Journal of Applied Logic最新文献

The article proposes a way to add marketing into the standard threshold model of social networks. Within this framework, the article studies logical properties of the influence relation between sets of agents in social networks. Two different forms of this relation are considered: one for promotional marketing and the other for preventive marketing. In each case a sound and complete logical system describing properties of the influence relation is proposed. Both systems could be viewed as extensions of Armstrong's axioms of functional dependency from the database theory.

The article proposes a logical framework for reasoning about agents' ability to protect their privacy by hiding certain information from a privacy intruder. It is assumed that the knowledge of the intruder is derived from the observation of pieces of evidence and that there is a cost associated with the elimination of the evidence. The logical framework contains a modal operator labeled by a group of agents and a total budget available to this group. The key contribution of this work is the proposed incorporation of the cost factor into privacy protection reasoning within the standard modal logic framework. The main technical result are the soundness and completeness theorems for the introduced logical system with respect to a formally defined semantics.

The expert system shell MECore provides a series of knowledge management operations to define probabilistic knowledge bases and to reason under uncertainty. To provide a reference work for MECore algorithmics, we bring together results from different sources that have been applied in MECore and explain their intuitive ideas. Additionally, we report on our ongoing work regarding further development of MECore's algorithms to compute optimum entropy distributions and provide some empirical results. Altogether this paper explains the intuition of important theoretical results and their practical implications, compares old and new algorithmic approaches and points out their benefits as well as possible limitations and pitfalls.

The aim of this paper is to explore the applicability of the ‘number of possible states’ argument in inferential problems in multi-expert reasoning. The argument is Bayesian and it is similar in spirit to the one used to derive the maximum entropy inference process. Under certain conditions a particular way of applying it surprisingly suggests that the weighted arithmetic mean should be used in meta-analysis with unexplained heterogeneity.

That one's degrees of belief at any one time obey the axioms of probability theory is widely regarded as a necessary condition for static rationality. Many theorists hold that it is also a sufficient condition, but according to critics this yields too subjective an account of static rationality. However, there are currently no good proposals as to how to obtain a tenable stronger probabilistic theory of static rationality. In particular, the idea that one might achieve the desired strengthening by adding some symmetry principle to the probability axioms has appeared hard to maintain. Starting from an idea of Carnap and drawing on relatively recent work in cognitive science, this paper argues that conceptual spaces provide the tools to devise an objective probabilistic account of static rationality. Specifically, we propose a principle that derives prior degrees of belief from the geometrical structure of concepts.

This paper addresses the degree to which The Port Royal Logic anticipates Boolean Algebra. According to Marc Dominicy the best reconstruction is a Boolean Algebra of Carnapian properties, functions from possible worlds to extensions. Sylvain Auroux's reconstruction approximates a non-complemented bounded lattice. This paper argues that it is anachronistic to read lattice algebra into the Port Royal Logic. It is true that the Logic treats extensions like sets, orders ideas under a containment relation, and posits mental operations of abstraction and restriction. It also orders species in a version of the tree of Porphyry, and allows that genera may be divided into species by privative negation. There is, however, no maximal or minimal idea. Abstraction is not binary. Neither abstraction nor restriction is closed. Ideas under containment, therefore, do not form a lattice. Nor are the relevant formal properties of lattices discussed. Term negation is privative, not a complementation operation. The technical ideas relevant to the discussion are defined. The Logic's purpose in describing structure was not to develop algebra in the modern sense but rather to provide a new basis for the semantics of mental language consistent with Cartesian metaphysics. The account was not algebraic, but metaphysical and psychological, based on the concept of comprehension, a Cartesian version of medieval objective being.

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.

Adaptive knowledge modeling is an approach for extending the abilities of the Object-Oriented World Model, a system for representing the state of an observed real-world environment, to open-world modeling. In open environments, entities unforeseen at the design-time of a world model can occur. For coping with such circumstances, adaptive knowledge modeling is tasked with adapting the underlying knowledge model according to the environment. The approach is based on quantitative measures, introduced previously, for rating the quality of knowledge models. In this contribution, adaptive knowledge modeling is extended by measures for detecting the need for model adaptation and identifying the potential starting points of necessary model change as well as by an approach for applying such change. Being an extended and more detailed version of [17], the contribution also provides background information on the architecture of the Object-Oriented World Model and on the principles of adaptive knowledge modeling, as well as examination results for the proposed methods. In addition, a more complex scenario is used to evaluate the overall approach.

Starting off from the usual language of modal logic for multi-agent systems dealing with the agents' knowledge/belief and common knowledge/belief we define so-called epistemic Kripke structures for intuitionistic (common) knowledge/belief. Then we introduce corresponding deductive systems and show that they are sound and complete with respect to these semantics.