Rethinking the Physical Symbol Systems Hypothesis

It is now more than a half-century since the Physical Symbol Systems Hypothesis (PSSH) was first articulated as an empirical hypothesis. More recent evidence from work with neural networks and cognitive architectures has weakened it, but it has not yet been replaced in any satisfactory manner. Based on a rethinking of the nature of computational symbols -- as atoms or placeholders -- and thus also of the systems in which they participate, a hybrid approach is introduced that responds to these challenges while also helping to bridge the gap between symbolic and neural approaches, resulting in two new hypotheses, one that is to replace the PSSH and other focused more directly on cognitive architectures.Comment: Final version published at the the 16th Annual AGI Conference, 202

Thoughts on Architecture

The term architecture has evolved considerably from its original Greek roots and its application to buildings and computers to its more recent manifestation for minds. This article considers lessons from this history, in terms of a set of relevant distinctions introduced at each of these stages and a definition of architecture that spans all three, and a reconsideration of three key issues from cognitive architectures for architectures in general and cognitive architectures more particularly

Defining and Explorting the Intelligence Space

Intelligence is a difficult concept to define, despite many attempts at doing so. Rather than trying to settle on a single definition, this article introduces a broad perspective on what intelligence is, by laying out a cascade of definitions that induces both a nested hierarchy of three levels of intelligence and a wider-ranging space that is built around them and approximations to them. Within this intelligence space, regions are identified that correspond to both natural -- most particularly, human -- intelligence and artificial intelligence (AI), along with the crossover notion of humanlike intelligence. These definitions are then exploited in early explorations of four more advanced, and likely more controversial, topics: the singularity, generative AI, ethics, and intellectual property.Comment: May ultimately appear as a journal article and/or a book chapte

On Unified Theories of Cognition: a response to the reviews

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30999/1/0000674.pd

Two frameworks for integrating knowledge in induction

The use of knowledge in inductive learning is critical for improving the quality of the concept definitions generated, reducing the number of examples required in order to learn effective concept definitions, and reducing the computation needed to find good concept definitions. Relevant knowledge may come in many forms (such as examples, descriptions, advice, and constraints) and from many sources (such as books, teachers, databases, and scientific instruments). How to extract the relevant knowledge from this plethora of possibilities, and then to integrate it together so as to appropriately affect the induction process is perhaps the key issue at this point in inductive learning. Here the focus is on the integration part of this problem; that is, how induction algorithms can, and do, utilize a range of extracted knowledge. Preliminary work on a transformational framework for defining knowledge-intensive inductive algorithms out of relatively knowledge-free algorithms is described, as is a more tentative problems-space framework that attempts to cover all induction algorithms within a single general approach. These frameworks help to organize what is known about current knowledge-intensive induction algorithms, and to point towards new algorithms

A Specification of the Soar Cognitive Architecture in Z

A formal specification of the sixth revision of the Soar architecture in the Z notation was constructed to elucidate and clarify the definition of Soar and to guide its implementation. Soar is a cognitive architecture that has been successfully applied to many domains and has been proposed as an exemplar unified theory of cognition. Z is a model theoretic specification language based in set theory that has syntax and type checking programs available. The specification has a complete coverage of the architecture, a low level of abstraction and a considerable implementation bias

The Diggable City: Making Urban Agriculture a Planning Priority

In addition to an inventory of potential urban agriculture sites, the team also conducted a literature review, held focus groups with relevant stakeholders, conducted numerous interviews, and administered and analyzed surveys. The results of these outreach efforts greatly informed criteria development and recommendations, and expanded our understanding of the potential for urban agriculture in Portland. This project was conducted under the supervision of Sy Adler, Deborah Howe, and Connie Ozawa. A DVD version of this work produced in 2006 can be found at: http://search.library.pdx.edu/PSU:CP7111350869000145

Emotion in the Common Model of Cognition

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition

A preliminary analysis of the Soar architecture as a basis for general intelligence

In this article we take a step towards providing an analysis of the Soar architecture as a basis for general intelligence. Included are discussions of the basic assumptions underlying the development of Soar, a description of Soar cast in terms of the theoretical idea of multiple levels of description, an example of Soar performing multi-column subtraction, and three analyses of Soar: its natural tasks, the sources of its power, and its scope and limitsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29595/1/0000684.pd