# Cognitive Artificial Intelligence Using Bayesian Computing Based on Hybrid Monte Carlo Algorithm
## 1 Introduction  
From symbolic and connectionist paradigms to present-day deep learning methods, various studies of artificial intelligence (AI) have been conducted [1, 2, 3]. In the meantime, not only computer science, but also mathematics, statistics, brain science, psychology, and industrial engineering have been interdisciplinary studies for AI research [4, 5, 6]. To date, most AI research has aimed at developing intelligent systems that perform optimal decision-making procedures. For example, AI playing Go focused on the goal of defeating opponents [7]. However, when humans play the Go game, in some cases, they perform actions that are slightly advantageous to their opponents for the fun of the game. We have to consider other concepts of current AI technology. Therefore, we propose cognitive AI (CAI) in our research. CAI is AI that imitates human thinking and behaves with emotion [8]. As the research on AI in various fields is more actively conducted, the demand for CAI will increase further [8, 9]. For instance, in the fields of healthcare and education, the need for CAI that can emotionally communicate with humans has been raised [10, 11, 12, 13, 14]. The research on CAI is still at an early stage. [PERSON] and [PERSON] (2021) presented an introduction to a knowledge-growing system by CAI [6]. In addition, [PERSON] et al. (2021) proposed predictions using a knowledge-growing system as a CAI approach [15]. The previous two studies related to CAI focused on knowledge-growing systems and did not deal with human decision making based on emotions. In another study related to CAI, [PERSON] (2021) proposed a method for making a machine capable of mimicking human thinking [8]. The method applied the results of a posterior distribution and Bayesian bootstrap to construct a machine imitating human thinking [8]. In this paper, we also use a method for CAI development using advanced Bayesian computing. We consider the hybrid Monte Carlo (HMC) algorithm for our Bayesian approach [16, 17, 18, 19, 20, 21, 22, 23]. Human thinking is performed through fast computation based on a parallel neural network structure. Therefore, we chose HMC, which has a faster computation speed than the popular Metropolis-Hastingsalgorithm in Bayesian learning [16; 17; 18]. The HMC algorithm is used to construct model parameters [19; 20; 21; 22; 23]. This paper contributes to developing machines that think and behavior similar to humans with emotion. We apply the HMC algorithm to create the proposed method. We expect our research results to be used in the development of human-friendly AI systems in various practical applications, such as healthcare, education, and mobility. Traditional AI focuses on optimal decision making, but the CAI proposed in this study focuses on the development of machines that mimic human emotions.  
In order to proceed with the proposed research, we organized our paper as follows. In Section 2, the research background is introduced. In this section, we describe the concepts of cognitive systems for AI and Markov Chain Monte Carlo (MCMC) algorithms. We present our proposed method for CAI in Section 3. In the next section, we perform a simulation study to show how the proposed method can be used in real domains. Lastly, we present our conclusions and future works in Section 5.