The Development of Intelligent Agents: A Case-Based Reasoning Approach to Achieve Human-Like Peculiarities via Playback of Human Traces
Naveed Anwer Butt, Zafar Mahmood, Ghani Ur Rehman, Moustafa M. Nasralla, Muhammad Zubair, Haleem Farman, Sohaib Bin Altaf Khattak · IEEE Access · 2023
Recent advances in the digital gaming industry have provided impressive demonstrations of highly skillful artificial intelligence agents capable of performing complex intelligent behaviors. Additionally, there is a significant increase in demand for intelligent agents that can imitate video game characters and human players to increase the perceived value of engagement, entertainment, and satisfaction. The believability of an artificial agent’s behavior is usually measured only by its ability in a specific task. Recent research has shown that ability alone is not enough to identify human-like behavior. In this work, we propose a case-based reasoning (CBR) approach to develop human-like agents using human game play traces to reduce model-based programming effort. The proposed framework builds on the demonstrated case storage, retrieval and solution methods by emphasizing the impact of seven different similarity measures. The goal of this framework is to allow agents to learn from a small number of demonstrations of a given task and immediately generalize to new scenarios of the same task without task-specific development. The performance of the proposed method is evaluated using instrumental measures of accuracy and similarity with multiple loss functions, e.g. by comparing traces left by agents and players. The study also developed an automated process to generate a corpus for a simulation case study of the Pac-Man game to validate our proposed model. We provide empirical evidence that CBR systems recognize human player behavior more accurately than trained models, with an average accuracy of 75%, and are easy to deploy. The believability of play styles between human players and AI agents was measured using two automated methods to validate the results. We show that the high p-values produced by these two methods confirm the believability of our trained agents.