Advances in artificial intelligence

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The content covers a range of topics including invited talks on hierarchy in fluid construction grammars and description logics in ontology applications. It discusses knowledge representation and reasoning, introducing a new n-ary existential quantifier in description logics, and explores subsumption related to hybrid TBoxes. The annotation also addresses dependency calculus for reasoning in general point relation algebra and the temporalization of spatial calculi through generalized neighborhood graphs. In machine learning, it highlights the design of geologic structure models using case-based reasoning, the application of constrained linear regression models for interval-valued data prediction, and the use of stochastic learning weak estimators for training patterns with non-stationary distributions. Noise robustness through inverse mutations is also mentioned. The diagnosis section presents the development of adaptable fault detection algorithms for induction motors, computing optimal action sequences via niche genetic algorithms, and automatic abstraction of time-varying system models for model-based diagnosis. In neural networks, it includes neuro-fuzzy Kolmogorov’s networks for time series prediction and optimal power distribution network design using multiobjective genetic algorithms. Planning topics cover metaheuristics for late work minimization, optimal disassembly scheduling algorithms, and controlled reachabi