Engineering Ambient Intelligence Systems using Agent Technology using Agent Technology Nikos Spanoudakis Technical University of Crete
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Engineering Ambient Intelligence Systems using Agent Technologyusing Agent Technology
Nikos SpanoudakisTechnical University of Crete
Presentation Contents
Application Domain and Challenges
The ASEME Methodology
System Architecture System Architecture
Results
Conclusion
Project goals
HERA (Home sERvices for specialised elderly Assisted living) aims to provide cost-effective specialized assisted living services for the elderly people suffering from elderly 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
Application Domain
Ambient Assisted Living (AAL)
combination of tele-homecare and smart homes
in the field of Ambient Intelligence (AmI)
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
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
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)
THE ASEME METHODOLOGY
A methodology for guiding project development
AOSE Considerations
What, how many agents? How to structure of agent? Model of the environment? Communication? Communication? Relationships? Coordination? Protocols?
(Hexmoor and Brainov, 2002)
ASEMEthe phases and abstraction layers
Agent Level Capability LevelDevelopment
Phase Society Level
Levels of Abstraction
Goals RequirementsActorsRequirements
Analysis
Capabilities
Agent Control ComponentsSociety Control
Roles and Protocols
Design
Analysis
Agent codeCapabilities
codePlatform
management codeImplementation
Analysis
Functionality
Requirements Analysis
Identify the stakeholders
Seniors@home
Hospitals, health centers
Telecom operators, internet service providers (ISPs), portals
The Systems Actors Goals model (SAG) - initial
SAG refined
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
Analysis Phase
Transform SAG goal to use case (SUC)
Analysis Phase
Refine the SUC diagram
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 Interpretationx . y x followed by yx | y x or y occursx* x occurs 0 or more timesx+ x occurs 1 or more timesx ~ x occurs infinitely often[x] x is optional
x || y x and y interleaved
The AIP modelRole
Rules for engaging
Results
Liveness
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
Example – Assign pills
Example – Assign pills
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
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
…
A graphical view of SRM
The functionality graph
Interfaces with external systems
Functionality sending a standard FIPA ACL message
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
Transformation templates
Are applied recursively in formulas
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
The EAC model for AssignPills
And the Personal Assistant
Automatic Code Generation
Automatically generating all control code. The developer just needs to invoke functions at appropriate parts
Automatic Code Generation
Automatically generating all control code. The developer just needs to invoke functions at appropriate parts
The ASEME Dashboard
AmI Architecture
Environment
Interfaces(standards)
People
ComputingSubsystems
Sensors
Integrated System Architecture
MAS Architecture
Agents Architecture
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
Results
Satisfaction of the users for the two phases
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
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
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