# A New AI Approach by Acquisition of Characteristics in Human Decision-Making Process
###### Abstract
By integrating planning with imitation learning, agents can exhibit more flexible and robust decision-making behavior, allowing them to handle complex tasks and adapt to changing environments beyond the specific demonstrations they were trained on. Regarding the different methodologies for reproducing the expert behavior, IL can be generally divided into two major approaches: behavior cloning (BC) and IRL. BC simply tackles the IL problem as a supervised learning problem that aims to build a straightforward mapping from the state space to the action space under the learned policy. But this method can only provide relatively acceptable performance in environments that are the same or similar to the training dataset due to the property of an MDP. However, minor errors can quickly compound when the learned policy departs from the observed states in the demonstration [10]. To increase the awareness of the environment dynamics, an IRL approach uses reinforcement learning methods to extract a reward function based on the optimal expert trajectory and learning a policy from the inferred reward function. However, the IRL method is hardly implemented into real-world tasks since recovering a reward function requires intensive interactions with an external environment, which can be costly in terms of computation and safety [11; 12]. Decision making is performed as the center of human interaction with the real world and the decision-making process has a tight relationship with individual characteristics, which was mentioned in our previous work [13]. In this paper, we propose an approach to characterize human decisions. Generally defining the characteristics of decision making may involve understanding the decision strategies employed by individuals or groups in different contexts and examining how the cognitive processes interact to influence decision outcomes. Additionally understanding how the environmental factors shape decision-making behavior can contribute to defining its characteristics. The characteristics of decision making can also be defined in terms of decision outcomes, such as the quality of decisions, decision confidence, decision speed, and decision consistency. Analyzing these outcomes can provide insights into the underlying characteristics of decision-making processes. The characteristics of decision making can be inferred from analyzing the probability distribution of a group of people's decisions. Let us break down how this can be achieved:  
* Tendency and preference: Analyzing the probability distribution allows us to identify tendencies and preferences within a group. For example, if a certain option is chosen more frequently than others, it suggests a preference for that option within the group [14; 15]. * Variability and consensus: The spread or variability of decisions within the probability distribution provides insights into the level of agreement or disagreement between decision makers. A narrow distribution indicates a high level of consensus, whereas a broader distribution suggests greater variability in decision preferences [16; 17]. * Decision biases: Certain decision biases may manifest as skewed or asymmetric probability distributions. For instance, if decisions tend to cluster around a particular option due to an anchoring bias or status quo bias, it will be reflected in the shape of the probability distribution [18]. * Risk preferences: Decision making often involves trade-offs between risks and rewards. By analyzing the probability distribution of decisions, we can infer the risk preferences of the group. A risk-averse group may exhibit a probability distribution skewed toward safer options, while a risk-seeking group may display a distribution skewed toward riskier alternatives [19; 20]. * Decision stability: The stability of decision making over time can also be assessed through changes in the probability distribution.