1,330 research outputs found
Assessing relevance
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
Coping with the Limitations of Rational Inference in the Framework of Possibility Theory
Possibility theory offers a framework where both Lehmann's "preferential
inference" and the more productive (but less cautious) "rational closure
inference" can be represented. However, there are situations where the second
inference does not provide expected results either because it cannot produce
them, or even provide counter-intuitive conclusions. This state of facts is not
due to the principle of selecting a unique ordering of interpretations (which
can be encoded by one possibility distribution), but rather to the absence of
constraints expressing pieces of knowledge we have implicitly in mind. It is
advocated in this paper that constraints induced by independence information
can help finding the right ordering of interpretations. In particular,
independence constraints can be systematically assumed with respect to formulas
composed of literals which do not appear in the conditional knowledge base, or
for default rules with respect to situations which are "normal" according to
the other default rules in the base. The notion of independence which is used
can be easily expressed in the qualitative setting of possibility theory.
Moreover, when a counter-intuitive plausible conclusion of a set of defaults,
is in its rational closure, but not in its preferential closure, it is always
possible to repair the set of defaults so as to produce the desired conclusion.Comment: Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996
Putting Inferentialism and the Suppositional Theory of Conditionals to the Test
This dissertation is devoted to empirically contrasting the Suppositional Theory of conditionals, which holds that indicative conditionals serve the purpose of engaging in hypothetical thought, and Inferentialism, which holds that indicative conditionals express reason relations. Throughout a series of experiments, probabilistic and truth-conditional variants of Inferentialism are investigated using new stimulus materials, which manipulate previously overlooked relevance conditions. These studies are some of the first published studies to directly investigate the central claims of Inferentialism empirically.
In contrast, the Suppositional Theory of conditionals has an impressive track record through more than a decade of intensive testing. The evidence for the Suppositional Theory encompasses three sources. Firstly, direct investigations of the probability of indicative conditionals, which substantiate āthe Equationā (P(if A, then C) = P(C|A)). Secondly, the pattern of results known as āthe defective truth tableā effect, which corroborates the de Finetti truth table. And thirdly, indirect evidence from the uncertain and-to-if inference task.
Through four studies each of these sources of evidence are scrutinized anew under the application of novel stimulus materials that factorially combine all permutations of prior and relevance levels of two conjoined sentences. The results indicate that the Equation only holds under positive relevance (P(C|A) ā P(C|Ā¬A) \u3e 0) for indicative conditionals. In the case of irrelevance (P(C|A) ā P(C|Ā¬A) = 0), or negative relevance (P(C|A) ā P(C|Ā¬A) \u3c 0), the strong relationship between P(if A, then C) and P(C|A) is disrupted. This finding suggests that participants tend to view natural language conditionals as defective under irrelevance and negative relevance (Chapter 2). Furthermore, most of the participants turn out only to be probabilistically coherent
above chance levels for the uncertain and-to-if inference in the positive relevance condition, when applying the Equation (Chapter 3). Finally, the results on the truth table task indicate that the de Finetti truth table is at most descriptive for about a third of the participants (Chapter 4).
Conversely, strong evidence for a probabilistic implementation of Inferentialism could be obtained from assessments of P(if A, then C) across relevance levels (Chapter 2) and the participantsā performance on the uncertain-and-to-if inference task (Chapter 3). Yet the results from the truth table task suggest that these findings could not be extended to truth-conditional Inferentialism (Chapter 4). On the contrary, strong dissociations could be found between the presence of an effect of the reason relation reading on the probability and acceptability evaluations of indicative conditionals (and connate sentences), and the lack of an effect of the reason relation reading on the truth evaluation of the same sentences. A birdās eye view on these surprising results is taken in the final chapter and it is discussed which perspectives these results open up for future research
Qualitative probabilistic inference under varied entropy levels
In previous work, we studied four well known systems of qualitative probabilistic inference, and presented data from computer simulations in an attempt to illustrate the performance of the systems. These simulations evaluated the four systems in terms of their tendency to license inference to accurate and informative conclusions, given incomplete information about a randomly selected probability distribution. In our earlier work, the procedure used in generating the unknown probability distribution (representing the true stochastic state of the world) tended to yield probability distributions with moderately high entropy levels. In the present article, we present data charting the performance of the four systems when reasoning in environments of various entropy levels. The results illustrate variations in the performance of the respective reasoning systems that derive from the entropy of the environment, and allow for a more inclusive assessment of the reliability and robustness of the four systems
Relevance differently affects the truth, acceptability, and probability evaluations of āandā, ābutā, āthereforeā, and āifāthenā
In this study we investigate the influence of reason-relation readings of indicative conditionals and āandā/ābutā/āthereforeā sentences on various cognitive assessments. According to the Frege-Grice tradition, a dissociation is expected. Specifically, differences in the reason-relation reading of these sentences should affect participantsā evaluations of their acceptability but not of their truth value. In two experiments we tested this assumption by introducing a relevance manipulation into the truth-table task as well as in other tasks assessing the participantsā acceptability and probability evaluations. Across the two experiments a strong dissociation was found. The reason-relation reading of all four sentences strongly affected their probability and acceptability evaluations, but hardly affected their respective truth evaluations. Implications of this result for recent work on indicative conditionals are discussed
Application of aboutness to functional benchmarking in information retrieval
Experimental approaches are widely employed to benchmark the performance of an information retrieval (IR) system. Measurements in terms of recall and precision are computed as performance indicators. Although they are good at assessing the retrieval effectiveness of an IR system, they fail to explore deeper aspects such as its underlying functionality and explain why the system shows such performance. Recently, inductive (i.e., theoretical) evaluation of IR systems has been proposed to circumvent the controversies of the experimental methods. Several studies have adopted the inductive approach, but they mostly focus on theoretical modeling of IR properties by using some metalogic. In this article, we propose to use inductive evaluation for functional benchmarking of IR models as a complement of the traditional experiment-based performance benchmarking. We define a functional benchmark suite in two stages: the evaluation criteria based on the notion of "aboutness," and the formal evaluation methodology using the criteria. The proposed benchmark has been successfully applied to evaluate various well-known classical and logic-based IR models. The functional benchmarking results allow us to compare and analyze the functionality of the different IR models
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