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What role, if any, does formal logic play in characterizing epistemically rational belief? Traditionally, belief is seen in a binary way - either one believes a proposition, or one doesn't. Given this picture, it is attractive to impose certain deductive constraints on rational belief: that one's beliefs be logically consistent, and that one believe the logical consequences of one's beliefs. A less popular picture sees belief as a graded phenomenon. This picture (explored more bydecision-theorists and philosophers of science thatn by mainstream epistemologists) invites the use of probabilistic coherence to constrain rational belief. But this latter project has often involved defining graded beliefs in terms of preferences, which may seem to change the subject away fromepistemic rationality.Putting Logic in its Place explores the relations between these two ways of seeing beliefs. It argues that the binary conception, although it fits nicely with much of our commonsense thought and talk about belief, cannot in the end support the traditional deductive constraints on rational belief. Binary beliefs that obeyed these constraints could not answer to anything like our intuitive notion of epistemic rationality, and would end up having to be divorced from central aspects of ourcognitive, practical, and emotional lives.But this does not mean that logic plays no role in rationality. Probabilistic coherence should be viewed as using standard logic to constrain rational graded belief. This probabilistic constraint helps explain the appeal of the traditional deductive constraints, and even underlies the force of rationally persuasive deductive arguments. Graded belief cannot be defined in terms of preferences. But probabilistic coherence may be defended without positing definitional connections between beliefsand preferences. Like the traditional deductive constraints, coherence is a logical ideal that humans cannot fully attain. Nevertheless, it furnishes a compelling way of understanding a key dimension of epistemic rationality.
The first comprehensive account of the concept and practices of deduction covering philosophy, history, cognition and mathematical practice.
The idea of a possible world that differs in some way from our "actual" world - a world where, for example, the grass is red or no people exist - can help us analyse and understand a wide range of philosophical concepts, such as counterfactuals, properties, modality, and the notions of possibility and necessity. In Possible Worlds Rod Girle surveys current thinking about possible worlds by Kripke, Lewis, Armstrong, Stalnaker, and others. Beginning with a discussion of "possible for" and "possible that," and imagination and fiction, Girle moves on to analyse Kripke's many logics for possibility and Lewis's counterpart worlds. Epistemic possibility, computation and possible worlds, physically possible worlds, impossible worlds, and real possibility are discussed in separate chapters. How the idea of a possible world can be put to use in different areas of philosophy is examined, as are problems that may arise and the benefits that can be gained.
Lance Rips describes a unified theory of natural deductive reasoning and fashions a working model of deduction, with strong experimental support, that is capable of playing a central role in mental life.
This book is a collection of papers written by outstanding researchers in the newly emerging field of computational semantics. It is aimed at those linguists, computer scientists, and logicians who want to know more about the algorithmic realization of meaning in natural language and about what is happening in this field of research. It includes a general introduction by the editors.
Stringently reviewed papers presented at the October 1992 meeting held in Cambridge, Mass., address such topics as nonmonotonic logic; taxonomic logic; specialized algorithms for temporal, spatial, and numerical reasoning; and knowledge representation issues in planning, diagnosis, and natural langu
Information modelling is the essential part of information systems design. Design methods, specification languages, and tools tend to become application dependent, aiming at integration of methodologies stretching from traditional database design to knowledge bases, and including use of logical languages, and process oriented reactive systems description. The topics of the articles cover a wide variety of problems in the area of information modelling, information systems specification, and knowledge bases, ranging from foundations and theories to systems construction and application studies. The contributions are grouped into the following major categories: - Systems specification and information modelling schemes - User interfaces and multimedia - Knowledge organization database structuring - Formal systems - Knowledge and information - From conceptual modelling to software engineering - Description and organization of concepts and objects - Learning systems and applications This book is the eighth volume in the sub-series 'Information Modelling and Knowledge Bases'. This dates back to 1990 with annual publications now amounting to more than 200 reviewed articles. The current volume is intended for researchers, students and practitioners in the area of information systems.
The use of mathematical logic as a formalism for artificial intelligence was recognized by John McCarthy in 1959 in his paper on Programs with Common Sense. In a series of papers in the 1960's he expanded upon these ideas and continues to do so to this date. It is now 41 years since the idea of using a formal mechanism for AI arose. It is therefore appropriate to consider some of the research, applications and implementations that have resulted from this idea. In early 1995 John McCarthy suggested to me that we have a workshop on Logic-Based Artificial Intelligence (LBAI). In June 1999, the Workshop on Logic-Based Artificial Intelligence was held as a consequence of McCarthy's suggestion. The workshop came about with the support of Ephraim Glinert of the National Science Foundation (IIS-9S2013S), the American Association for Artificial Intelligence who provided support for graduate students to attend, and Joseph JaJa, Director of the University of Maryland Institute for Advanced Computer Studies who provided both manpower and financial support, and the Department of Computer Science. We are grateful for their support. This book consists of refereed papers based on presentations made at the Workshop. Not all of the Workshop participants were able to contribute papers for the book. The common theme of papers at the workshop and in this book is the use of logic as a formalism to solve problems in AI.