Engineering Ambient Intelligence Systems using Agent Technology
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The document presents the development of the HERA system, which provides specialized assisted living services for elderly individuals suffering from various conditions through ambient intelligence and agent technology. It outlines the ASEME methodology, challenges in engineering ambient assisted living systems, and the roles of agents in enhancing user interaction and service delivery. Results from trials indicate user satisfaction and the effectiveness of agent technology in personal assistance within these environments.
![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](https://image.slidesharecdn.com/presentationprint-160526070512/85/Engineering-Ambient-Intelligence-Systems-using-Agent-Technology-17-320.jpg)
Engineering Ambient Intelligence Systems using Agent Technology
- 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
- 35. MAS 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