Integration of Multi-Agent Simulation and Logic Programming Language: Initial Challenges in Traffic Simulation

International audience Social changes and urban redevelopment are leading to the consideration of various traffic policies. For example, in recent years, reducing environmental impact has become a societal challenge, making it necessary to discuss how to adapt to new technologies like autonomous vehicles. In this context, developing traffic infrastructure and establishing traffic regulations have become essential. When introducing new policies, it is important to assess their impact on surrounding transportation environments. However, evaluating the effects of traffic policies in real-world situations is difficult, which creates a need for simulation technologies. We have developed a multi-agent urban simulator called MACiMA, which allows for large-scale reproduction of complex traffic phenomena. MACiMA can calculate and simulate traffic flow at a regional level, considering various factors such as vehicles, roads, and signals that interact with each other. One significant limitation of MACiMA is that it requires manual programming of each agent's behavior according to specific rules and regulations when analyzing the environmental impact of different policies. To address this issue, we propose integrating MACiMA with PROLEG, a Prologbased reasoning system. PROLEG can manage the decision-making processes of agents based on traffic laws and policies stored in its knowledge base. By combining these two systems, simulations can be conducted without the need to explicitly implement each agent's behavior according to specific policies or regulations being examined. This makes it easier to manage complex traffic rules and agent behaviors, and the implementation cost can be reduced. This integration not only simplifies the implementation of various scenarios but also helps study their potential outcomes. We have conducted multi-agent traffic simulations using agent control based on the integration of MACiMA and PROLEG's reasoning capabilities. Currently, we are using a scenario where all traffic consists of autonomous vehicles that can automatically follow traffic rules and adjust their behaviors as needed. In our simulation, all agents interact with PROLEG, allowing us to assess the impact of traffic rules effectively. This challenge of using a logic programming language is one potential approach to creating an agent with valid reasoning capabilities on GAMA.