Abstract
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.
Highlights
In order to analyze decision behaviors, human performance modeling (HPM) has been researched in the last few decades [3]
Similar to the human-like behavior, multistep decision is mutually influenced during the periods rather than the Complexity instant moment. e Markov property, strengthening the correlation in decision path, constricts that the selection of action elements is only relevant to the decision adopted at the previous moment
For a more general situation, the semi-Markov decision process overcomes the restriction by adopting the time-varying transition rate. us, the sojourn time between each mode can be of any non-exponential distribution
Summary
In order to analyze decision behaviors, human performance modeling (HPM) has been researched in the last few decades [3]. Existing physical obstruction makes humans unable to access state parameters without sensor measurement in the interaction environment, which causes unavoidable deviation To cover this shortage, the partially observable Markov decision method uses interactive object state as an uncertain estimation of observable state set, which is the main difference from the observable state Markov decision method [7]. A semi-Markov jump decision method is proposed to optimize the human cognitive searching decision path through the multistep transition part and mental rehearsal part in a specified airplane pilot interaction scenario. E time interval in sequential decision is not a constant and is arbitrary It cannot be modeled by noise like exponential distribution which obeys the Markov transition law. Our method integrates the semiMarkov decision transition interval, the multiple decision path estimation, and the changeable decision solution space for jump state
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