Analysis Techniques For Man-Machine Systems Design

NATO UNoCLASSIFIEDAC/243(Panel 8)TR/7Volume 2- 94 -;5.2 SYSTEMS ANALYSIS BY INTEGRATED NETWORKS OF TASKS(SAI NT)What the technique doesSAINT is a general purpose network modelling and simulation technique that can be used in the design anddevelopment of complex human-machine systems. Using a Monte Carlo approach. SAINT provides the conceptualframework and the means for modelling systems whose processes can be described by discrete and continuousfunctions/tasks, and interactions between them. It provides a mechanism for combining human performance modelsand dynamic system behaviours in a single modelling structure. SAINT facilitates an assessment of the contributionthat personnel and machine components make to overall system performance. The discrete component of a SAINTmodel consists of nodes and branches, each node representing a task. Tasks are described bv a set of characteristics,e.g. performance time duration, priority, and resource requirements. Branches connecting the nodes indicate precedencerelations and are used to model the sequencing and looping requirements among tasks. SAINT allows the modellingof predecessor-successor relationships which are deterministic. probabilistic, and conditional. The precedence relationsalso indicate the flow of entities through the network. Entities are characterized by attributes which specify the flowof information or material. The continuous component of a SAINT model is the state variable description. Statevariables are defined by writing algebraic. difference, or differential equations that govern time-dependent systembehaviour. The use of state variables in SAINT is optional. The interactions between the discrete and the continuousmodels are initiated either by tasks being completed or by state variables crossing specified threshold values.Inputs to the techniqueOutputs of the techniqueFor modelling the discrete part. SAINT offers a graphic Once the SAINT model has been built. the modeller can[ symbol set for specifying the task network. The analyst impose a data collection structure to obtain informationmust specify every task and the details of each predecessor- about the behaviour of the system as it is exercised. Datasuccessor relationship. This requires information on the which can be obtained are 1) statistical descriptions ofspecific tasks characteristics and precedence relations. the execution of specific tasks, e.g. time interval andSAINT also offers a special set of variables for describing task completion statistics. 2) resource utilizationstate variable equations of the continuous model in statistics. e.g. busy/idle status of human (work load) andFORTRAN. To permit the modeller to make assignments equipment resources. 3) histograms of the probabilityto attribute values, to establish task durations, or to and cumulative density functions for distributedspecify special output formats. SAINT provides special variables. e.g. task durations. 4) time traces of statefunctions and programs which the user has also to write in variables.FORTRAN.When to useThe technique can be used in any phase of human-machine system developmenL It is particularly useful in theconcept development and design definition stages, and is most useful if dynamic processes are being controlled, or ifthe system has multiple-operators.NATO UNCLASSIFIED- 94 -