How Minds Work: A Cognitive Theory of Everything - a Tutorial on the IDA Model of Cognition

The IDA model of cognition is a fully integrated artificial cognitive system reaching across the full spectrum of cognition, from low-level perception/action to high-level reasoning. Extensively based on empirical data, it accurately reflects the full range of cognitive processes found in natural cognitive systems. As a source of plausible explanations for very many cognitive processes, the IDA model provides an ideal tool to think with about how minds work. This online tutorial offers a reasonably full account of the IDA conceptual model, including background material. It also provides a high-level account of the underlying computational “mechanisms of mind” that constitute the IDA computational model.

Is a General Theory of Socially Disapproved Violence Possible (or Necessary)?

A model of theoretical science is set forth to guide the formulation of general theories around abstract concepts and processes. Such theories permit explanatory application to many phenomena that are not ostensibly alike, and in so doing encompass socially disapproved violence, making special theories of violence unnecessary. Though none is completely adequate for the explanatory job, at least seven examples of general theories that help account for deviance make up the contemporary theoretical repertoire. From them, we can identify abstractions built around features of offenses, aspects of individuals, the nature of social relationships, and different social processes. Although further development of general theories may be hampered by potential indeterminacy of the subject matter and by the possibility of human agency, maneuvers to deal with such obstacles are available.

Foundations of stochastic geometry and theory of random sets

The first section of this chapter starts with the Buffon problem, which is one of the oldest in stochastic geometry, and then continues with the definition of measures on the space of lines. The second section defines random closed sets and related measurability issues, explains how to characterize distributions of random closed sets by means of capacity functionals and introduces the concept of a selection. Based on this concept, the third section starts with the definition of the expectation and proves its convexifying effect that is related to the Lyapunov theorem for ranges of vector-valued measures. Finally, the strong law of large numbers for Minkowski sums of random sets is proved and the corresponding limit theorem is formulated. The chapter is concluded by a discussion of the union-scheme for random closed sets and a characterization of the corresponding stable laws.