Perceptual Memory and Learning : Recognizing , Categorizing , and Relating

In this position paper we attempt to derive an architecture and mechanism for perceptual memory and learning for software agents and robots from what is known, or believed, about the same faculties in human and other animal cognition. Based on that of the IDA model of Global Workspace Theory, a conceptual and computational model of cognition, this architecture, together with its mechanisms, offers the real possibility of autonomous software agents and robots learning their own ontologies during a developmental period. Thus the onerous chore of designing and implementing such an ontology can be avoided.

[1]  R. Yerkes,et al.  The relation of strength of stimulus to rapidity of habit‐formation , 1908 .

[2]  Irene M. Pepperberg,et al.  Cognition in an African Grey parrot , 1991 .

[3]  Arpad Kelemen,et al.  A Comparative Study of Different Machine Learning Approaches for Decision Making , 2005 .

[4]  Stan Franklin,et al.  An action selection mechanism for "conscious" software agents , 2002 .

[5]  J. Mandler.  Perceptual and Conceptual Processes in Infancy , 2000 .

[6]  A. Baddeley.  The episodic buffer: a new component of working memory? , 2000, Trends in Cognitive Sciences.

[7]  B. Baars.  A cognitive theory of consciousness , 1988 .

[8]  Pentti Kanerva,et al.  Sparse Distributed Memory , 1988 .

[9]  W. Tuckwell,et al.  The Honey Bee , 1891, Nature.

[10]  Melanie Mitchell,et al.  The Copycat project: a model of mental fluidity and analogy-making , 1995 .

[11]  Stan Franklin,et al.  Conscious software: a computational view of mind , 2002 .

[12]  P. Maes.  How to Do the Right Thing , 1989 .

[13]  Stan Franklin,et al.  Automating human information agents , 2002 .

[14]  Stan Franklin,et al.  Sparse distributed memory for ‘conscious’ software agents , 2003, Cognitive Systems Research.

[15]  Stan Franklin,et al.  Deliberation and Voluntary Action in "Conscious" Software Agents , 2000 .

[16]  Arthur C. Graesser,et al.  Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents , 1996, ATAL.

[17]  L. Barsalou,et al.  Whither structured representation? , 1999, Behavioral and Brain Sciences.

[18]  S. Corkin,et al.  Intact priming of patterns despite impaired memory , 1990, Neuropsychologia.

[19]  Arthur C. Graesser,et al.  Natural language sensing for autonomous agents , 1998, Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174).

[20]  Victor S. Johnston.  Why We Feel: The Science Of Human Emotions , 1999 .

[21]  K. A. Ericsson,et al.  Long-term working memory. , 1995, Psychological review.

[22]  Salvatore Sessa,et al.  Soft computing agents: new trends for designing autonomous systems , 2002 .

[23]  B. Baars,et al.  How conscious experience and working memory interact , 2003, Trends in Cognitive Sciences.

[24]  G. Handelmann,et al.  Hippocampus, space, and memory , 1979 .

[25]  Feelings and Emotions as Motivators and Learning Facilitators , 2004 .

[26]  Stan Franklin,et al.  Consciousness and conceptual learning in a socially situated agent , 2000 .

[27]  B. Baars.  The conscious access hypothesis: origins and recent evidence , 2002, Trends in Cognitive Sciences.

[28]  Stan Franklin,et al.  IDA, a Conscious Artifact? , 2006 .

[29]  John V. Jackson.  Idea for a mind , 1987, SGAR.

[30]  Stan Franklin,et al.  Modeling Consciousness and Cognition in Software Agents , 2000 .

[31]  Irene Daum,et al.  Perceptual learning in amnesia , 2002, Neuropsychologia.

[32]  J. A. Hobson,et al.  A neurocognitive approach to dreaming , 1994 .