On Cognitive Searching Optimization in Semi-Markov Jump Decision Using Multistep Transition and Mental Rehearsal

Cognitive searching optimization is a subconscious mental phenomenon in decision making. Aroused by exploiting accessible human action, alleviating inefficient decision and shrinking searching space remain challenges for optimizing the solution space. Multiple decision estimation and the jumpy decision transition interval are two of the cross-impact factors resulting in variation of decision paths. To optimize the searching process of decision solution space, we propose a semi-Markov jump cognitive decision method in which a searching contraction index bridges correlation from the time dimension and depth dimension. With the change state and transition interval, the semi-Markov property can obtain the action by limiting the decision solution to the specified range. From the decision depth, bootstrap re-sampling utilizes mental rehearsal iteration to update the transition probability. In addition, dynamical decision boundary by the interaction process limits the admissible decisions. Through the flight simulation, we show that proposed index and reward vary with the transition decision steps and mental rehearsal frequencies. In conclusion, this decision-making method integrates the multistep transition and mental rehearsal on semi-Markov jump decision process, opening a route to the multiple dimension optimization of cognitive interaction.