1. Engineering Agent Systems for Decision Support
Alberto Fernández
School of Experimental Sciences and Technology
University Rey Juan Carlos
Madrid - Spain

2. Introduction Decision Support Systems Relevant in complex domains
Intelligent DEcision-making Assistant (IDEA)
Reactive:
What is happening?
Why is it happening?
What can be done?
What may happen if?
Proactively:
warnings
Application of agent technology to design IDEAs

3. Analysis UER (User-Environment-Responsibility) technique
User-centred Analysis
use cases
Environment-Centred Analysis
reaction cases
Responsibility-Centred Analysis
goal cases

4. Task Design Tasks Questions A priori distribution
Problem identification, Diagnosis, Action planning, Prediction
Questions
What is happening?: problem identification + diagnosis
What to do?: action planning + prediction
What may happen if?: prediction + p. identif.+ diagnosis
What to do if?: predict. + identif. + diagnosis + planning
A priori distribution
physical, organisational, availability of knowledge, ... => multiagent system
Interdependencies => co-ordination task

5. Method Design Tasks => Problem-Solving Methods
Problem Identification
classification method with two options:
a) reference situation + difference classification
b) direct classification
Diagnosis: several methods
a) classification method with additional information
b) cover & differentiate method

6. Method Design Action Planning Prediction Co-ordination
classification reasoning on predefined plans
collection of plans to perform subtasks
plan refinement strategy
Prediction
a) specific simulation methods
b) a simplified model: envisionment graph
simple systems, level of precision is not very high
Co-ordination
1) dependency detection
2) option generation
3) management decision

7. An example: Road Traffic Management
The domain:
urban motorways
Traffic Control Centre (TCC) is to assure a smooth flow of vehicles
The task:
assist TCC engineers in their decision-making respecting coherent signal plans
Infrastructure (Barcelona):
52 VMS
3 groups of traffic lights
7 ramp access controls
>300 sensors (loop detectors)

8. Traffic Management: Analysis

9. Traffic Management: Design
Problem Identification and Diagnosis
raw data pre-processing + fuzzy abstraction
knowledge base of frames (problem scenarios)

10. Example scenario

11. Traffic Management: Design
Problem Identification and Diagnosis
raw data pre-processing + fuzzy abstraction
knowledge base of frames (problem scenarios)
Action Planning and Prediction
calculation of contribution of each path to the problem
coherent alternative signal plans are generated
knowledge base of frames (traffic distribution scenarios)

12. Example scenario

13. Traffic Management: Design
Co-ordination
Centralised (TRYS)
co-ordinator agent
knowledge base of rules for managing dependencies
Decentralised (TRYSA2)
11 autonomous traffic agents for managing problem areas
knowledge base distributed among agents

14. Conclusions
Agent-based approach can be applied to the construction of advanced DSS
Agent-oriented knowledge and software engineering techniques can be applied
Illustrated in the road traffic domain
Improving integration of the different phases
Currently, evaluation in traffic control (Basque Country) and bus fleet management (Southern Spain)

15. Engineering Agent Systems for Decision Support
Alberto Fernández
School of Experimental Sciences and Technology
University Rey Juan Carlos
Madrid - Spain