This paper formally compares some central notions from two well-known formalisms for rule-based argumentation, DeLP and ASPIC+. The comparisons especially focus on intuitive adequacy and inter-translatability, consistency, and closure properties. As for differences in the definitions of arguments and attack, it turns out that DeLP's definitions are intuitively appealing but that they may not fully comply with Caminada and Amgoud's rationality postulates of strict closure and indirect consistency. For some special cases, the DeLP definitions are shown to fare better than ASPIC+. Next, it is argued that there are reasons to consider a variant of DeLP with grounded semantics, since in some examples its current notion of warrant arguably has counterintuitive consequences and may lead to sets of warranted arguments that are not admissible. Finally, under some minimality and consistency assumptions on ASPIC+ arguments, a one-to-many correspondence between ASPIC+ arguments and DeLP arguments is identified in such a way that if the DeLP warranting procedure is changed to grounded semantics, then DeLP notion of warrant and ASPIC+'s notion of justification are equivalent. This result is proven for three alternative definitions of attack.

AI has achieved remarkable mastery over games such as Chess, Go, and Poker, and even Jeopardy, but the rich variety of standardized exams has remained a landmark challenge. Even in 2016, the best AI system achieved merely 59.3% on an 8th Grade science exam challenge. This paper reports unprecedented success on the Grade 8 New York Regents Science Exam, where for the first time a system scores more than 90% on the exam's non-diagram, multiple choice (NDMC) questions. In addition, our Aristo system, building upon the success of recent language models, exceeded 83% on the corresponding Grade 12 Science Exam NDMC questions. The results, on unseen test questions, are robust across different test years and different variations of this kind of test. They demonstrate that modern NLP methods can result in mastery on this task. While not a full solution to general question-answering (the questions are multiple choice, and the domain is restricted to 8th Grade science), it represents a significant milestone for the field.

Aiming at developing a medical expert system for low back pain management, the paper proposes an efficient knowledge representation scheme using frame data structures, and also derives a reliable resolution logic through Bayesian Network. When a patient comes to the intended expert system for diagnosis, the proposed inference engine outputs a number of probable diseases in sorted order, with each disease being associated with a numeric measure to indicate its possibility of occurrence. When two or more diseases in the list have the same or closer possibility of occurrence, Bayesian Network is used for conflict resolution. The proposed scheme has been validated with cases of empirically selected thirty patients. Considering the expected value 0.75 as level of acceptance, the proposed system offers the diagnostic inference with the standard deviation of 0.029. The computational value of Chi-Squared test has been obtained as 11.08 with 12 degree of freedom, implying that the derived results from the designed system conform the homogeneity with the expected outcomes. Prior to any clinical investigations on the selected low back pain patients, the accuracy level (average) of 73.89% has been achieved by the proposed system, which is quite close to the expected clinical accuracy level of 75%.

Low Back Pain (LBP) is a common medical condition that deprives many individuals worldwide of their normal routine activities. In the absence of external biomarkers, diagnosis of LBP is quite challenging. It requires dealing with several clinical variables, which have no precisely quantified values. Aiming at the development of a fuzzy medical expert system for LBP management, this research proposes an attractive lattice-based knowledge representation scheme for handling imprecision in knowledge, offering a suitable design methodology for a fuzzy knowledge base and a fuzzy inference system. The fuzzy knowledge base is constructed in modular fashion, with each module capturing interrelated medical knowledge about the relevant clinical history, clinical examinations and laboratory investigation results. This approach in design ensures optimality, consistency and preciseness in the knowledge base and scalability. The fuzzy inference system, which uses the Mamdani method, adopts the triangular membership function for fuzzification and the Centroid of Area technique for defuzzification. A prototype of this system has been built using the knowledge extracted from the domain expert physicians. The inference of the system against a few available patient records at the ESI Hospital, Sealdah has been checked. It was found to be acceptable by the verifying medical experts.

Both datasets are being shared by Google AI researchers to supply the training material necessary to model natural language systems that achieve human-level performance. Google researchers call CCPE a new way to collect voice data. It includes 500 dialogues with people about their movie preferences -- 10,000 in total, across 12,000 utterances. Movie preferences were chosen as a topic because of the value of metadata such as the names of actors and directors. "We do not restrict the workers to detailed scripts or to a small knowledge base and hence we observe that our dataset contains more realistic and diverse conversations in comparison to existing datasets," a paper published covering CCPE reads.

