科学心理学的观念与人文科学的逻辑奠基

科学心理学观念的普遍兴起,是19世纪中叶错综复杂的思想史背景的产物。只有在人类思想及其历史作为整体的关系背景中,才能澄清这个观念的内在本性。也只有在系统地接受这个背景的制约关系中,这个观念才能获得其真理的实现。能否在人类思想作为整体中胜任并执行其理论职能,成为我们检验一种心理学体系之真理性的途径之一:科学心理学观念只有实现为现象学心理学,才能执行其为人文科学奠基的逻辑职能; 反之亦然。这个关系还从否定方面暗示着,一切以异化的形式实现的心理学,都不可能真正承担起为人文科学奠基的逻辑职能,并因而违背了我们的常识。     

论推类逻辑与中国古代医学

推类逻辑就是基于“推类”的中国古代逻辑。易数推类为中医科学提供了一个有效工具,提供了一个有效地认识自然、机体（肌体）、疾病的方法。取象比类是中医思维的核心,推类是中国古代医学辨证论治的基本方法。推类具有很强的具象性,并以比类为基础。     

An Early Buddhist Text on Logic: Fang Bian Xin Lun

The Fang Bian Xin Lun is a text on Buddhist logic which is thought to be the earliest one still to be extant. It appears in Chinese only (T1632). The great Italian indologist Giuseppe Tucci, believing that the text was originally a Sanskrit text, translated it into Sanskrit and gave it the title Upāyahṛdaya. The paper provides the historical background of the development of logic in Classical India up to the time of this text, summarizes its content and translates its first section.

Philosophy of science in Estonia

This paper presents a survey of the philosophy of science in Estonia. Topics covered include the historical background (science at the 17th century Academia Gustaviana, in the 19th century, during the Soviet period) and an overview of the current situation and main areas of research (the problem of demarcation, a critique of the traditional understandings of science, φ-science, classical and non-classical science, the philosophy of chemistry, the problem of induction, the sociology of scientific knowledge, semiotics as a methodology).     

Logics of public communications

Multi-modal versions of propositional logics S5 or S4—commonly accepted as logics of knowledge—are capable of describing static states of knowledge but they do not reflect how the knowledge changes after communications among agents. In the present paper (part of broader research on logics of knowledge and communications) we define extensions of the logic S5 which can deal with public communications. The logics have natural semantics. We prove some completeness, decidability and interpretability results and formulate a general method that solves certain kind of problems involving public communications—among them well known puzzles of Muddy Children and Mr. Sum & Mr. Product. As the paper gives a formal logical treatment of the operation of restriction of the universe of a Kripke model, it contributes also to investigations of semantics for modal logics. This paper was originally published as Plaza, J. A. (1989). Logics of public communications. In M. L. Emrich, M. S. Pfeifer, M. Hadzikadic, & Z.W. Ras (Eds.), Proceedings of the fourth international symposium on methodologies for intelligent systems: Poster session program (pp. 201–216). Publisher: Oak Ridge National Laboratory, ORNL/DSRD-24. Research partly supported by NSF Grant CCR-8702307 and PSC-CUNY Grant 668283.     

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     

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.     

Abductive reasoning in neural-symbolic systems

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration.