A Formal Model of Plausibility Monitoring in Language Comprehension

AAAI Conferences

Recent work in psychology provided evidence that plausibility monitoring is a routine component of language comprehension by showing that reactions of test persons were delayed when, e.g., a positive response was required for an implausible target word. These experimental results raise the crucial question of whether, and how, the role of plausibility assessments for the processes inherent to language comprehension can be made more precise. In this paper, we show that formal approaches to plausibility from the field of knowledge representation can explain the observed phenomena in a satisfactory way. In particular, we argue that the delays in response time are caused by belief revision processes which are necessary to overcome the mismatch between plausible context (or background resp. world) knowledge and implausible target words.


On the Interrelationships Among C-Inference Relations Based on Preferred Models for Sets of Default Rules

AAAI Conferences

Various semantics have been developed for knowledge bases consisting of qualitative conditionals representing default rules. Recently, skeptical, weakly skeptical, and credulous inference relations based on c-representations and taking classes of preferred models into account have been proposed. In this paper, we investigate their interrelationships and solve several open problems regarding these interrelationships. In particular, we prove that the preferred models obtained from three different notions of minimality lead to pairwise distinct inference relations, and that none of them is able to exactly capture skeptical c-inference over all c-representations.


Intensional Combination of Rankings for OCF-Networks

AAAI Conferences

Similar to Bayesian networks, so-called OCF-networks combine structural information encoded in a directed graph with qualitative information expressed by ranking degrees of (conditional) formulas. The benefits of such techniques are twofold: First, the high complexity of the semantical ranking functions approach is reduced substantially, and second, global ranks are obtained from local information. However, in many practical applications, even the local rankings are only available in parts, or not exactly in the format that is needed. In this paper, we apply inductive reasoning methods like system Z+ or c-representations, to fill up missing values in the local conditional tables. This allows the user to specify knowledge for such OCF-networks in its most appropriate and reliable form and leave the technical details to an inference engine.


On Rational Monotony and Weak Rational Monotony for Inference Relations Induced by Sets of Minimal C-Representations

AAAI Conferences

Reasoning in the context of a conditional knowledge base containing rules of the form ’If A then usually B’ can be defined in terms of preference relations on possible worlds. These preference relations can be modeled by ranking functions that assign a degree of disbelief to each possible world. In general, there are multiple ranking functions that accept a given knowledge base. Several nonmonotonic inference relations have been proposed using c-representations, a subset of all ranking functions. These inference relations take subsets of all c-representations based on various notions of minimality into account, and they operate in different inference modes, i.e., skeptical, weakly skeptical, or credulous. For nonmonotonic inference relations, weaker versions of monotonicity like rational monotony (RM) and weak rational monotony (WRM) have been developed. In this paper, we investigate which of the inference relations induced by sets of minimal c-representations satisfy rational monotony or weak rational monotony.


Observations on the Minimality of Ranking Functions for Qualitative Conditional Knowledge Bases and Their Computation

AAAI Conferences

Ordinal conditional functions (OCFs) provide a semantic domain for qualitative conditionals of the form "if A, then (normally) B" by ordering worlds according to their degree of surprise. Transferring the idea of maximum entropy to a more qualitative domain, c-representations of a knowledge base R consisting of a set of conditionals have been defined as OCFs satisfying in particular the property of conditional indifference. While c-representations for R can be specified as the solutions of a constraint satisfaction problem CR(R), it has been an open problem whether there may be different minimal c-representations induced by minimal solutions of CR(R). Another open question has been whether particular inequations in CR(R) may be sharpened by transforming them into equations without loosing any minimal solutions, taking different notions of minimality into account. In this paper, we answer both questions and discuss further aspects of OCF minimality.