1.1 Contexts and Situations
The definitions and interrelationships of contexts, situations and human intentions have been intensively discussed in pervasive computing (Bihler et al., 2005) and service computing (Rolland et al., 07). These notions were originally studied in different areas and then later were put together with the goal of improving human-centric systems including robots, software agents in smart home, and mobile systems.
One definition of context is “any information that can be used to characterize the situation of an entity” (Dey, 2001) for providing relevant information or services to the user. Typically, context awareness research centers on designing context models or context ontology (Strang & Linnhoff-Popien, 2004; Krummenacher & Strang, 2007), and developing various context reasoning and monitoring methods. Many of the existing approaches synthesize information about locations, surrounding objects, physiological readings of people etc., for use in real world applications. For example, location-based services (Bellavista et al., 2008) provide personalized functions based on the location gathered from context information. As one of the drivers for a pervasive computing paradigm, context awareness creates a new means of human-computer interaction because as it is deployed all over the environment, it changes the way users interact with the environment or the software system. CA systems rely heavily on the information of various sensor data to adapt to changing situations of the user who is using a CA service. Normally the CA system contains three major steps: collecting environmental data through sensor network or control frameworks, understanding contexts from the environmental data based on a context model for the system, and triggering actions to provide services. These three steps constitute a spiral of executions of a CA system; any change in the existing environment could cause changes in the context model and therefore trigger new actions, which in turn again affect the environment and so on.
The definition of situation has evolved over a long period. In the 1980's, situation was originally studied to investigate a mathematically based theory of natural language semantics (Barwise & Perry, 1980). In a later work (Barwise & Perry, 1983), situation was defined in terms of information conveyed in a discourse that can be clearly recognized in common sense and human language. Based on this definition, events and episodes are situations in time. Today, researchers have different perspectives on how to define situation, often depending on research objectives. For instance, some define situation as “histories” (i.e., finite sequences of primitive actions (Levesque et al., 1998)). Situation can also be reasoned by aggregating a set of predicates that explicitly represent information about sensory data (Mastrogiovanni et al., 2008). Sequencing such predicates can form the history of a finite number of instances in a first-in first-out order. For our purposes, generally a situation is a set of contexts in the application over a period of time that affects future system behavior (Yau et al., 2008).