Contextual Representation Model

In CDL, the MCS model is extended with defeasible rules and a preference relation on the system contexts. Let’s consider C as the set of contexts Ci in a MCS: A context Ci is

defined as a tuple of the form (Vi, Ri, Ti), where Vi is the vocabulary of Ci, Ri is a set of

rules, and Ti is a preference ordering on C.

Vi is a set of positive and negative literals of the form (ci : ai) and ∼ (ci: ai), which denotes

the negation of (ci: ai). Each context uses a distinct vocabulary, i.e., Vi∩ Vj = ∅ iff i 6= j.

This reflects the fact that each entity (e.g. device in an ambient intelligent environment) may use its own terminology. We should note, though, that the proposed model may also enable different contexts to use common literals by adding a context identifier, e.g. as a prefix, in each such literal and using appropriate mappings to associate them to each context.

Ri consists of a set of local rules and a set of mapping rules. The body of a local rule is a

conjunction of local literals (literals that are contained in Vi), while its head is labeled by a

local literal. There are two types of local rules: • Strict rules, of the form

rl_i: (ci : a1), . . . , (ci: an−1) → (ci : an)

They express sound local knowledge and are interpreted as follows: whenever the literals in the body of the rule ((ci: a1), . . . , (ci: an−1)) are strict consequences of the

local theory, then so is the conclusion of the rule ((ci : an)). Strict rules with empty

body denote factual knowledge. • Defeasible rules, of the form

rd_i : (ci : a1), . . . , (ci : an−1) ⇒ (ci : an)

They are used to express uncertainty: a defeasible rule cannot be applied to support its conclusion if there is adequate contrary evidence.

Mapping rules associate local literals of Ci with literals from the vocabularies of other

contexts (foreign literals). The body of each such rule is a conjunction of local and foreign literals, while its head is labeled by a local literal. Mapping rules are modeled as defeasible rules of the form:

rm_i : (cj : a1), . . . , (ck : an−1) ⇒ (ci : an)

A mapping rule associates literals from different contexts (e.g. (cj : a1) from Cj and

(ck: an−1) from Ck), with a local literal of the context that has defined ri, which labels the

head of the rule (here (ci : an)). By representing mappings as defeasible rules, we can deal

with ambiguities and inconsistencies caused by importing mutually conflicting information from different contexts. Finally, each context Ci defines a strict total preference ordering Ti

2.6. Contextual Representation Model

on C to express its confidence in the knowledge it imports from other contexts:

Ti = [Ck, Cl, ..., Cn]

Ck is preferred to Cl by Ci, if Ck precedes Cl in Ti. The strict total ordering enables

resolving all potential conflicts that may arise from the interaction of contexts through their mapping rules. In a later version of CDL [BA11], Ti is defined as a partial preference order

on C, which enables handling incomplete preference information. For sake of simplicity, we adopt here the original definition of Ti. In this thesis, we adopt ideas from contextual

defeasible logic in order to develop a context aware framework suitable for ambient intelligent systems. This will be presented in more details in chapter 7.

3

Modeling frameworks

Contents

3.1 Introduction . . . . 22 3.2 Model-driven engineering . . . . 22 3.3 [email protected] for context representation . . . . 23 3.3.1 Requirements of [email protected] . . . 24 3.3.2 Native Independent Versioning . . . 24 3.3.3 Time management . . . 25 3.3.4 Eclipse Modeling Framework (EMF) . . . 26 3.3.5 Kevoree Modeling Framework (KMF) . . . 26 3.4 Kevoree - A component based software platform . . . . 27 3.5 Kevoree Critical Features for AmI . . . . 28

3.1

Introduction

AmI systems aim to “make the system adapt to people,” as opposed to “people adapting to the system”. In order to achieve this, context awareness is a first step needed to trigger system adaptations. However, the distributed and heterogeneous nature of ambient intelligent systems lead to an important complexity when it comes to representing their current context or state, in order to later reason about it. To cope with such complexity, an abstract layer, that is always synchronized with the reality, is needed to represent the main domain concepts, and the relationship between them. For this goal, various methods such as model-driven engineering can help to structure and organize the data, in order to process it for a later reasoning. This step requires abstracting the system into domain specific concepts and relationships between these concepts to ease context derivation and the reasoning afterwards. For this goal, we use modeling techniques from model-driven engineering that we will present in section3.2. For every AmI agent, we use data models to represent the knowledge this agent possess. For the whole AmI system, we use distributed component models, where every component is an abstraction for an AmI agent. The next step is to enable a reflection layer where we feedback the system representation to the system itself to enable self-adaptation. In order to achieve this, we use [email protected] which we present in section 3.3.