AmI-DEU
Ambient Intelligence — Domain Expert User FrameworkA semantic framework that empowers domain experts — not developers — to build intelligent IoT applications by describing user intentions in their own language, and automatically compiling them into autonomous agents deployed on smart environments.
Building IoT apps today requires software engineers.
AmI-DEU removes that dependency.
A geriatric physician, an occupational therapist, or a city mobility planner all have rich, domain-specific knowledge about what an IoT application should do — but none have the programming skills to build it. AmI-DEU bridges that gap by letting domain experts express their knowledge as activity intentions, which the framework automatically compiles into deployable smart environment applications.
"AmI-DEU empowers domain experts to describe applications in diverse domains based on the user intention — an anticipated outcome of the end-user goal represented as a self-described Context."
— AmI-DEU Framework · AMI-Lab Research · Université de SherbrookeDomain experts describe what should happen in their own vocabulary. The framework handles the how.
A single intention definition runs in a smart home, a hospital, and a smart city — without modification.
IaaC applications carry enriched, compiled knowledge. The original meaning is never lost during compilation or deployment.
Deployed agents run autonomously on ContextAA. They assess conditions, act on the environment, and adapt — no human in the loop.
From Intention to Deployed Agent
AmI-DEU follows a three-phase lifecycle: domain experts describe their domain, express activity intentions as flows, and the action machine compiles IaaC applications for autonomous deployment.
The AmI-DEU Visual IDE
A drag-and-drop environment built on the flow metaphor — no code, no terminal, no configuration files. Two separate workspaces reduce cognitive load: one for defining the activity domain, one for building application flows.
Three Core Semantic Elements
AmI-DEU's semantic model reduces an IoT application to three composable building blocks. Together they encode the full intent of an application: what to observe, when to act, and what to do — all expressed in domain expert language.
The framework intentionally avoids full ontology-based modeling (which forces experts into predefined categories) and language-based modeling (which adds cognitive overhead). Instead, AmI-DEU uses logic rules + pattern matching — the simplest formal structure that keeps domain experts in control while allowing the action machine to reason semantically.
Represents any context — either an input (sensor reading, user state, environment condition) or an output (result of an environment action). Entities can be linked to domain concepts to improve semantic matching.
Evaluates context against defined rules and thresholds. Conditions trigger the application's response logic — they are the "when" of an intention: when heart rate exceeds X, when no movement is detected for Y minutes.
Describes how the smart environment should react: deliver a notification, change a device state, publish a new context, or activate a service. Actions are the "do" of an intention — and depend on available IoT resources.
Represents domain-specific objects and their relationships — Person, House, Room, Device. Concepts augment application semantics and can connect to external data sources such as MQTT or APIs.
The top-level object — a full representation of an anticipated end-user outcome. An intention subsumes user profile, domain knowledge, required resources, and all actions. It becomes the IaaC application after compilation.
The compiled artifact: a self-described, enriched context object containing conditions, actions, and knowledge. Reduced in size but rich in meaning — ready for autonomous deployment on any ContextAA node.
From Everyday Goals to Compiled Applications
An intention in AmI-DEU is a high-level description of what a user wants to achieve — expressed in their own domain language. These are real examples of what domain experts can define, covering everything from simple reminders to full emergency protocols.
"Play music to reduce stress when the system detects increased physiological indicators of anxiety in the user."
"Support departure preparation: remind to prepare breakfast, get dressed, prepare lunch, and notify optimal departure time."
"Ambient assisted living for an elderly person: cooking reminders such as add salt and turn off the stove."
"Remind me to rest 10 minutes each hour by displaying a non-intrusive alert on my computer and vibrating my chair softly."
"Emergency dispatch, control and supervision protocol combining multiple scenarios — medical, fire, security risk — and scenes such as chemical fire or security alert."
What Makes Up AmI-DEU
The framework is composed of four tightly integrated components that together take an idea from a domain expert's head to a running autonomous agent in a smart environment.
A drag-and-drop interface with two workspaces: a Domains area for defining concepts and relationships, and an Applications area for building intention flows. Designed for End-User Development (EUD) — no programming background required.
The brain of AmI-DEU. The action machine processes the expert's intention flow, matches rules and knowledge semantics using onto-AMI, and compiles a reduced but enriched IaaC application ready for ContextAA deployment.
AMI-Lab's universal ontology for smart environments. Provides the semantic backbone for concept matching and application compilation. Ensures that compiled intentions are interoperable across diverse IoT environments and device vocabularies.
Compiled IaaC applications are deployed directly to ContextAA, AMI-Lab's distributed multi-agent platform. ContextAA handles context matching, agent coordination, and service continuity — whether in a smart home, hospital ward, or smart city.
Domain concepts can be linked directly to live data sources: MQTT brokers (e.g., DomoSense gateway), REST APIs, or smart city data platforms (e.g., InterSCity). Context flows from physical sensors into the intention logic automatically.
AmI-DEU has been validated end-to-end on a physical testbed using real IoT devices for smart home scenarios, and extended to a smart city simulator for scalability evaluation. Results show enhanced adaptation before and after deployment.
AmI-DEU in the Platform Ecosystem
Whether you are a researcher, a domain expert, or a student — AmI-DEU is open for collaboration. Contact us or explore the full platform documentation.
Project Team
Bessam Abdulrazak
Professor, Department of Computer Science, Université de Sherbrooke
Director of the AMI-Lab
Victor Manuel Ponce Diaz
Ph.D. Student
Supervisor: Prof. Bessam Abdulrazak
Period: until September 2022