In this article we strive to provide a detailed and principled analysis of the role of beliefs in goal processing—that is, the cognitive transition that leads from a mere desire to a proper intention. The resulting model of belief-based goal processing has also relevant consequences for the analysis of intentions, and constitutes the necessary core of a constructive theory of intentions, i.e. a framework that not only analyzes what an intention is, but also explains how it becomes what it is. (...) We discuss similarities and differences between our approach and other standard accounts of intention, in particular Bratman’s planning theory. The aim here is to question and refine the conceptual foundations of many theories of intentional action: as a consequence, although our analysis is not formal in itself, it is ultimately meant to have deep consequences for formal models of intentional agency. (shrink)

We focus on the production of efficient descriptions of objects, actions and events. We define a type of efficiency, textual economy, that exploits the hearer’s recognition of inferential links to material elsewhere within a sentence. Textual economy leads to efficient descriptions because the material that supports such inferences has been included to satisfy independent communicative goals, and is therefore overloaded in the sense of Pollack [18]. We argue that achieving textual economy imposes strong requirements on the representation and reasoning used (...) in generating sentences. The representation must support the generator’s simultaneous consideration of syntax and semantics. Reasoning must enable the generator to assess quickly and reliably at any stage how the hearer will interpret the current sentence, with its (incomplete) syntax and semantics. We show that these representational and reasoning requirements are met in the SPUD system for sentence planning and realization. (shrink)

Choosing the right plan is often choosing the more coherent plan: but what is coherence? We argue that coherence-directed practical inference ought to be represented computationally. To that end, we advance a theory of deliberative coherence, and describe its implementation in a program modelled on Thagard's ECHO. We explain how the theory can be tested and extended, and consider its bearing on instrumentalist accounts of practical rationality.

Dans cet article, je montre que quatre normes de rationalité associées aux intentions peuvent être déduites de normes similaires s’appliquant aux acceptations en contextes délibératifs, un type d’état mental apparenté mais irréductible aux croyances par lequel un agent tient certains faits pour acquis lorsqu’il délibère. Je montre que cette approche, que je nomme le pragmatisme hybride, évite certaines limitations de l’approche la plus prisée dans la littérature, le cognitivisme, et qu’en comparaison avec les approches purement pragmatistes, principales rivales du cognitivisme, (...) le pragmatisme hybride rend mieux justice à nos intuitions relatives aux normes associées aux intentions. Je montre enfin que le pragmatisme hybride permet d’expliquer comment les intentions influencent le raisonnement pratique, et de ce fait, comblent un vide important dans les théories contemporaines.In this paper, I show that four normative requirements on rational intentions follow from similar requirements on acceptations in deliberative contexts, a type of mental state close but irreducible to beliefs by which agents take some facts for granted in deliberation. I show that this approach, which I call hybrid pragmatism, avoid shortcomings of cognitivism, one of the most prominent one in the literature. I show furthermore that in comparison with purely pragmatist approaches, the main rivals of cognitivism, an hybrid form of pragmatism makes better justice to our intuitions regarding the sources of the normative requirements on rational intentions, and that it allows for understanding how intentions influence practical reasoning. (shrink)

Algorithms for NLG NLG is typically broken down into stages of discourse planning (to select information and organize it into coherent paragraphs), sentence planning (to choose words and structures to fit information into sentence-sized units), and realization (to determine surface form of output, including word order, morphology and final formatting or intonation). The SPUD system combines the generation steps of sentence planning and surface realization by using a lexicalized grammar to construct the syntax and semantics of a sentence simultaneously.

I show how a conversational process that takes simple, intuitively meaningful steps may be understood as a sophisticated computation that derives the richly detailed, complex representations implicit in our knowledge of language. To develop the account, I argue that natural language is structured in a way that lets us formalize grammatical knowledge precisely in terms of rich primitives of interpretation. Primitives of interpretation can be correctly viewed intentionally, as explanations of our choices of linguistic actions; the model therefore fits our (...) intuitions about meaning in conversation. Nevertheless, interpretations for complex utterances can be built from these primitives by simple operations of grammatical derivation. In bridging analyses of meaning at semantic and symbol‐processing levels, this account underscores the fundamental place for computation in the cognitive science of language use. (shrink)