$𝟭𝟱𝟬,𝟬𝟬𝟬 𝗶𝗻 𝗚𝗿𝗮𝗻𝘁𝘀 𝘁𝗼 𝗦𝘁𝘂𝗱𝘆 𝗛𝗼𝘄 𝗣𝗟𝗡 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗖𝗮𝗻 𝗕𝗲 𝗚𝘂𝗶𝗱𝗲𝗱 𝗯𝘆 𝗖𝗮𝘂𝘀𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗮𝗻𝗱/𝗼𝗿 𝗛𝗼𝘄 𝗣𝗟𝗡 𝗥𝘂𝗹𝗲𝘀 𝗖𝗮𝗻 𝗗𝗶𝘀𝘁𝗶𝗻𝗴𝘂𝗶𝘀𝗵 𝗖𝗮𝘂𝘀𝗮𝗹 𝗳𝗿𝗼𝗺 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀

Implementing causal, versus strictly correlative, inference can lead to a deeper understanding of many complex problems.

Probabilistic Logic Networks (PLN), with their robust mathematical foundations for handling uncertain and incomplete knowledge—such as induction, abduction, analogy, and reasoning about time—offer a strong framework for this task.

Learn more and apply today: https://bit.ly/PLN-Inference-Control-RFP