A Case-Based Reasoning and Clustering Framework for the Development of Intelligent Agents in Simulation Systems

Artificial Intelligence (AI) techniques are essential to the modeling of realistic behaviors for agents in simulation systems. Although Case-Based Reasoning (CBR) and Clustering techniques are being explored in the implementation of such agents in computer games, these techniques are still under-used in the implementation of simulation systems. This work approaches this gap by proposing a new CBR and clustering framework in which clustering algorithms and clustering evaluation techniques are explored in both the construction of adjusted similarity functions and the organization of sub-case bases, which are indexing components to the efficient retrieval of relevant cases from case bases so as to support the solution of new simulation problems. To evaluate this framework, a case-based algorithm was implemented to simulate the choice of military supplies to be used in artillery battery missions in virtual tactical simulations.