Kobonet is a deep Q-network (DQN) model that integrates causal principles to enhance problem-solving capabilities. Unlike traditional DQNs that rely on associative learning, Kobonet uses the PEACE (Probabilistic Easy vAriational Causal Effect) formula to estimate causal effects, reducing spurious correlations and increasing decision reliability.

Integrating causal reasoning with Kobonet offers several benefits:

• Enhanced Problem-Solving: Robots gain a better understanding of the causal structure of their environment, leading to more effective problem-solving.
• Reduction of Spurious Correlations: By focusing on true causal relationships, Kobonet minimizes errors caused by confounding factors.
• Increased Adaptability: Robots can adapt more quickly and flexibly to dynamic and complex environments.
• Optimized Performance: Overall efficiency of robotic operations is improved without compromising speed or accuracy.

Kobonet enhances performance by using causal principles to better understand environmental interactions. This enables robots to:

• Make More Accurate Decisions: Based on verified causal relationships rather than misleading correlations.
• Respond More Reliably: By adapting their actions according to changes in the environment.
• Maximize Efficiency: By optimizing robotic processes for better resource utilization and error reduction.

Kobonet is beneficial for various sectors, including:

• Manufacturing: Improving the precision and efficiency of production processes.
• Healthcare: Optimizing robotic systems for safer and more accurate medical interventions.
• Logistics: Enhancing the reliability and flexibility of inventory and distribution management systems.
• Agriculture: Better resource management and improved harvesting and planting techniques through deep causal understanding.