Causal Generalization via Goal-Driven Analogy

Causal knowledge and reasoning allow cognitive agents to predict the outcome of their actions and infer the likely reasons behind observed events, enabling them to interact with their surroundings effectively. Causality has been the subject of some research in artificial intelligence (AI) over the past decade due to its potential for task-independent knowledge representation and generalization. Yet, the question of how the agents can autonomously generalize their causal knowledge while seeking their active goals still needs to be answered. This work introduces an analogy-based learning mechanism that enables causality-based agents to autonomously generalize their existing knowledge once the generalization aligns with the agents’ goal achievement. The methodology is centered on constructivism, causality, and analogy-making. The introduced mechanism is integrated with a general-purpose cognitive architecture, Autocatalytic Endogenous Reflective Architecture (AERA), and evaluated in a robotic experiment in a 3D simulation environment. Both empirical and analytical results show the effectiveness of this mechanism.