The Cascade model of cognitive skill acquisition was developed to integrate a number of AI techniques and to account for psychological results on the self-explanation effect. In previous work, we compared Cascade's behavior to aggregate data collected from the protocols of 9 subjects in a self evaluation study. Here, we report the results of a fine-grained analysis, in which we matched Cascade's behavior to the individual protocols of each of the subjects. Our analyses demonstrate empirically that cascade is a good model of subject behavior at the level of goals and inferences. It covers aliout lTi%, of the subjects" example-studying behavior and 6 0 % to 9 0 % of their problem-solving l)ehavior. In addition, this research forced us to (leveloj) general feasible methods for matching a simulation to large protocols (approximately 3000 stages total). Finally, the analyses point out some weaknesses in the Cascade system and provide us vvitli direction for future analyses of the model and data.

The SuM-to-MiN transition that children exhibit when learning to add provides an ideal domain for studying naturally occurring discovery processes. We discuss a computational model that accounts for this transition, including the appropriate intermediate strategies. In order to account for all of these shifts, the model must sometimes learn without the benefit of impasses. Our model smoothly integrates impasse-driven and impasse free learning in a single, simple learning mechanism.

Several studies have found that learning is more effective when students explain examples to themselves. Although these studies show that learning and self-explanation co-occur, they do not reveal why. Three explanations have been proposed and computational models have been built for each. T h e gap-filling explanation is that self-explanation causes subjects to detect and hll gaps in their domain knowledge. T h e schema formation explanation is that self-explanation causes the learner to abstract general solution procedures and associate each with a general description of the problems it appUes to. T h e analogical enhancement explanation is that self- explanation cause a richer elaboration of the ex- ample, which facilitates later use of the example for analogical problem solving. W e claim that, in one study at least, gapfilling accounts for most of the self-explanation effect.

Eureka is a problem-solving system that operates through a form of analogical reasoning. The system was designed to study how relatively low-level memory, reasoning, and learning mechanisms can account for high-level learning in human problem solvers. Thus, Eureka's design has focused on issues of memory representation and retrieval of analogies, at the expense of complex problem-solving ability or sophisticated analogical elaboration techniques. Two computational systems for analogical reasoning, ARCS/ACM E and MAC/FAC, are relatively powerful and well-known in the cognitive science literature. However, they have not addressed issues of learning, and they have not been implemented in the context of a performance task that can dictate what makes an analogy "good". Thus, it appears that these different research directions have much to offer each other W e describe the Eureka system and compare its analogical retrieval mechanism with those in ARC S and MAC/FAC. W e then discuss the issues involved in incorporating ARC S and MAC/FAC into a learning problem solver such as Eureka.

The TacAir-Soar system is a computer program that generates human-like behavior flying simulated aircraft in tactical air combat training scenarios. The design of the system has been driven by functional concerns, allowing the system to generate a wide range of appropriate behaviors in severely time-limited situations. The combination of constraints from the complexity and dynamics of the domain with the overall goal of human-like behavior led to a system that can be viewed as a model of cognition for high-level, complex tasks. This paper analyzes the system in such a light, and describes how the functional design constraints map on to cognitively plausible representations and mechanisms, sometimes in surprising ways.

Several investigations have found that students learn more when they explain examples to themselves while studying them. Moreover, they refer less often to the examples while solving problems, and they read less of the example each time they refer to it. These findings, collectively called the self-explanation effect, have been reproduced by our cognitive simulation program. Cascade. Cascade has two kinds of learning. It learns new rules of physics (the task domain used in the human data modeled) by resolving impasses with reasoning based on overly-general, non-domain knowledge. It acquires procedural competence by storing its derivations of problem solutions and using them as analogs to guide its search for solutions to novel problems. This paper discusses several runs of Cascade wherein the strategies for explaining examples is varied and the initial domain knowledge b held constant. These computational experiments demonstrate the computational sufficiency of a strategy-based account for the self-explanation effect.