A Note on Binary Inductive Logic

We consider the problem of induction over languages containing binary relations and outline a way of interpreting and constructing a class of probability functions on the sentences of such a language. Some principles of inductive reasoning satisfied by these probability functions are discussed, leading in turn to a representation theorem for a more general class of probability functions satisfying these principles.     

Consistency Defaults

A consistency default is a propositional inference rule that asserts the consistency of a formula in its consequence. Consistency defaults allow for a straightforward encoding of domains in which it is explicitely known when something is possible. The logic of consistency defaults can be seen as a variant of cumulative default logic or as a generalization of justified default logic; it is also able to simulate Reiter default logic in the seminormal case. A semantical characterization of consistency defaults in terms of processes and in terms of a fixpoint equation is given, as well as a normal form. Presented by Melvin Fitting     

基于外域范例可拓推理的研究

基于范例推理(CBR)是人类常用的一种解决问题方式,也是人工智能的一种研究范式,在许多领域都得到了应用。但CBR也可能受到心理定势和负迁移的影响,对于创造性思维有一定的负面作用。研究对CBR进行了改进,提出了在外域搜索范例并作可拓变换的方法,称为基于外域范例的可拓推理,并将它运用到产品创新构思系统的设计中,为把认知心理学应用到国家经济建设中提供了新思路。     

对归纳推理非对称性现象的特征迁移解释

作者创新提出对归纳推理非对称性现象的特征迁移解释,认为根据由前提类别已知的特征集合构成的特征样本中迁移出现在结论类别中的特征的比例,能预测作为新特征的归纳特征由前提类别迁移到结论类别的可能性。以大学生为被试的实验结果支持对非对称性现象的特征迁移解释而不是原来的两种相似性解释。     

Pandora: A Reasoning Toolbox using Natural Deduction Style

Pandora is a tool for supporting the learning of first ordernatural deduction. It includes a help window, an interactivecontext sensitive tutorial known as the "e-tutor" and facilitiesto save, reload and export to LATEX. Every attempt to applya natural deduction rule is met with either success or a helpfulerror message, providing the student with instant feedback.Detailed electronic logs of student usage are recorded for evaluationpurposes. This paper describes the basic functionality, thee-tutor, our experiences of using the tool in teaching and ourfuture plans.     

Open answer set programming for the semantic web

We extend answer set programming (ASP) with, possibly infinite, open domains. Since this leads to undecidable reasoning, we restrict the syntax of programs, while carefully guarding knowledge representation mechanisms such as negation as failure and inequalities. Reasoning with the resulting extended forest logic programs (EFoLPs) can be reduced to finite answer set programming, for which reasoners are available.We argue that extended forest logic programming is a useful tool for uniformly representing and reasoning with both ontological and rule-based knowledge, as they can capture a large fragment of the OWL DL ontology language equipped with DL-safe rules. Furthermore, EFoLPs enable nonmonotonic reasoning, a desirable feature in locally closed subareas of the Semantic Web.     

Termination of logic programs with imperfect information: applications and query procedure

A general logic programming framework allowing for the combination of several adjoint lattices of truth-values is presented. The language is sorted, enabling the combination of several reasoning forms in the same knowledge base. The contribution of the paper is two-fold: on the one hand, sufficient conditions guaranteeing termination of all queries for the fix-point semantics for a wide class of sorted multi-adjoint logic programs are presented and related to some well-known probability-based formalisms; in addition, we specify a general non-deterministic tabulation goal-oriented query procedure for sorted multi-adjoint logic programs over complete lattices. We prove its soundness and completeness as well as independence of the selection ordering. We apply the termination results to probabilistic and fuzzy logic programming languages, enabling the use of the tabulation proof procedure for query answering.     

Foundations of generativity: Personal and family correlates of emerging adults’ generative life-story themes

Recently, the Eriksonian midlife personality strength of generativity has been described as existing in many forms and in persons of differing ages (McAdams, 2001). In this longitudinal study, narratives of life “turning points” and significant accomplishments were examined for generative themes in 32 adolescent/emerging adults (aged 16 and 20 years). We also explored these emerging adults’ volunteering behavior, prosocial reasoning, and parental influence as potential factors in generativity. Several parenting factors when adolescents were 16 (parents’ autonomy-encouraging practices, their emphasis on caring in stories of family value teaching, and adolescents’ reports of authoritative parenting style) were associated with more generative themes in narratives at age 20. Adolescents’ levels of prosocial reasoning, volunteering behavior, and personal value choices were also associated as expected with generative theme usage at age 20 in life stories, supporting the meaningfulness of this construct in emerging adulthood.     

Belief-desire reasoning in the explanation of behavior: do actions speak louder than words?

The mechanisms underwriting our commonsense psychology, or 'theory of mind', have been extensively investigated via reasoning tasks that require participants to predict the action of agents based on information about beliefs and desires. However, relatively few studies have investigated the processes contributing to a central component of 'theory of mind' - our ability to explain the action of agents in terms of underlying beliefs and desires. In two studies, we demonstrate a novel phenomenon in adult belief-desire reasoning, capturing the folk notion that 'actions speak louder than words'. When story characters were described as searching in the wrong place for a target object, adult subjects often endorsed mental state explanations referencing a distracter object, but only when that object was approached. We discuss how this phenomenon, alongside other reasoning "errors" (e.g., hindsight bias; the curse of knowledge) can be used to illuminate the architecture of domain specific belief-desire reasoning processes.     

Causal relations drive young children's induction, naming, and categorization

A number of recent models and experiments have suggested that evidence of early category-based induction is an artifact of perceptual cues provided by experimenters. We tested these accounts against the prediction that different relations (causal versus non-causal) determine the types of perceptual similarity by which children generalize. Young children were asked to label, to infer novel properties, and to project future appearances of a novel animal that varied in two opposite respects: (1) how much it looked like another animal whose name and properties were known, and (2) how much its parents looked like parents of another animal whose name and properties were known. When exemplar origins were known, children generalized to exemplars with similar origins rather than with similar appearances; when origins were unknown, children generalized to exemplars with similar appearances. Results indicate even young children possess the cognitive control to choose the similarities that best predict accurate generalizations.