Recent discoveries in automated planning are broadening the scope of planners, from toy
problems to real applications. However, applying automated planners to real-world …

Explanation-based learning (EBL) is a technique by which an intelligent system can learn by
observing examples. EBL systems are characterized by the ability to create justified …

Safety suffers from the variety of methods and models that are used to assess human
performance. For example, operation is concerned primarily with human error, while design …

Brings together a diversity of research on goal-driven learning to establish a broad,
interdisciplinary framework that describes the goal-driven learning process. In cognitive …

Operationality is the key property that distinguishes the final description learned in an
explanation-based system from the initial concept description input to the system. Yet most …

Derivational analogy solves a problem by replaying the plan used to solve a previous
problem, modifying it where necessary. We analyze how four published systems use this …

Many theories of explanation evaluation are based on context-independent criteria. Such
theories either restrict their consideration to explanation towards a fixed goal, or assume that …

Admissible heuristics are an important class of heuristics worth discovering: they guarantee
shortest path solutions in search algorithms such as A* and they guarantee less expensively …

In cognitive science, artificial intelligence, psychology, and education, a growing body of
research supports the view that learning is largely a goal-directed process. Experimental …

Present day systems, intelligent or otherwise, are limited by the conceptualizations of the
world given to them by their designers. In this paper, we propose a novel, first-principles …