The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques

The paper describes VEX-93 as a hybrid environment for developing knowledge-based and problem solver systems. It integrates methods and techniques from artificial intelligence, image and signal processing and data analysis, which can be mixed. Two hierarchical levels of reasoning contains an intelligent toolbox with one upper strategic inference engine and four lower ones containing specific reasoning models: truth-functional (rule-based), probabilistic (causal networks), fuzzy (rule-based) and case-based (frames). There are image/signal processing-analysis capabilities in the form of programming languages with more than one hundred primitive functions. User-made programs are embeddable within knowledge basis, allowing the combination of perception and reasoning. The data analyzer toolbox contains a collection of numerical classification, pattern recognition and ordination methods, with neural network tools and a data base query language at inference engines's disposal. VEX-93 is an open system able to communicate with external computer programs relevant to a particular application. Metaknowledge can be used for elaborate conclusions, and man-machine interaction includes, besides windows and graphical interfaces, acceptance of voice commands and production of speech output. The system was conceived for real-world applications in general domains, but an example of a concrete medical diagnostic support system at present under completion as a cuban-spanish project is mentioned. Present version of VEX-93 is a huge system composed by about one and half millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version

Mathematical Knowledge, the Analytic Method, and Naturalism

This chapter tries to answer the following question: How should we conceive of the method of mathematics, if we take a naturalist stance? The problem arises since mathematical knowledge is regarded as the paradigm of certain knowledge, because mathematics is based on the axiomatic method. Moreover, natural science is deeply mathematized, and science is crucial for any naturalist perspective. But mathematics seems to provide a counterexample both to methodological and ontological naturalism. To face this problem, some authors tried to naturalize mathematics by relying on evolutionism. But several difficulties arise when we try to do this. This chapter suggests that, in order to naturalize mathematics, it is better to take the method of mathematics to be the analytic method, rather than the axiomatic method, and thus conceive of mathematical knowledge as plausible knowledge

Discovering qualitative empirical laws

In this paper we describe GLAUBER, an AI system that models the scientific discovery of qualitative empirical laws. We have tested the system on data from the history of early chemistry, and it has rediscovered such concepts as acids, alkalis, and salts, as well as laws relating these concepts. After discussing GLAUBER we examine the program's relation to other discovery systems, particularly methods for conceptual clustering and language acquisition

Ruminations: Sundry Notes and Essays on Logic

Ruminations is a collection of sundry notes and essays on Logic. These complement and enrich the author’s past writings, further analyzing or reviewing certain issues. Among the many topics covered are: the importance of the laws of thought, and how they are applied using the logic of paradox; details of formal logic, including some important new insights on the nesting, merger and splitting up of hypothetical propositions; details of causal logic, including analogical reasoning from cause to cause; a cutting-edge phenomenological analysis of negation. Additionally, this volume is used to publish a number of notes and essays previously only posted in his Internet site, including a history of Jewish logic and an analysis of Islamic logic

Unconvinced, Unreconstructed, and Unrepentant: A Reply to Professor Friedman’s Response

Software defined networks are poised to dramatically simplify deployment and management of networks. OpenFlow, in particular, is becoming popular and starts being deployed. While the definition of the “northbound” API that can be used by the new services to interact with an OpenFlow controller is receiving considerable attention, the traditional, “southbound”, API that is used to program OpenFlow switches is far from perfect. In this paper, we analyze the current OpenFlow API and its usage in several controllers and show semantic differences between the intended and actual use. Thus, we argue for making the OpenFlow API clean and simple. In particular, we propose to mimic the process that exists in the Python community for deriving changes that result in a preferably only one, obvious way of performing a task. Toward this end, we propose three OpenFlow Enhancement Proposals: i) providing positive acknowledgment, ii) informing the controller about “silent” modifications, and iii) providing a partial order synchronization primitive.QC 20150704</p

Truth and Probability

Contains two other essays as well: Further Considerations & Last Papers: Probability and Partial Belief.