Covered surface
Implantation Rate
3%
Ministry of the environment and rural and marine affairs
a. Requirement Modelling (RM) >> Problem Formulation (PF)
3. Case study: pest control 16
/ 23
a. Requirement Modelling (RM) >> Process Identification (PI)
3. Case study: pest control 17
/ 23
a. Requirement Modelling (RM) >> Domain Identification (DI)
a. Requirement Modelling (RM) >> Definition of the Project Scope (DPS)
Commitment Pest control processes performed by growers and agricultural engineers in Monitor Crop
Result of RM - Definition of requirements and domain concepts - Limitation of scope
3. Case study: pest control 18
/ 23
b. Expert Modelling (EM)
RM - Domain Identification
RM - Process Identification
DAG for a general Integrated Production System
A BN structure for the grape’s fly case pest
3. Case study: pest control 19
/ 23
c. Specification of the Software Solution (SSS)
PLATFORM DEPENDENT WORKFLOWS
d. Design of the Software Solution e. Coding and Debugging f. Software Evolution
Index 20
/ 23
1. Introduction 2. Process model 3. Case study: pest control 4. Conclusions 5. Future works
4. Conclusions 21
/ 23
This work shows how to integrate methods of software and knowledge engineering into a unified perspective. We have chosen Bayesian Networks (BN’s) as technique to handle uncertainty in decision-making-problems. The process model proposed allows the seamless inclusion of BN models into a final software solution for an organizational environment. The applicability of this solution has been validated in a real world problem: Integrated Production in agriculture.
5. Future Works 22
/ 23
Test the applicability of this approach to other case studies. Adapt the expert modelling workflow to other modelling techniques in order to verify that the approach reduces the software development effort. Computer-aided software engineering tools must be widened to include inference processes and structures originated in each of the knowledge techniques applied.
