In studies of scientific methodology, surprisingly little attention has been given to tests of hypotheses. Such testing constitutes a methodology common to various scientific disciplines and is an essential factor in the development of science since it determines which theories are retained. The classical theory of tests is a major accomplishment but requires modification in order to produce a theory that accounts for the success of science. The revised theory is an analysis of the nondeductive aspect of scientific reasoning. It (...) results in a new definition of probability and a nonclassical point of view with regard to the foundations of probability. (shrink)

In studies of scientific methodology, surprisingly little attention has been given to tests of hypotheses. Such testing constitutes a methodology common to various scientific disciplines and is an essential factor in the development of science since it determines which theories are retained. The classical theory of tests is a major accomplishment but requires modification in order to produce a theory that accounts for the success of science. The revised theory is an analysis of the nondeductive aspect of scientific reasoning. It (...) results in a new definition of probability and a nonclassical point of view with regard to the foundations of probability. (shrink)

In studies of scientific methodology, surprisingly little attention has been given to tests of hypotheses. Such testing constitutes a methodology common to various scientific disciplines and is an essential factor in the development of science since it determines which theories are retained. The classical theory of tests is a major accomplishment but requires modification in order to produce a theory that accounts for the success of science. The revised theory is an analysis of the nondeductive aspect of scientific reasoning. It (...) results in a new definition of probability and a nonclassical point of view with regard to the foundations of probability. (shrink)

Provides an introduction to the philosophy of Alfred North Whitehead in the form of a dialogue between Whitehead and the author.

St. Augustine.--Boethius.--Abelard.--St. Thomas of Aquin.--The origins in Greece.--The neo-scholastic revival.

pt. 1. lecture 1. Meet the beast ; lecture 2. Medieval formulations ; lecture 3. The Reformation, the apocalypse revived ; lecture 4. Prophecy and science I, Francis Bacon ; lecture 5. John Milton and freedom of the press ; lecture 6. New Heaven, new earth, modern democracy ; lecture 7. Andrew Marvell, poet of the Republic ; lecture 9. The universe as matter, the universe as spirit -- pt. 2. lecture 10. The hope of Israel, the origins of toleration (...) ; lecture 11. Anti-Antichrist, the limits of prophecy ; lecture 12. Prophecy and Science II, prophecy and progress ; lecture 13. The apocalypse and the American Revolution ; lecture 14. Antichrist and the post-apocalyptic age, cold war ideology ; lecture 15. Antichrist and the post-apocalyptic age, Martin Luther King, Jr. ; lecture 16. Antichrist and the post-apocalyptic age, belief against politics. (shrink)

The expressive power of logic is believed to be able to model most of the fundamental aspects of information retrieval. However, it is also understood that classical logic is ineffective for handling partiality and uncertainty in IR. Applying non-classical logics such as the AGM belief revision logic and the possibilistic logic to adaptive information retrieval is appealing since they provide a powerful and rigorous framework to model partiality and uncertainty inherent in any IR processes. The maxi-adjustment method, which is an (...) effective computational apparatus of the AGM paradigm, is applied to develop the learning components of the adaptive information agents. Essentially, maxi-adjustment allows the partial representation K of a user's information needs N to be refined gradually based on the user's relevance feedback t. Generally speaking, learning in adaptive information agents is characterised by the AGM belief revision Kt*. On the other hand, possibilistic logic supports a gradated assessment of the uncertainty arising from matching K with the imperfect characterisation d of an information object D. Information matching in adaptive information agents is underpinned by K ƕ d, where ƕ is the possibilistic inference relation. This paper illustrates how maxi-adjustment and possibilistic deduction can be applied to develop the adaptive information agents. Their impact on the agents learning autonomy and explanatory power is also discussed. (shrink)