Enhancing Intelligence Of Business Simulation Games

Abstract

Developing computer simulation games incorporates different computing aspects such as computer graphic, artificial intelligence, simulation itself, software engineering, etc. This paper is focusing on building intelligence into business simulation games by embedding intelligent agents into its software. Agents are proposed to generate simulation scenarios; define initial conditions of the game and/or respond to players' decisions by changing an economical environment according to the goals set by a game leader, and the agent's behavioural rules. Other applications of intelligent agents such as when an agent simulates a tutor or acts as a virtual player are also discussed in the paper.

How to Use Artificial Intelligence to Improve Entrepreneurial Attitude in Business Simulation Games: Implications From a Quasi-Experiment

“…This satisfies the participant's requirement for individuality and adaptability of the simulated game process (Kuleto et al, 2021). On the other hand, the use of AI technology can effectively avoid human factors of unreliability and instability, thus improving the quality of simulation games (Bikovska et al, 2006). Secondly, the role of teachers in BSGs is limited.…”

“…The intervention of AI makes it possible for the diverse contexts of BSGs, and contextual learning is an important consideration for game designers in entrepreneurship education (Zulfiqar et al, 2019). On the one hand, AI can also provide different simulation scenarios for participants with different course requirements and ability levels by changing different parameter configurations (Bikovska et al, 2006). This satisfies the participant's requirement for individuality and adaptability of the simulated game process (Kuleto et al, 2021).…”

“…Finally, AI can save and share game data, which can help reduce the time and cost of the game (Matulis and Harvey, 2021), facilitate quick access to educational support for students (Kuleto et al, 2021),thereby promoting the maximum effect of entrepreneurship education. The more different situations an AI accumulates, the more conducive it is to function (Bikovska et al, 2006). AI can also automatically correct mistakes and omissions in the game process based on participants' game data to continuously develop and improve game modes (Xi, 2020).…”

Scenario-Based Planning And Management Of Simulation Game: A Review

“…Along with other methods artificial intelligence can be applied as well (Dobson et al, 2004;Bikovska et al, 2006). In the gaming, agents can replace human players or work in the background as individual assistant, monitor trainee and evaluate his/her action online.…”

“…They usually model complex systems, so it is not easy to model and manage them. As examples of such games can be mentioned the International Logistics Management Game introduced in (Bikovska et al, 2006) and The Distributor Game introduced in (van Houten et al, 2005). In general, a computer business simulation game may be interpreted as a sequential decision-making experience with reality, which is simulated and animated on the computer.…”