Stability and plasticity : constructing cognitive agents

Cataloged from PDF version of article.The AI field is currently dominated by domain-specific approaches to intelligence and cognition instead of being driven by the aim of modeling general human intelligence and cognition. This is despite the fact that the work widely regarded as marking the birth of AI was the project of creating a general cognitive architecture by Newell and Simon 1959. This thesis aims to examine recently designed models and their various cognitive features and limitations in preparation for building our own comprehensive model that would aim to address their limitations and give a better account for human cognition. The models differ in the kind of cognitive capabilities they view as the most important. They also differ in whether their foundation is built on symbolic or sub-symbolic atomic structures. Furthermore, we will look at studies in the philosophy and cognitive psychology domain in order to better understand the requirements that need to be met in order for a system to emulate general human cognition.Bozyiğit, ÖgeM.S

Connectionist Mechanisms for Cognitive Control

An understanding of cognitive control is crucial for understanding high-level cognition and delineating the functional role of prefrontal cortex in supporting complex cognitive operations. In this paper, we approach the problem of cognitive control by examining the control needs of SHRUTI, a neurally plausible and cognitively motivated model of inference and decision-making. It is shown that processing via spreading activation has a number of limitations with respect to inference and decision-making, and specific forms of controlled processing is required to overcome these limitations. We propose a set of primitive, neurally plausible control mechanisms, including monitoring, filtering, selection, maintenance, organization, and manipulation, describe connectionist implementations of these primitive mechanisms, and demonstrate the use of several of these primitives in a complex control process. (c) 2004 Published by Elsevier B.V

Connectionist Mechanisms for Cognitive Control

An understanding of cognitive control is crucial for understanding high-level cognition and delineating the functional role of prefrontal cortex in supporting complex cognitive operations. In this paper, we approach the problem of cognitive control by examining the control needs of SHRUTI, a neurally plausible and cognitively motivated model of inference and decision-making. It is shown that processing via spreading activation has a number of limitations with respect to inference and decision-making, and specific forms of controlled processing is required to overcome these limitations. We propose a set of primitive, neurally plausible control mechanisms, including monitoring, filtering, selection, maintenance, organization, and manipulation, describe connectionist implementations of these primitive mechanisms, and demonstrate the use of several of these primitives in a complex control process. 1