A key step in medical diagnosis is giving the patient a universally recognized label (e.g. Appendicitis) which essentially assigns the patient to a class(es) of patients with similar body failures. However, two patients having the same disease label(s) with high probability may still have differences in their feature manifestation patterns implying differences in the required treatments. Additionally, in many cases, the labels of the primary diagnoses leave some findings unexplained. Medical diagnosis is only partially about probability calculations for label X or Y. Diagnosis is not complete until the patient overall situation is clinically understood to the level that enables the best therapeutic decisions. Most machine learning models are data centric models, and evidence so far suggest they can reach expert level performance in the disease labeling phase. Nonetheless, like any other mathematical technique, they have their limitations and applicability scope. Primarily, data centric algorithms are knowledge blind and lack anatomy and physiology knowledge that physicians leverage to achieve complete diagnosis. This article advocates to complement them with intelligence to overcome their inherent limitations as knowledge blind algorithms. Machines can learn many things from data, but data is not the only source that machines can learn from. Historic patient data only tells us what the possible manifestations of a certain body failure are. Anatomy and physiology knowledge tell us how the body works and fails. Both are needed for complete diagnosis. The proposed Double Deep Learning approach, along with the initiative for Medical Wikipedia for Smart Machines, leads to AI diagnostic support solutions for complete diagnosis beyond the limited data only labeling solutions we see today. AI for medicine will forever be limited until their intelligence also integrates anatomy and physiology.

Artificial Intelligence is machine intelligence or ability to think and process information like natural human intelligence in order to create expert systems with human intelligence (reasoning, learning, and problem solving) with help from science and technology disciplines such as Mathematics, Engineering, Biology, Computer Science, Linguistics and Psychology. The term intelligence, literally, means the ability to acquire and apply knowledge and skills. The term Artificial Intelligence ( Artificial Intelligence) is pretty self-explanatory. It is the ability to acquire and apply knowledge and skills artificially. In 1956, a group of researchers from different disciplines of technology gathered for the summit called Dartmouth Summer Research Project.

ABSTRACT: This paper investigates the detection and diagnosis of brush seizing faults in the spindle positioning servo drive of a high-precision machining centre using a recently developed time–frequency pattern classification technique known as selective regional correlation (SRC). It is shown that SRC is capable of significantly enhancing the resolution of fault diagnosis when compared to conventional correlation-based techniques. The performance of this approach is evaluated using three time–frequency transformation techniques: the short-time Fourier transform (STFT), continuous wavelet transform (CWT) and S-Transform. In addition, three different 2D windows are used to isolate features for use with SRC: a rectangular (boxcar) window, a Gaussian window and a Kaiser window. The results have indicated that SRC is a promising tool for machine condition monitoring (MCM).

In coming years, the most intelligent organizations will need to blend technology-enabled insights with a sophisticated understanding of human judgment, reasoning, and choice. Those that do this successfully will have an advantage over their rivals. To succeed in the long run, businesses need to create and leverage some kind of sustainable competitive edge. This advantage can still derive from such traditional sources as scale-driven lower cost, proprietary intellectual property, highly motivated employees, or farsighted strategic leaders. But in the knowledge economy, strategic advantages will increasingly depend on a shared capacity to make superior judgments and choices. Intelligent enterprises today are being shaped by two distinct forces. The first is the growing power of computers and big data, which provide the foundation for operations research, forecasting models, and artificial intelligence (AI). The second is our growing understanding of human judgment, reasoning, and choice.

Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking models for the whole questions, which are likely to fail in generating queries for complex, long-tail questions. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. Our experiments on two benchmark datasets show that our approach significantly outperforms the existing ones, especially for complex questions. Also, it achieves promising performance with limited training data and noisy entity/relation linking results. 1 Introduction Knowledge-based question answering (KBQA) aims to answer natural language questions over knowledge bases (KBs) such as DBpedia and Freebase. Formal query generation is an important component in many KBQA systems (Bao et al., 2016; Cui et al., 2017; Luo et al., 2018), especially for answering complex questions. Given entity and relation linking results, formal query generation aims to generate correct executable queries, e.g., SP ARQL queries, for the input natural language questions. An example question and its formal query are shown in Figure 1.