Symbolic Reasoning with Bounded Cognitive Resources

We present a multi-domain computational model for symbolic reasoning that was designed with the aim of matching human performance. The computational model is able to reason by deduction, induction, and abduction. It begins with an arbitrary theory in a given domain and gradually extends this theory as new regularities are learned from positive and negative examples. At the core of the computational model is a cognitive model with bounded cognitive resources. The combinatorial explosion problem, which frequently arises in inductive learning, is tackled by searching for solutions inside this cognitive model only. By way of example, we show that the computational model can learn elements of two different domains, namely arithmetic and English grammar

Symbolic Reasoning with Bounded Cognitive Resources

We present a multi-domain computational model for symbolic reasoning that was designed with the aim of matching human performance. The computational model is able to reason by deduction, induction, and abduction. It begins with an arbitrary theory in a given domain and gradually extends this theory as new regularities are learned from positive and negative examples. At the core of the computational model is a cognitive model with bounded cognitive resources. The combinatorial explosion problem, which frequently arises in inductive learning, is tackled by searching for solutions inside this cognitive model only. By way of example, we show that the computational model can learn elements of two different domains, namely arithmetic and English grammar