Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

Knowledge-Based Modeling of Material Behavior with Neural Networks

Abstract: To date, material modeling has involved the development of mathematical models of material behavior derived from human observation of, and reasoning with, experimental data. An alternative, discussed in this paper, is to use a computation and knowledge representation paradigm, called neural networks, developed by researchers in connectionism (a subfield of artificial intelligence) to model material behavior. The main benefits in using a neural‐network approach are that all behavior can be represented within a unified environment of a neural network and that the network is built directly from experimental data using the self‐organizing capabilities of the neural network, i.e., the network is presented with the experimental data and “learns” the relationships between stresses and strains. Such a modeling strategy has important implications for modeling the behavior of modern, complex materials, such as composites. In this paper, the behaviors of concrete in the state of plane stress under monotonic biaxial ...

Knowledge representation using fuzzy Petri nets

Abstract: A fuzzy Petri net model (FPN) is presented to represent the fuzzy production rule of a rule-based system in which a fuzzy production rule describes the fuzzy relation between two propositions. Based on the fuzzy Petri net model, an efficient algorithm is proposed to perform fuzzy reasoning automatically. It can determine whether an antecedent-consequence relationship exists from proposition d/sub s/ to proposition d/sub j/, where d/sub s/ not=d/sub j/. If the degree of truth of proposition d/sub s/ is given, then the degrees of truth of proposition d/sub j/ can be evaluated. The formal description of the model and the fuzzy reasoning algorithm are shown in detail. The upper bound of the time complexity of the fuzzy reasoning algorithm is O(nm), where n is the number of places and m is the number of transitions. Its execution time is proportional to the number of nodes in a sprouting tree generated by the algorithm only generates necessary reasoning paths from a starting place to a goal place, it can be executed very efficiently. >

Knowledge representation in fuzzy logic

Abstract: The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control. >

Machine invention of first order predicates by inverting resolution

Abstract: It has often been noted that the performance of existing learning systems is strongly biased by the vocabulary provided in the problem description language. An ideal system should be capable of overcoming this restriction by defining its own vocabulary. Such a system would be less reliant on the teacher's ingenuity in supplying an appropriate problem representation. For this purpose we present a mechanism for automatically inventing and generalising first-order Horn clause predicates. The method is based on inverting the mechanism of resolution. The approach has its roots in the Duce system for induction of propositional Horn clauses. We have implemented the new mechanism in a system called CIGOL. CIGOL uses incremental induction to augment incomplete clausal theories. A single, uniform knowledge representation allows existing clauses to be used as background knowledge in the construction of new predicates. Given examples of a high-level predicate CIGOL generates related sub-concepts which it then asks its human teacher to name. Generalisations of predicates are tested by asking questions of the human teacher. CIGOL generates new concepts and generalisations with a preference for simplicity. We illustrate the operation of CIGOL by way of various sessions in which auxiliary predicates are automatically introduced and generalised.