Towards a paradigm shift in belief representation methodology

Abstract Research programs must often divide issues into manageable sub-issues. The assumption is that an approach developed to cope with a sub-issue can later be integrated into an approach to the whole issue—possibly after some tinkering with the sub-approach, but without affecting its fundamental features. However, the present paper examines a case where an AI issue has been divided in a way that is, apparently, harmless and natural, but is, actually, fundamentally out of tune with the realities of the issue. As a result, some approaches developed for a certain sub-issue cannot be extended to a total approach without fundamental modification. The issue in question is that of representing and reasoning about people's beliefs, hopes, intentions and other ‘propositional attitudes’, and/or interpreting natural language sentences that report propositional attitudes. Researchers have, quite understandably, de-emphasized the problem of dealing in detail with nested attitudes (e.g., hopes about beliefs, belief...

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