This paper introduces a concept called task muddiness as a metric for higher intelligence. Task muddiness is meant to be inclusive and expendable in nature. The intelligence required to execute a task is measured by the composite muddiness of the task described by multiple muddiness factors. The composite muddiness explains why many challenging tasks are muddy and why autonomous mental development is necessary for muddy tasks. It facilitates better understanding of intelligence, what the human adult mind can do, and how (...) to build a machine to acquire higher intelligence. The task-muddiness indicates a major reason why a higher biological mind is autonomously developed from autonomous, simple-to-complex experience. The paper also discusses some key concepts that are necessary for understanding the mind and intelligence, such as intelligence metrics, the mode a task is conveyed to the task executor, a human and a machine being a joint task performer in the traditional artificial intelligence (AI), a developmental agent (human or machine) being a sole task performer, and the need for autonomy in task-nonexplicit learning. (shrink)

Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training instances and (...) rules, and based on the new representation, a Human-Like Learning (HULL) algorithm for incrementally learning concepts well in imperfect training environments. Several examples are given to make the algorithm clearer. Finally, experimental results are presented that show the proposed learning algorithm works well in imperfect learning environments. (shrink)

The book presents the case that cognitive science should turn its attention to developing theories of human cognition that cover the full range of human perceptual, cognitive, and action phenomena. Cognitive science has now produced a massive number of high-quality regularities with many microtheories that reveal important mechanisms. The need for integration is pressing and will continue to increase. Equally important, cognitive science now has the theoretical concepts and tools to support serious attempts at unified theories. The argument is made (...) entirely by presenting an exemplar unified theory of cognition both to show what a real unified theory would be like and to provide convincing evidence that such theories are feasible. The exemplar is SOAR, a cognitive architecture, which is realized as a software system. After a detailed discussion of the architecture and its properties, with its relation to the constraints on cognition in the real world and to existing ideas in cognitive science, SOAR is used as theory for a wide range of cognitive phenomena: immediate responses ; discrete motor skills ; memory and learning ; problem solving ; language ; and development. The treatments vary in depth and adequacy, but they clearly reveal a single, highly specific, operational theory that works over the entire range of human cognition, SOAR is presented as an exemplar unified theory, not as the sole candidate. Cognitive science is not ready yet for a single theory – there must be multiple attempts. But cognitive science must begin to work toward such unified theories. (shrink)