Modelling states of a computing system aware of an aspect of context Krunoslav Peter Andrija Stampar Teaching Institute of Public Health INFuture2015

Introduction (1/2) a modern computing system – responsive to its context a system aware of a single aspect of context senses states of an aspect of context changes its state accordingly modelled by using a finite-state automaton the state-transition table → set of decision rules of the decision-making subsystem

Introduction (2/2) a simple and technology-independent approach to the problem of implementing the decision-making subsystem the title slide + 12 slides errata instead of „dependent” must be „independent” in the Introduction instead of „system itself” should be „the system itself” in the Context section instead of „ connected or disconnected to” must be „either connected to or disconnected from” in the Context section

Context (1/2) information about system’s environment and the system itself information such as network connectivity, communication costs, light conditions, location etc. the aspect of context sensors → acquiring context

Context (2/2) 1.a discrete aspect of context – for example, network connectivity (connected to or disconnected from a network) elements of the alphabet of states: { connected, disconnected } a sequence of discrete states – a word of a regular language – the regular expression: disconnected (connected disconnected)* 2.a continuous aspect of context – for example, noise threshold = 80 dB: “normal” (< 80 dB) and “loud” noise (≥ 80 dB) the regular language: (normal + loud)*

Context-aware system differences between traditional and context-aware computing main functionality – user input and system's output utilizes contextual information and adapts to its context → three additional functionalities of a context-aware system: sensing, reasoning and acting the three subsystems of an abstract architecture of context-aware system: user input →main system→ output context →sensing subsystem thinking subsystem (decision-making subsystem) acting subsystem→ adaptation

Finite-state automaton (FSA) an appropriate model for a system aware of an aspect of context an FSA is a 5-tuple (S, Σ, δ, s 0, F), where S – a finite set of states Σ – an alphabet δ – the state-transition function δ: S x Σ → S s 0 – the start state F – a set of accepting states the state-transition function – represented as a state-transition table the FSA M accepts the language L(M) – a regular language

Specifying adaptive behaviour (1/2) adaptive behaviour of a system aware of the aspect C of context → like an FSA (S, Σ, δ, s 0, F), where S – a finite set of system’s states Σ – a finite set of states of the aspect C of context δ – the state-transition function δ: S x Σ → S s 0 – the initial state F – a set of system’s final states

Specifying adaptive behaviour (2/2) Example 1 – the system M 1 is aware of noise conditions senses noise conditions every second behaviour: if (noise is “loud”) then (system’s sound is “loud”) recognizes language L(M 1 ): (normal + loud)* defined as the FSA M 1 ({ volume 1, volume 2 }, { normal, loud }, δ, { volume 1 }, { volume 1, volume 2 }), with a state-transition table (Table 1) Table 1 – the state-transition table of the FSA M 1 δnormalloud volume 1 volume 2 volume 1 volume 2

Implementing a system aware of one aspect of context (1/2) the sensing subsystem → a discrete value the thinking subsystem a discrete value → the context variable system’s state → the state variable the acting subsystem – triggered by assigning a value to the state variable different from the previous value

Implementing a system aware of one aspect of context (2/2) Example 2 – the state-transition table of the FSA M 1 → decision rules Input: state, context if (state == “volume 1 ” and context == “normal”) then state = “volume 1 ” if (state == “volume 1 ” and context == “loud”) then state = “volume 2 ” if (state == “volume 2 ” and context == “normal”) then state = “volume 1 ” if (state == “volume 2 ” and context == “loud”) then state = “volume 2 ” Output: state if (State and Context) then State corresponds to δ: S x Σ → S a state-transition table for any aspect of context

Other approaches first-order logic-based formalisms to represent context and situations, and rules that map situations to required actions an approach inspired by biological processes, such as the human nervous system learning about and adapting to dynamical context and user behaviour

Conclusion modelling a system aware of an aspect of context like a finite-state automaton helps a developer of a system to systematically define context’s and system’s states adaptation to an aspect of context – in the form of the state-transition table → the set of decision rules of the thinking subsystem