A Dharmakīrtian Model of Relevance Realization in Cognitive Agents

A fundamental problem facing any learning organism is how to determine which aspects of its experience to attend to and which to ignore when guiding its goal-oriented actions. This problem, known as relevance realization, is key to understanding how cognitive agents, biological or artificial, learn purposeful behaviors through experience-based interactions with their environment. However, a comprehensive theoretical understanding of how organisms solve the relevance realization problem is currently lacking. Here, we approach this challenge by proposing a synthesis between a contemporary computational framework of goal-oriented learning in cognitive agents and a canonical Buddhist account of concept formation, specifically as developed by the Indian philosopher Dharmakīrti (c. 6-7th century C.E.). Our approach posits that cognitive agents form goal-dependent conceptual representations of the world that reflect subjective preferences about properties of simulated sensorimotor experience streams. By drawing on Dharmakīrti’s ideas to inform computational models of goal-directed learning, our approach bears on current debates regarding the prerequisites for and implications of artificially emulating human-like cognition.