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The Method: The Aggregation of Variability (Step 3)
Published in Hollnagel Erik, FRAM: The Functional Resonance Analysis Method, 2017
Preconditions are the system conditions or states that, in principle, must exist before a function can be carried out. Preconditions must obviously be provided by one or more other functions, that is, the state (of the Precondition) is the Output from upstream functions. In many cases, a function cannot be carried out unless the Preconditions have been established, and part of the function may be to check whether the Preconditions have been fulfilled, that is, whether the corresponding system state is true. One possibility is that the Preconditions are checked, but that the check cannot be completed because the timing of the 'signal' is imprecise, or because the state is ambiguous. The latter means that it cannot be determined precisely whether or not the Precondition is true. Another possibility is that the Preconditions are not checked properly due to variability in the way the checking is carried out.
The evolution of the fAIble system to automatically compose and narrate stories for children
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
A.J. Gonzalez, T. Anchor, A. Hevia, A. Posadas, J. Wade, R.A. Ansag, K. Benko, B. Bottoni, V. Kazakova, M.J. Alvarez, J.M Wong, J. Martin, R. Knauf, K.P. Jantke, A.S. Wu
With respect to event generation, AESOP selects the next event (action) stochastically from the Possibility Space, based on a probability influenced by weights assigned to attributes of the characters. These weights were selected heuristically by the development team based on their experiences, and were not scientifically selected or validated in any way. This obviates the need to plan the entire plot ahead of time and can serve to introduce surprising actions, something not possible in a template-based system such as Campfire. This also adds interestingness to the story and increases the likelihood of story diversity. Actions in AESOP have contextually-filtered pre-conditions and post-conditions. Pre-conditions dictate whether an action is possible, while post-conditions dictate how the world changes after the action is taken e.g., by removing or adding conditions to the Story World, as well as possibly changing a character’s attribute and relation weights.
PDDL4J: a planning domain description library for java
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2018
Unlike domain-dependent approaches that build upon algorithms specifically devised for each type of problem, planners are based on a declarative (non-procedural and very high-level) language specifying what needs to be done rather than how to do it. The problem representation describes the initial state of the world – objects under consideration and their properties, a goal to achieve and actions that can be performed on these objects to achieve the goal. Actions have triggering preconditions and resulting postconditions, which are the actions’ effects. The preconditions define under which circumstances (states of the world) actions can be performed. When triggered, actions change the state of the world as specified by their effects, and generate new states. As actions are not necessarily reversible (past states are no longer achievable from the present state), actions have precedence constraints and are not ordered in any sequence. A solution to a planning problem is a set of ordered actions, called a plan, starting from the initial state and ending in a goal state. For example, in graph theory, graph colouring is an assignment of colours to vertices (objects) such that no two connected vertices share the same colour (constraint). A planning problem for graph colouring defines: a set of colours and vertices, and their connections and associated colours (the initial state); three actions that are solely applicable to two connected vertices (preconditions), and whose effect is always to disconnect them (i.e. colour two unpainted vertices with two different colours; colour one unpainted vertex with a colour different from the another connected vertex, and simply disconnect two painted vertices). It is obvious that these actions are not reversible because painted vertices cannot be repainted. However, any valid combination (with respect to preconditions and precedence constraints) of actions satisfies the problem specification. Then, the goal here is to disconnect all of the vertices, and a plan is a sequence of colouring actions that reaches this goal. Another example of a planning problem is logistics (see Section 5.1) where the objects are parcels, trucks, airplanes, airports and locations: the parcels can be loaded and unloaded (actions) from trucks or airplanes. Two other actions are driving trucks from one location to another one, and flying airplanes between airports. The initial state indicates the location of the parcels, trucks and airplanes (properties of the objects), and the goal specifies the parcels’ destinations. A plan is a sequence of actions of loading/unloading parcels, driving trucks and flying airplanes between different locations in such a way that, in the final state reached by the last action, the goal is satisfied.