| Abstract: | We present a memory-based approach to learning commonsense causal relations from episodic text. The method relies on dynamic memory that consists of events, event schemata, episodes, causal heuristics, and causal hypotheses. The learning algorithms are based on applying causal heuristics to precedents of new information. The heuristics are derived from principles of causation, and, to a limited extent, from domain-related causal reasoning. Learning is defined as finding—and later augmenting—inter-episodal and intea-episodal causal connections. The learning algorithms enable inductive generalization of causal associations into AND/OR graphs. The methodology has been implemented and tested in the program NEXUS. |
