Abstract
This paper discusses the representation of propositional attitudes (beliefs, etc.) in connectionist systems that do not implement symbolic represen tations. One prominent way of symbolically representing attitudes is through meta-representational schemes. These have representational expressions that themselves refer to representational expressions. Meta-representation is one of the most expressively powerful symbolic approaches for attitude representation. Therefore: could non-implementationai connectionist systems use an analogous approach? Unfortunately, it is not straightforward to devise a plausible analogy. The paper looks at three main possibilities: (i) the representational activation patterns of the non-implementational connectionist system refer to the system's own activation patterns; (ii) the activation patterns refer to formal symbolic expressions; and (iii) the activation patterns refer to natural-language expressions. These approaches, which are not claimed to be exhaustive, concentrate respectively on the following facts about symbolic meta-representation schemes: (1) they typically refer to their own representational expressions; (2) the schemes typically refer to formal symbolic expressions; although (3) some of the schemes refer to natural language expressions. The article briefly argues that possibility (iii) avoids some of the problems of (i) and (ii). There are also two independent, non connectionism-derived reasons for considering (iii). One is that it is strongly related to a prevalent commonsense metaphor of beliefs and so on as internal, natural language utterances. (The other is to do with the heightened difficulty of handling vague quantification within propositional attitude contexts, but is discussed elsewhere and not in the present paper.) The paper as a whole highlights the point that even a non-implementational connectionist system must be able to think about complex symbolic constructs such as logic expressions and natural language phrases, even though it does not think with them.
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More From: Journal of Experimental & Theoretical Artificial Intelligence
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