Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox

After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of logical probability as the confirmation measure. So far, many confirmation measures have been proposed. Measure F proposed by Kemeny and Oppenheim among them possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al., and normalizing property suggested by many researchers. Based on the semantic information theory, a measure b* similar to F is derived from the medical test. Like the likelihood ratio, measures b* and F can only indicate the quality of channels or the testing means instead of the quality of probability predictions. Furthermore, it is still not easy to use b*, F, or another measure to clarify the Raven Paradox. For this reason, measure c* similar to the correct rate is derived. Measure c* supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the Raven Paradox. An example indicates that measures F and b* are helpful for diagnosing the infection of Novel Coronavirus, whereas most popular confirmation measures are not. Another example reveals that all popular confirmation measures cannot be used to explain that a black raven can confirm “Ravens are black” more strongly than a piece of chalk. Measures F, b*, and c* indicate that the existence of fewer counterexamples is more important than more positive examples’ existence, and hence, are compatible with Popper’s falsification thought

A Tool for Automatic Creation of Rule-Based Expert Systems with CFs

International audienceThis paper introduces a tool, namely ACRES (Automatic CReator of Expert Systems), which can automatically produce rule-based expert systems as CLIPS scripts from a dataset containing knowledge about a problem domain in the form of a large number of cases. The rules are created via a simple systematic approach and make use of certainty factors (CFs). CFs of same conclusions can be combined either using the MYCIN method or a generalization of MYCIN's method. This latter method requires calculation of some weights, based on a training dataset, via the use of a genetic algorithm. Creation of an expert system is outlined. Small scale experimental results comparing the above methods with each other and a neural network are finally presented

Expert System Early Diagnosis Of Schizophrenia Using Certainty Factor Methods And Forward Chaining

Mentall Illness atau gangguan jiwa merupakan gangguan dalam diri seseorang yang mengakibatkan seseorang terganggu dalam proses berfikir, tindakan, hubungan sosial, serta emosi. Skizofrenia merupakan gangguan jiwa berat yang menimbulkan halusinasi, karena tidak normalnya proses berfikir, serta tidak dapat membedakan dunia nyata dan khayalan. Menurut riskesdas 2018, di Indonesia perbandingan antara penderita skizofrenia dan psikolog sangat signifikan perbedaannya, dimana penderita skizofrenia mencapai 7% populasi masyarakat Indonesia atau sekitar 18 juta kasus dibandingkan jumlah psikolog klinis yang hanya 782 orang. Angka pasung pada penderita skizofrenia mencapai 5 juta jiwa, pemasungan terjadi karena kurangnya pemahaman masyarakat mengenai penyakit skizofrenia. Oleh karena itu, sangatlah dibutuhkan sistem pakar yang mampu menggantikan peran psikolog dalam hal membantu masyarakat dalam mengetahui gejala dan penanganan masalah ini. Tipe penyakit skizofrenia dalam penelitian ini yaitu Skizofrenia Paranoid, Katatonik, Residual, Hebefrenik dan Tak Terinci. Tujuan penelitian ini yaitu menghasilkan sistem pakar untuk mendiagnosa dini tipe penyakit skizofrenia serta memberikan rekomendasi solusi pada user. Sistem pakar yang akan dibangun menggunakan metode Certainty Factor dengan penerapan Forward Chaining dalam pengambilan kesimpulan hasil diagnosa, basis pengetahuan dalam sistem ini dapat diupdate sesuai dengan perkembangan pengetahuan. Output sistem ini merupakan diagnosa dini tipe penyakit skizofrenia dan rekomendasi penanganan. Sistem diuji dengan blackbox test, UAT (User Acceptance Test), dan tes akurasi dengan hasil 87%

New Axioms for Probability and Likelihood Ratio Measures

ABSTRACT Probability ratio and likelihood ratio measures of inductive support and related notions have appeared as theoretical tools for probabilistic approaches in the philosophy of science, the psychology of reasoning, and artificial intelligence. In an effort of conceptual clarification, several authors have pursued axiomatic foundations for these two families of measures. Such results have been criticized, however, as relying on unduly demanding or poorly motivated mathematical assumptions. We provide two novel theorems showing that probability ratio and likelihood ratio measures can be axiomatized in a way that overcomes these difficulties

User data discovery and aggregation: the CS-UDD algorithm

In the social web, people use social systems for sharing content and opinions, for communicating with friends, for tagging, etc. People usually have different accounts and different profiles on all of these systems. Several tools for user data aggregation and people search have been developed and protocols and standards for data portability have been defined. This paper presents an approach and an algorithm, named Cross-System User Data Discovery (CS-UDD), to retrieve and aggregate user data distributed on social websites. It is designed to crawl websites, retrieve profiles that may belong to the searched user, correlate them, aggregate the discovered data and return them to the searcher which may, for example, be an adaptive system. The user attributes retrieved, namely attribute-value pairs, are associated with a certainty factor that expresses the confidence that they are true for the searched user. To test the algorithm, we ran it on two popular social networks, MySpace and Flickr. The evaluation has demonstrated the ability of the CS-UDD algorithm to discover unknown user attributes and has revealed high precision of the discovered attributes