Motivation And Intensionality In A Computer Simulation Model

This dissertation describes a computer simulation model of paranoia. The model mimics the behavior of a patient participating in a psychiatric interview by answering questions, introducing its own topics, and responding to negatively-valued (e. g., threatening or shame-producing) situations. The work focuses on the motivational mechanisms required to instigate and direct the modelled behavior. Major components of the model are: (1) A production system (PS) formalism accounting for the instigation and guidance of behavior as a function of internal (affective) and external (real-word) environmental factors; (2) A model of affects (emotions) as an anticipation mechanism based on a small number of basic pain-pleasure factors; and (3) a formalism for intensional behavior (directed by internal models) requiring a dual representation of symbol and concept. An intensional object (belief) can be accessed either by sensing it in the environment (concept) or by its name (token). Similarly, an intensional action (intention) can be specified either by its conditions in the immediate environment (concept) or by its name (token). Issues of intelligence, psychopathological modelling, and artificial intelligence programming are discussed. The paranoid phenomenon is found to be explainable as an extremely skewed use of normal processes. Applications of these constructs are found to be useful in AI programs dealing with error recovery, incompletely specified input data, and natural language specification of tasks to perform.

Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.

Viewing the human brain as "the most complex and powerful computer known," with a memory capacity and computational power exceeding the largest mainframe systems, Professor Baron sets the groundwork for understanding the computational structure and organization of the human brain. He provides the introductory framework necessary for this new and growing field of investigation and he discusses human vision, mental imagery, sensory-motor functions, audition, affect and behavior.

Pattern-Directed Inference Systems provides a description of the design and implementation of pattern-directed inference systems (PDIS) for various applications. The book also addresses the theoretical significance of PDIS for artificial intelligence and cognitive psychology. The book is divided into eight sections. The introduction provides a brief overview of pattern-directed inference systems, including a historical perspective, a review of basic concepts, and a survey of work in this area. Subsequent chapters address topics on architecture and design, methods for accessing and controlling rule based systems, methods for obtaining adaptive behavior via rule-based systems and cognitive modeling. Constructing models of human information processing, natural language understanding and multilevel systems and complexity are described as well. The last section discusses the earlier chapters in the book and provides a unifying set of principles for the PDIS formalism. Computer scientists, psychologists, engineers, and researchers in artificial intelligence will find the book very informative.