Optimization of Actions in Activation Timed Influence Nets

A sequential evolution of actions, in conjunction with the preconditions of their environment and their effects, are all depicted by Activation Timed Influence Nets. In this paper, we develop two algorithms for the optimal selections of such actions, given a set of preconditions. A special case for the two algorithms is also considered where the selection of actions is further constrained by the use of dependencies among them. The two algorithms are based on two different optimization criteria: one maximizes the probability of a given set of target effects, while the other maximizes the average worth of the effects’ vector. Povzetek: Predstavljena sta dva algoritma za optimizacijo akcij v časovno odvisnih mrežah. 1