This presentation was given at the nectar session of the 9th Hellenic Conference on Artificial Intelligence (SETN 2016) that took place on May18th- 20th in Thessaloniki. It is about applying an agent-oriented software engineering (AOSE) methodology, i.e. the Agent Systems Engineering Methodology (ASEME) for building intelligent systems. We present it along with a case study in the Ambient Intelligence (AmI) Application Domain. We discuss the challenges, the ASEME Methodology, the System Architecture and our results.

1. Engineering Ambient
Intelligence Systems
using Agent Technologyusing Agent Technology
Nikos Spanoudakis
Technical University of Crete

2. Presentation Contents
 Application Domain and Challenges
 The ASEME Methodology
 System Architecture System Architecture
 Results
 Conclusion

3. Project goals
 HERA (Home sERvices for specialised elderly
Assisted living) aims to provide cost-effective
specialized assisted living services for the
elderly people suffering fromelderly people suffering from
 Mild Cognitive Impairment (MCI)
 mild/moderate Alzheimer Disease (AD)
 other diseases (diabetes, cardiovascular)
to improve the quality of their home life, extend its
duration

4. Application Domain
 Ambient Assisted Living (AAL)
 combination of tele-homecare and smart homes
 in the field of Ambient Intelligence (AmI)

5. Challenges
 Engineering an AAL system is a non-trivial task
(Nehmer et al., 2006)
 Several issues are open for this type of
applications (Kleinberger et al., 2007; Koch,applications (Kleinberger et al., 2007; Koch,
2006):
 Adaptability
 Natural and anticipatory Human-Computer Interaction
 Heterogeneity
 Lack of an evaluation framework considering legal,
ethical, economical, usability and technical aspects

6. Contribution
 An Agent Oriented Software
Engineering (AOSE) methodology for
developing AmI systems
An architecture for the problem domain An architecture for the problem domain
 An architecture for integrating agents to
the general service oriented software
architecture

7. Why agents?
 Agents are
proactive (have goals and pursue them)
reactive (respond to events in environment)
social (acquainted with other similar software
and can cooperate-compete with it)
autonomous (do not need human intervention
to act)
intelligent (may perform tasks that when
performed by humans we consider that are
the evidence of a certain intelligence)

8. THE ASEME METHODOLOGY
A methodology for guiding project development

9. AOSE Considerations
 What, how many agents?
 How to structure of agent?
 Model of the environment?
 Communication? Communication?
 Relationships?
 Coordination?
 Protocols?
(Hexmoor and Brainov, 2002)

10. ASEME
the phases and abstraction layers
Agent Level Capability Level
Development
Phase Society Level
Levels of Abstraction
Goals RequirementsActors
Requirements
Analysis
Capabilities
Agent Control ComponentsSociety Control
Roles and Protocols
Design
Analysis
Agent code
Capabilities
code
Platform
management code
Implementation
Analysis
Functionality

11. Requirements Analysis
 Identify the stakeholders
Seniors@home
Hospitals, health centers
Telecom operators, internet service providers
(ISPs), portals

12. The Systems Actors Goals
model (SAG) - initial

13. SAG refined

14. Analysis Phase
 The first model is the System Use Cases
 Goals are transformed to high level tasks
and are decomposed to simple tasks
 Let’s see for example the goal assign pills

15. Analysis Phase
 Transform SAG goal to use case (SUC)

16. Analysis Phase
 Refine the SUC diagram

17. The Agent Interaction Protocols
model
 For each use case connecting two roles
we create an interaction protocol
 We use Gaia formulas to define liveness
of each role within the protocolof each role within the protocol
Operator Interpretation
x . y x followed by y
x | y x or y occurs
x* x occurs 0 or more times
x+ x occurs 1 or more times
x ~ x occurs infinitely often
[x] x is optional
x || y x and y interleaved

18. The AIP model
Role
Rules for
engaging
Results
Liveness

19. The system roles model (SRM)
 Shows each role’s liveness
 Including all used protocols
 Associating including use case to Associating including use case to
capabilities
 Included use cases to activities
 Associating activities to functionalities

