Models of learning word meanings have generally assumed prior knowledge of the concepts to which the words refer. However, novel natural language text or discourse can often present both unknown concepts and words which refer to these concepts. Also, developmental data suggests that the learning of words and their concepts frequently occurs concurrently instead of concept learning proceeding word learning. This paper presents an integrated computational model for acquiring both word meanings and their underlying concepts concurrently. This model is implemented as a word learning component added to the CI-NLSIS explanation-based schema acquisition system for narrative understanding. A detailed example is described in which CnNHSIS learns provisional definitions for the words "kidnap", "kidnapper", and "ransom" as well as a kidnapping schema from a single narrative.

This study describes a psychological experiment on biases that people exhibit in refining probabilistic causal knowledge. In the experiment, the effect of background knowledge was shown by manipulating the causal structure of prior knowledge provided to the subjects. It was found that later training instances affected the refinement of the background knowledge in different ways depending on the causal model initially given to the subjects. The two biases found in the current experiment are (1) knowledge refinement was conservative in the sense that background knowledge was modified as little as possible to account for the observed data and (2) weakening of an existing causal relationship resulted in automatic strengthening of a related causal relationship.

Student modeling has been identified as an important component to the long term development of Intelligent Computer-Aided Instruction (ICAI) systems. Two basic approaches have evolved to model student misconceptions. One uses a static, predefined library of user bugs which contains the misconceptions modeled by the system. The other uses induction to learn student misconceptions from scratch. Here, we present a third approach that uses a machine learning technique called theory revision. Using theory revision allows the system to automatically construct a bug library for use in modeling while retaining the flexibility to address novel errors.

Recent explanalion-based learning (EBL) models in Al allow a computer program to learn aschema by analyzing a single example. For example. GENESIS is an EBL system which learns a planschema from a single specific instance presented in a narrative. Previous learning models in both Aland psychology have required multiple examples. This paper presents experimental evidence thatpeople can learn a plan schema from a single narrative and that the learned schema agrees with thatpredicted by EBL. This evidence suggests that GENESIS, originally constructed as a machine learningsystem, can be interpreted as a psychological model of learning a complex schema from a singleexample.