There are many models of mind, and many different exemplars of agent architectures. Some models of mind map onto computational designs and some agent architectures are capable of supporting different models of mind. Many agent architectures are competency-based designs related to tasks in specific domains. The more general frameworks map across tasks and domains. These types of agent architectures are capable of many cognitive competencies associated with a functioning mind. However, there is a problem with many of these approaches when they are applied to the design of a mind analogous in type to the human mind - there is no core other than an information processing architecture. As any specific architecture is applied to different domains, the information processing content (knowledge and behaviours) of the architecture changes wholesale. From the perspective of developing intelligent computational systems this is more than acceptable. From the perspective of developing or simulating functioning (human-like) minds this is problematic - these models are in effect autistic. This paper presents an emotion-based core for mind. This work draws on evidence from neuroscience, philosophy and psychology. As an agent monitors its internal interactions and relates these to tasks in its external environment, the impetus for change within itself (i.e. a need to learn) is manifested as an unwanted combination of emotions (a disequilibrium). The internal landscape of emotion, control states and dispositions provides a basis for a computational model of personality (and consciousness).
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