Saturday, December 22, 2007

The Semantic Web begets the Pragmatic Web 

A group working on the problems of context dependence and the social process of building common knowledge of ontologies. There is a web site here: with a manifesto: The Pragmatic Web: a Manifesto By Mareike Schoop, Aldo de Moor, and Jan L.G. Dietz (PDF 2006)

"The Web has been extremely successful in enabling information sharing among a seemingly unlimited number of people worldwide. The evergrowing amount of documents on the Web, however, results in information overload and often makes it difficult to discover the information that is relevant. The goal of the Semantic Web is to develop the basis for intelligent applications that enable more efficient information use by not just providing a set of linked documents but a collection of knowledge repositories with meaningful content and additional logic structure.

Data and rules for reasoning about data and information are systematically described, for example by using the Resource Description Framework (RDF), after which they can be more easily shared and used by people as well as by distributed software agents. The main components for implementing the Semantic Web are ontologies. Ontologies represent concepts and relations between the concepts; these can be hierarchical relations, whole-part relations, or any other meaningful type of linkage between the concepts.

Will it work this way? According to Rob McCool, cofounder of the large-scale RDF project TAP, the answer is negative. “Because it’s a complex format and requires users to sacrifice expressivity and pay enormous costs in translation and maintenance, the Semantic Web will never achieve its widespread public adoption.” The most problematic assumption is that context-free facts and logical rules would be sufficient [1]. Internet researcher Munindar Singh, wellknown for his pioneering work on agent communication, writes: “If there is one lesson to be learned from the long history of databases, it is that it is practically impossible to describe data well enough for it to be used in arbitrary applications” [2]."

Thursday, December 20, 2007

An Approach to the Problem of Representation 

Representational Content in Humans and Machines by Mark H. Bickhard (PDF 1993)

This article focuses on the problem of representational content. Accounting for
representational content is the central issue in contemporary naturalism: it is the
major remaining task facing a naturalistic conception of the world.
Representational content is also the central barrier to contemporary cognitive
science and artificial intelligence: it is not possible to understand representation
in animals nor to construct machines with genuine representation given current
(lack of) understanding of what representation is. An elaborated critique is
offered to current approaches to representation, arguing that the basic underlying
approach is, at root, logically incoherent, and, thus, that standard approaches are
doomed to failure. An alternative model of representation — interactivism — is
presented that avoids or solves the problems facing standard approaches.
Interactivism is framed by a version of functionalism, and a naturalization of that
functionalism completes an outline of a naturalization of representation and
representational content."

Tuesday, December 18, 2007

Solving the Symbol Grounding Problem with Neural Nets 



Searle's Chinese Room Argument (that rule-based symbol manipulation is not enough for symbol-understanding) is based on a symptom of the Symbol Grounding Problem (that rule-based symbol manipulation is circular and ungrounded). Symbols must be grounded directly in the capacity to identify and interact with the objects they designate. One candidate way to do this is to use neural nets to try to give a robot Turing-scale sensorimotor capacities congruent with its Turing-scale linguistic capacities. Such a grounded hybrid symbolic/dynamic robot would be immune to Searle's Chinese Room Argument."

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