20. Example – Assign pills

21. Example – Assign pills

22. Assign pills: Produced SRM
Role: PersonalAssistant
Capabilities and Protocols:
AssignPills_PersonalAssistant, …
Activities:
ReceiveNewPillPrescriptionRequest, UpdateUserScheduleReceiveNewPillPrescriptionRequest, UpdateUserSchedule
Liveness:
PersonalAssistant = AssignPills_PersonalAssistant OP? …
AssignPills_PersonalAssistant = ReceiveNewPillPrescriptionRequest.
UpdateUserSchedule
… This formula was copied from
the AIP model

23. Assign pills: Refined SRM
Role: PersonalAssistant
Capabilities and Protocols:
AssignPills_PersonalAssistant, UpdateUserSchedule, …
Activities:
ReceiveNewPillPrescriptionRequest, ResolveConflicts,ReceiveNewPillPrescriptionRequest, ResolveConflicts,
UpdateUserScheduleStructure, …
Liveness:
PersonalAssistant = AssignPills_PersonalAssistant~ || …
AssignPills_PersonalAssistant = ReceiveNewPillPrescriptionRequest.
UpdateUserSchedule
UpdateUserSchedule = ResolveConflicts. UpdateUserScheduleStructure
…

24. A graphical view of SRM
 The functionality graph
Interfaces with
external systems
Functionality sending a
standard FIPA ACL message

25. Design phase
 In the design phase liveness formulas are
transformed to statecharts
 Then,
The variables (in/out params) of each state
activity are defined
the transition expressions are defined

26. Transformation templates
 Are applied recursively in formulas

27. The Inter- and Intra-Agent Control
 The inter-agent control (EAC) is a statechart defining the
parallel behaviour of two or more roles
 The intra-agent control (IAC) coordinates the interactions
between the agent’s capabilities (or modules)
 Every role in an EAC can be merged in the IAC model
as-is and it can be refined:
 By turning a state to a superstate with substates
 IAC allows the parallel execution of multiple protocols

28. The EAC model for AssignPills

29. And the Personal Assistant

30. Automatic Code Generation
 Automatically generating all control code.
The developer just needs to invoke
functions at appropriate parts

31. Automatic Code Generation
 Automatically generating all control code.
The developer just needs to invoke
functions at appropriate parts

32. The ASEME Dashboard

33. AmI Architecture
Environment
Interfaces
(standards)
People
Computing
Subsystems
Sensors

34. Integrated System Architecture

35. MAS Architecture

36. Agents Architecture

37. HERA trials
 Two development iterations
 We focused in two categories of users:
 the end-users (who use the HERA services)
 A total of 30 end-users (10 healthy elderly, 8 suffering from A total of 30 end-users (10 healthy elderly, 8 suffering from
MCI, 8 suffering from mild AD, and 4 suffering from moderate
AD) were selected to participate in the project trials phase
 the Medical Personnel (who configure the HERA
services and assess the end users’ progress).
 10 medical experts

38. Results
 Satisfaction of the users for the two phases

39. Concluding
 We showed how a practitioner can apply
the ASEME methodology, a model-driven
development methodology, to build an AmI
systemsystem
 We proposed an architecture for such a
successful real world system (HERA)
 The system validation results show that
agent technology aids personal assistance
in ambient intelligence environments

40. This presentation was about the IEEE Intelligent Systems paper:
Spanoudakis N., Moraitis, P.. Engineering Ambient Intelligence
Systems using Agent Technology. IEEE Intelligent Systems, Vol.
30, Issue 3, May-June 2015, pp. 60-67
Find the paper@ http://dx.doi.org/10.1109/MIS.2015.3
More on ASEME: http://aseme.tuc.gr
THANK YOU!
More on ASEME: http://aseme.tuc.gr
More on HERA: http://w3.mi.parisdescartes.fr/hera
More on Nikos: http://users.isc.tuc.gr/~nispanoudakis