Seven features which in practice seem to differentiate belief systems from knowledge systems are discussed. These are: nonconsensuality, “existence beliefs,” alternative worlds, evaluative components, episodic material, unboundedness, and variable credences. Each of these features gives rise to challenging representation problems. Progress on any of these problems within artificial intelligence would be helpful in the study of knowledge systems as well as belief systems, inasmuch as the distinction between the two types of systems is not absolute.

The definition and the assessment of the quality of argumentative texts has become an increasingly crucial issue in education, classroom discourse, and argumentation theory. The different methods developed and used in the literature are all characterized by specific perspectives that fail to capture the complexity of the subject matter, which remains ill-defined and not systematically investigated. This paper addresses this problem by building on the four main dimensions of argument quality resulting from the definition of argument and the literature in (...) classroom discourse: dialogicity, accountability, relevance, and textuality (DART). We use and develop the insights from the literature in education and argumentation by integrating the frameworks that capture both the textual and the argumentative nature of argumentative texts. This theoretical background will be used to propose a method for translating the DART dimensions into specific and clear proxies and evaluation criteria. (shrink)

This paper advances an approach to relevance grounded on patterns of material inference called argumentation schemes, which can account for the reconstruction and the evaluation of relevance relations. In order to account for relevance in different types of dialogical contexts, pursuing also non-cognitive goals, and measuring the scalar strength of relevance, communicative acts are conceived as dialogue moves, whose coherence with the previous ones or the context is represented as the conclusion of steps of material inferences. Such inferences are described (...) using argumentation schemes and are evaluated by considering 1) their defeasibility, and 2) the acceptability of the implicit premises on which they are based. The assessment of both the relevance of an utterance and the strength thereof depends on the evaluation of three interrelated factors: 1) number of inferential steps required; 2) the types of argumentation schemes involved; and 3) the implicit premises required. (shrink)

This study concerns topic selection in conversational reports of a personal event, the birth of a baby. Ninety phone calls from 12 fathers and 7 mothers were analyzed in terms of the subject's prior concerns (prenatal questionnaire) and the outcome of events (postpartum questionnaire). Four analyses were conducted. The first showed that subjects were likely to mention topics of high, rather than low, prior concern, and unusual, rather than ordinary, outcome. The second showed that chronologically early topics were discussed before (...) late topics. These findings were compatible with either a memory (i.e., “knowledge telling”) or a communication (i.e., “knowledge processing”) model of topic selection. Two additional analyses were conducted to evaluate predictions derived from each of these models. The first analysis showed that the conversations also contained a small set of pro forma topics that appeared earlier and more frequently than other topics. The second showed that these topics were more likely to be recipient‐initiated. These results suggest that reporters deviated from an optimal retrieval plan to take the recipient into account. The results as a whole support a communication‐based, “knowledge processing” (Bereiter & Scardomalia, 1980) model of topic selection. (shrink)