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По вашему запросу найдено документов: 11444

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Unification and Inference Rules in the Multi-modal Logic of Knowledge and Linear Time LTK

Bashmakov, Stepan I., Башмаков, Степан И.

formulas which are non-unificable in this logic. Passive inference rules are considered, it is shown

Non-unifiability in linear temporal logic of knowledge with multi-agent relations

Башмаков, С. И., Кошелева, А. В., Рыбаков, В.

to be not unifiable in LFPK. The second one is a construction of a basis for all inference rules passive in LFPK.

Unification and Inference Rules in the Multi-modal Logic of Knowledge and Linear Time LTK

Башмаков, С. И.

Unification and Inference Rules in the Multi-modal Logic of Knowledge and Linear Time LTK

Fuzzy logic inference ruleset augmentation with sample data in medical decision-making systems

Kurochkin, A., Sadov, V.

Fuzzy inference systems are widely used in order to implement complex rule-based decision

Admissible Inference Rules of Temporal Intransitive Logic with the Operator "tomorrow"

Rimatsky, Vitaliy V., Ganchukova, Olga V., Римацкий, Виталий В., Ганчукова, Ольга В.

approximability), admissible rules of this logic are investigated. The main result consists in proving

Fuzzy logic application approach in control of automatic spacecraft

Drozdov A.N., Drozdov N.A., Narozhnyy I.M.

vantages of neural networks and fuzzy inference systems. On the one hand, they allow developing and presenting system

Миварные технологии создания логического искусственного интеллекта: логическая обработка с линейной вычислительной сложностью более 3 миллионов продукций и возможность понимания смысла через моделирование большого контекста

Варламов, О. О., Varlamov, O. O.

: activeness, globality, learning capatibility, ability to make logical inferences and give conclusions in real

Analysis of Semantic Probabilistic Inference Control Method in Multiagent Foraging Task

Vorobiev, V., Rovbo, M.

on rule inference and logical descriptions. One of these methods is based on a semantic probabilistic

A neural network-like combinatorial data structure for symbolic machine learning algorithms

Naidenova, Xenia, Parkhomenko, Vladimir

learning algorithms is advanced. This structure can drastically increase the efficiency of inferring

Method for neuro-fuzzy inference system learning for ICE tests

Method for neuro-fuzzy inference system learning for ICE tests

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